Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works

Densely Connected Convolutional Networks

Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2016) · arXiv · doi:10.48550/arxiv.1608.06993 · Cited by 48,960 · see citing works

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works

A survey on transfer learning

Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works

A decision-theoretic generalization of on-line learning and an application to boosting

Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466 · see citing works

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works

A fast learning algorithm for deep belief nets

Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336 · see citing works

Approximation by superpositions of a sigmoidal function

Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883 · see citing works

Fully convolutional networks for semantic segmentation

Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898 · see citing works

The self-organizing map

Kohonen, T. (1990) · Neurocomputing · doi:10.1016/s0925-2312(98)00030-7 · Cited by 10,686 · see citing works

Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups

Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260 · see citing works

Image Super-Resolution Using Deep Convolutional Networks

Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105 · see citing works

Speech recognition with deep recurrent neural networks

Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893 · see citing works

Learning long-term dependencies with gradient descent is difficult

Bengio, Y., Simard, P., & Frasconi, P. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279181 · Cited by 8,542 · see citing works

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370 · see citing works

ADADELTA: An Adaptive Learning Rate Method

Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322 · see citing works

Deep visual-semantic alignments for generating image descriptions

Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902 · see citing works

Greedy layer-wise training of deep networks

Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841 · see citing works

Learning Deconvolution Network for Semantic Segmentation

Noh, H., Hong, S., & Han, B. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.178 · Cited by 4,422 · see citing works

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196 · see citing works

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019 · see citing works

Multi-column deep neural networks for image classification

Ciresan, D. C., Meier, U., & Schmidhuber, J. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248110 · Cited by 3,760 · see citing works

A survey of image classification methods and techniques for improving classification performance

Lu, D., & Weng, Q. (2007) · International Journal of Remote Sensing · doi:10.1080/01431160600746456 · Cited by 3,424 · see citing works

Return of the Devil in the Details: Delving Deep into Convolutional Nets

Chatfield, K., Simonyan, K., Vedaldi, A., & Zisserman, A. (2014) · Proceedings of the British Machine Vision Conference 2014 · doi:10.5244/c.28.6 · Cited by 3,335 · see citing works

A survey of deep neural network architectures and their applications

Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239 · see citing works

Pedestrian Detection: An Evaluation of the State of the Art

Dollár, P., Wojek, C., Schiele, B., & Perona, P. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.155 · Cited by 3,191 · see citing works

Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition

Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2134090 · Cited by 3,011 · see citing works

Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks

Chen, Y., Jiang, H., Li, C., Jia, X., & Ghamisi, P. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2584107 · Cited by 3,005 · see citing works

Remote Sensing Image Scene Classification: Benchmark and State of the Art

Cheng, G., Han, J., & Lu, X. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2675998 · Cited by 2,926 · see citing works

Some methods of speeding up the convergence of iteration methods

Polyak, B. T. (1964) · USSR Computational Mathematics and Mathematical Physics · doi:10.1016/0041-5553(64)90137-5 · Cited by 2,867 · see citing works

Vertex component analysis: a fast algorithm to unmix hyperspectral data

Nascimento, J. M. P., & Bioucas‐Dias, J. M. (2005) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2005.844293 · Cited by 2,683 · see citing works

Deep learning-based classification of hyperspectral data

Chen, Y., Lin, Z., Zhao, X., Wang, G., & Gu, Y. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2329330 · Cited by 2,679 · see citing works

Deep learning applications and challenges in big data analytics

Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015) · Journal of Big Data · doi:10.1186/s40537-014-0007-7 · Cited by 2,594 · see citing works

Spectral unmixing

Keshava, N., & Mustard, J. F. (2002) · IEEE Signal Processing Magazine · doi:10.1109/79.974727 · Cited by 2,549 · see citing works

Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art

Zhang, L., Zhang, L., & Du, B. (2016) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2016.2540798 · Cited by 2,229 · see citing works

Hyperspectral remote sensing data analysis and future challenges

Bioucas-Dias, J. M., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., & Chanussot, J. (2013) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2013.2244672 · Cited by 2,223 · see citing works

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

Sladojević, S., Arsenović, M., Anderla, A., Culibrk, D. R., & Stefanović, D. (2016) · Computational Intelligence and Neuroscience · doi:10.1155/2016/3289801 · Cited by 2,133 · see citing works

Deep Convolutional Neural Networks for Hyperspectral Image Classification

Hu, W., Huang, Y., Wei, L., Zhang, F., & Li, H.-C. (2015) · Journal of Sensors · doi:10.1155/2015/258619 · Cited by 1,962 · see citing works

DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving

Chen, C., Seff, A., Kornhauser, A., & Xiao, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.312 · Cited by 1,876 · see citing works

MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., & Zhang, Z. (2015) · arXiv · doi:10.48550/arxiv.1512.01274 · Cited by 1,819 · see citing works

Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images

Cheng, G., Zhou, P., & Han, J. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2601622 · Cited by 1,771 · see citing works

Deconvolutional networks

Zeiler, M. D., Krishnan, D., Taylor, G. W., & Fergus, R. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5539957 · Cited by 1,753 · see citing works

Trends in extreme learning machines: A review

Huang, G. M., Huang, G.-B., Song, S., & You, K. (2014) · Neural Networks · doi:10.1016/j.neunet.2014.10.001 · Cited by 1,737 · see citing works

Recent advances in techniques for hyperspectral image processing

Plaza, A., Benediktsson, J. A., Boardman, J. W., Brazile, J., Bruzzone, L., Camps‐Valls, G., Chanussot, J., Fauvel, M., Gamba, P. E., Gualtieri, A. G., et al. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2007.07.028 · Cited by 1,705 · see citing works

Static Analysis of Shape in TensorFlow ProgramsPreprint

The Theano Development Team, Al‐Rfou, R., Alain, G., et al. (2016) · arXiv (Cornell University) · doi:10.4230/lipics.ecoop.2020.15 · Cited by 1,629 · see citing works

PCANet: A Simple Deep Learning Baseline for Image Classification?

Chan, T.-H., Jia, K., Gao, S., Lu, J., Zeng, Z., & Ma, Y. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2475625 · Cited by 1,598 · see citing works

Change detection from remotely sensed images: From pixel-based to object-based approaches

Hussain, M., Chen, D., Cheng, A., Wei, H., & Stanley, D. (2013) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2013.03.006 · Cited by 1,515 · see citing works

Sequence to Sequence -- Video to Text

Venugopalan, S., Rohrbach, M., Donahue, J., Mooney, R., Darrell, T., & Saenko, K. (2015) · IEEE International Conference on Computer Vision · doi:10.1109/iccv.2015.515 · Cited by 1,507 · see citing works

A survey on object detection in optical remote sensing images

Cheng, G., & Han, J. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.03.014 · Cited by 1,503 · see citing works

Kernel-based methods for hyperspectral image classification

Camps‐Valls, G., & Bruzzone, L. (2005) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2005.846154 · Cited by 1,457 · see citing works

Training Very Deep NetworksPreprint

Srivastava, R. K., Greff, K., & Schmidhuber, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.06228 · Cited by 1,453 · see citing works

Advances in Spectral-Spatial Classification of Hyperspectral Images

Fauvel, M., Tarabalka, Y., Benediktsson, J. A., Chanussot, J., & Tilton, J. C. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2197589 · Cited by 1,288 · see citing works

Spectral-spatial classification of hyperspectral data based on deep belief network

Chen, Y., Zhao, X., & Jia, X. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2388577 · Cited by 1,259 · cited 6× · see citing works

Big data deep learning: Challenges and perspectives

Chen, X.-W., & Lin, X. (2014) · IEEE Access · doi:10.1109/access.2014.2325029 · Cited by 1,255 · see citing works

A review of unsupervised feature learning and deep learning for time-series modeling

Längkvist, M., Karlsson, L., & Loutfi, A. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.01.008 · Cited by 1,234 · see citing works

Untitled work

(2011) · doi:10.5591/978-1-57735-516-8/ijcai11-210 · Cited by 1,229 · see citing works

Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images

Sirinukunwattana, K., Raza, S. E. A., Tsang, Y.-W., Snead, D. R. J., Cree, I. A., & Rajpoot, N. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2525803 · Cited by 1,224 · see citing works

A guide to convolution arithmetic for deep learningPreprint

Dumoulin, V., & Visin, F. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.07285 · Cited by 1,223 · see citing works

Image processing with neural networks—a review

Egmont-Petersen, M., de Ridder, D., & Heinz Handels, A. (2002) · Pattern Recognition · doi:10.1016/s0031-3203(01)00178-9 · Cited by 1,185 · see citing works

Searching for exotic particles in high-energy physics with deep learning

Baldi, P., Sadowski, P., & Whiteson, D. (2014) · Nature Communications · doi:10.1038/ncomms5308 · Cited by 1,164 · see citing works

Pansharpening by Convolutional Neural Networks

Masi, G., Cozzolino, D., Verdoliva, L., & Scarpa, G. (2016) · Remote Sensing · doi:10.3390/rs8070594 · Cited by 1,158 · see citing works

Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning

Huang, W., Song, G., Hong, H., & Xie, K. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2311123 · Cited by 1,139 · see citing works

Machine learning in geosciences and remote sensing

Lary, D. J., Alavi, A. H., Gandomi, A. H., & Walker, A. L. (2015) · Geoscience Frontiers · doi:10.1016/j.gsf.2015.07.003 · Cited by 1,136 · see citing works

Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]

Arel, I., Rose, D. C., & Karnowski, T. P. (2010) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2010.938364 · Cited by 1,128 · see citing works

Satellite remote sensing of wetlands

Özesmi, S. L., & Bauer, M. E. (2002) · Wetlands Ecology and Management · doi:10.1023/a:1020908432489 · Cited by 1,108 · see citing works

Oil spill detection by satellite remote sensing

Brekke, C., & Solberg, A. H. S. (2005) · Remote Sensing of Environment · doi:10.1016/j.rse.2004.11.015 · Cited by 1,098 · see citing works

Review of studies on tree species classification from remotely sensed data

Fassnacht, F. E., Latifi, H., Stereńczak, K., Modzelewska, A., Lefsky, M. A., Waser‬, L. T., Straub, C., & Ghosh, A. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.08.013 · Cited by 1,078 · see citing works

Deep supervised learning for hyperspectral data classification through convolutional neural networks

Makantasis, K., Karantzalos, K. G., Doulamis, A. D., & Doulamis, N. D. (2015) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2015.7326945 · Cited by 1,045 · see citing works

Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review

Adam, E. M. I., Mutanga, O., & Rugege, D. (2009) · Wetlands Ecology and Management · doi:10.1007/s11273-009-9169-z · Cited by 1,027 · see citing works

Introduction Neural networks in remote sensing

Atkinson, P. M., & Tatnall, A. R. L. (1997) · International Journal of Remote Sensing · doi:10.1080/014311697218700 · Cited by 1,021 · see citing works

Energy-based Generative Adversarial Network

Zhao, J., Mathieu, M., & LeCun, Y. (2016) · arXiv · doi:10.48550/arxiv.1609.03126 · Cited by 912 · see citing works

Towards better exploiting convolutional neural networks for remote sensing scene classification

Nogueira, K., Penatti, O. A. B., & dos Santos, J. A. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.07.001 · Cited by 909 · see citing works

On optimization methods for deep learning

Ngiam, J., Coates, A., Lahiri, A., Prochnow, B., Le, Q. V., & Ng, A. Y. (2011) · International Conference on Machine Learning · Cited by 868 · see citing works

Hyperspectral Image Classification Using Deep Pixel-Pair Features

Li, W., Wu, G., Zhang, F., & Du, Q. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2616355 · Cited by 807 · see citing works

Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods

Camps‐Valls, G., Tuia, D., Bruzzone, L., & Benediktsson, J. A. (2013) · IEEE Signal Processing Magazine · doi:10.1109/msp.2013.2279179 · Cited by 786 · see citing works

Efficient Deep Learning for Stereo Matching

Luo, W., Schwing, A. G., & Urtasun, R. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.614 · Cited by 764 · see citing works

Marginalized Denoising Autoencoders for Domain AdaptationPreprint

Chen, M., Xu, Z., Weinberger, K. Q., & Sha, F. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.4683 · Cited by 742 · see citing works

Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?

Penatti, O. A. B., Nogueira, K., & dos Santos, J. A. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301382 · Cited by 730 · see citing works

Unsupervised Deep Feature Extraction for Remote Sensing Image Classification

Romero, A., Gatta, C., & Camps‐Valls, G. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2478379 · Cited by 729 · see citing works

Convolutional neural networks for hyperspectral image classification

Yu, S., Jia, S., & Xu, C. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.09.010 · Cited by 709 · see citing works

Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances

Tuia, D., Persello, C., & Bruzzone, L. (2016) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2016.2548504 · Cited by 698 · see citing works

Remote sensing big data computing: Challenges and opportunities

Ma, Y., Wu, H., Wang, L., Huang, B., Ranjan, R. K., Zomaya, A. Y., & Jie, W. (2014) · Future Generation Computer Systems · doi:10.1016/j.future.2014.10.029 · Cited by 681 · see citing works

Endmember variability in Spectral Mixture Analysis: A review

Somers, B., Asner, G. P., Tits, L., & Coppin, P. R. (2011) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.03.003 · Cited by 675 · see citing works

Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks

Marmanis, D., Datcu, M. P., Esch, T., & Stilla, U. (2015) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2015.2499239 · Cited by 652 · see citing works

A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

Dorigo, W. A., Zurita‐Milla, R., de Wit, A. J. W., Brazile, J., Singh, R., & Schaepman, M. E. (2006) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2006.05.003 · Cited by 639 · see citing works

Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks

Gong, M., Zhao, J., Liu, J., Miao, Q., & Jiao, L. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2435783 · Cited by 639 · see citing works

Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks

Chen, X., Xiang, S., Liu, C., & Pan, C. (2014) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2014.2309695 · Cited by 619 · see citing works

Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks

Kampffmeyer, M., Salberg, A.-B., & Jenssen, R. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.90 · Cited by 618 · see citing works

Learning Deconvolution Network for Semantic SegmentationPreprint

Noh, H., Hong, S., & Han, B. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.04366 · Cited by 613 · see citing works

A Quantitative and Comparative Analysis of Endmember Extraction Algorithms From Hyperspectral Data

Plaza, A., Martinez, P., Pérez, R., & Plaza, J. (2004) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2003.820314 · Cited by 612 · see citing works

Big Data for Remote Sensing: Challenges and Opportunities

Chi, M., Plaza, A., Benediktsson, J. A., Sun, Z., Shen, J., & Zhu, Y. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2016.2598228 · Cited by 610 · see citing works

CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos

Shou, Z., Chan, J., Zareian, A., Miyazawa, K., & Chang, S. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.155 · Cited by 580 · see citing works

Plant identification using deep neural networks via optimization of transfer learning parameters

Ghazi, M. M., Yanikoglu, B. A., & Aptoula, E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.01.018 · Cited by 541 · see citing works

Time series forecasting using a deep belief network with restricted Boltzmann machines

Kuremoto, T., Kimura, S., Kobayashi, K., & Obayashi, M. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.03.047 · Cited by 526 · see citing works

A critical synthesis of remotely sensed optical image change detection techniques

Tewkesbury, A., Comber, A., Tate, N., et al. (2015) · Remote Sensing of Environment · doi:10.1016/j.rse.2015.01.006 · Cited by 526 · see citing works

Spectral–spatial classification of hyperspectral images using deep convolutional neural networks

Yue, J., Zhao, W., Mao, S., & Liu, H. (2015) · Remote Sensing Letters · doi:10.1080/2150704x.2015.1047045 · Cited by 525 · see citing works

THE ISPRS BENCHMARK ON URBAN OBJECT CLASSIFICATION AND 3D BUILDING RECONSTRUCTION

Rottensteiner, F., Sohn, G., Jung, J., Gerke, M., Baillard, C., Benitez, S., & Breitkopf, U. (2012) · ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences · doi:10.5194/isprsannals-i-3-293-2012 · Cited by 525 · see citing works

Deep-plant: Plant identification with convolutional neural networks

Lee, S. H., Chan, C. S., Wilkin, P., & Remagnino, P. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7350839 · Cited by 510 · see citing works

Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data

Ali, I., Greifeneder, F., Stamenković, J., Neumann, M., & Notarnicola, C. (2015) · Remote Sensing · doi:10.3390/rs71215841 · Cited by 510 · see citing works

Deep Learning and Its Applications to Signal and Information Processing [Exploratory DSP

Yu, D., & Deng, L. (2010) · IEEE Signal Processing Magazine · doi:10.1109/msp.2010.939038 · Cited by 459 · see citing works

A survey of open source tools for machine learning with big data in the Hadoop ecosystem

Landset, S., Khoshgoftaar, T. M., Richter, A. N., et al. (2015) · Journal Of Big Data · doi:10.1186/s40537-015-0032-1 · Cited by 438 · see citing works

Real-Time Crisis Mapping of Natural Disasters Using Social Media

Middleton, S. E., Middleton, L., & Modafferi, S. (2014) · IEEE Intelligent Systems · doi:10.1109/mis.2013.126 · Cited by 426 · see citing works

Scene Classification via a Gradient Boosting Random Convolutional Network Framework

Zhang, F., Du, B., & Zhang, L. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2488681 · Cited by 426 · see citing works

Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine

Tang, J., Deng, C., Huang, G.-B., & Zhao, B. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2335751 · Cited by 407 · see citing works

The convergence of GIS and social media: challenges for GIScience

Sui, D. Z., & Goodchild, M. F. (2011) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2011.604636 · Cited by 403 · see citing works

A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection

Cruz-Roa, A. A., Arevalo Ovalle, J. E., Madabhushi, A., & González Osorio, F. A. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_50 · Cited by 403 · see citing works

Unsupervised Spectral–Spatial Feature Learning With Stacked Sparse Autoencoder for Hyperspectral Imagery Classification

Tao, C., Pan, H., Li, Y., & Zou, Z. (2015) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2015.2482520 · Cited by 398 · see citing works

Learning multiscale and deep representations for classifying remotely sensed imagery

Zhao, W., & Du, S. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.01.004 · Cited by 398 · see citing works

Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging

Zabalza, J., Ren, J., Zheng, J., Zhao, H., Qing, C., Yang, Z., Du, P., & Marshall, S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.11.044 · Cited by 389 · see citing works

Feature Mining for Hyperspectral Image Classification

Jia, X., Kuo, B., & Crawford, M. M. (2013) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2229082 · Cited by 384 · see citing works

Automatic Object Extraction from Aerial Imagery—A Survey Focusing on Buildings

Mayer, H. (1999) · Computer Vision and Image Understanding · doi:10.1006/cviu.1999.0750 · Cited by 358 · see citing works

Point cloud labeling using 3D Convolutional Neural Network

Huang, J., & You, S. O. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900038 · Cited by 353 · see citing works

A survey of randomized algorithms for training neural networks

Zhang, L., & Suganthan, P. N. (2016) · Information Sciences · doi:10.1016/j.ins.2016.01.039 · Cited by 352 · see citing works

Deep semantic understanding of high resolution remote sensing image

Qu, B., Li, X., Tao, D., & Lu, X. (2016) · 2016 International Conference on Computer, Information and Telecommunication Systems (CITS) · doi:10.1109/cits.2016.7546397 · Cited by 350 · see citing works

Benefits of Depth in Neural Networks

Telgarsky, M. (2016) · arXiv · doi:10.48550/arxiv.1602.04485 · Cited by 341 · see citing works

Transferred Deep Learning for Anomaly Detection in Hyperspectral Imagery

Li, W., Wu, G., & Du, Q. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2657818 · Cited by 338 · see citing works

Benchmarking State-of-the-Art Deep Learning Software Tools

Shi, S., Wang, Q., Xu, P., & Chu, X. (2016) · doi:10.1109/ccbd.2016.029 · Cited by 334 · see citing works

A New Pan-Sharpening Method With Deep Neural Networks

Huang, W., Xiao, L., Wei, Z., Liu, H., & Tang, S. (2015) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2014.2376034 · Cited by 333 · see citing works

Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists

Wang, K., Franklin, S. E., Guo, X., et al. (2010) · Sensors · doi:10.3390/s101109647 · Cited by 320 · see citing works

High-resolution satellite scene classification using a sparse coding based multiple feature combination

Sheng, G., Yang, W. T., Xu, T., & Sun, H. (2011) · International Journal of Remote Sensing · doi:10.1080/01431161.2011.608740 · Cited by 309 · see citing works

A neural network-based estimate of the seasonal to inter-annual variability of the Atlantic Ocean carbon sink

Landschützer, P., Gruber, N., Bakker, D. C. E., Schuster, U., Nakaoka, S., Payne, M., Sasse, T. P., & Zeng, J. (2013) · Biogeosciences · doi:10.5194/bg-10-7793-2013 · Cited by 307 · see citing works

Building detection in very high resolution multispectral data with deep learning features

Vakalopoulou, M., Karantzalos, K. G., Nikos Komodakis, A., & Nikos Paragios, A. (2015) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2015.7326158 · Cited by 305 · see citing works

Effective and Efficient Midlevel Visual Elements-Oriented Land-Use Classification Using VHR Remote Sensing Images

Cheng, G., Han, J., Guo, L., Liu, Z., Bu, S., & Ren, J. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2393857 · Cited by 305 · see citing works

Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network

Zhang, H., Li, Y., Zhang, Y., & Shen, Q. (2017) · Remote Sensing Letters · doi:10.1080/2150704x.2017.1280200 · Cited by 304 · see citing works

Multiple Classifier System for Remote Sensing Image Classification: A Review

Du, P., Xia, J., We, Z., et al. (2012) · Sensors · doi:10.3390/s120404764 · Cited by 300 · see citing works

Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks

Längkvist, M., Kiselev, A., Alirezaie, M., & Loutfi, A. (2016) · Remote Sensing · doi:10.3390/rs8040329 · Cited by 300 · see citing works

Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning

Lunga, D., Prasad, S., Crawford, M. M., et al. (2013) · IEEE Signal Processing Magazine · doi:10.1109/msp.2013.2279894 · Cited by 295 · see citing works

An Active Learning Approach to Hyperspectral Data Classification

Rajan, S., Ghosh, J., & Crawford, M. M. (2008) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2007.910220 · Cited by 292 · see citing works

High-Resolution SAR Image Classification via Deep Convolutional Autoencoders

Geng, J., Fan, J., Wang, H., Ma, X., Li, B., & Chen, F. (2015) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2015.2478256 · Cited by 291 · see citing works

Active Deep Learning for Classification of Hyperspectral Images

Liu, P., Zhang, H., & Eom, K. B. (2016) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2016.2598859 · Cited by 290 · see citing works

A discriminative deep model for pedestrian detection with occlusion handling

Ouyang, W., & Wang, X. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248062 · Cited by 281 · see citing works

Social media as an information source for rapid flood inundation mapping

Fohringer, J., Dransch, D., Kreibich, H., et al. (2015) · Natural hazards and earth system sciences · doi:10.5194/nhess-15-2725-2015 · Cited by 265 · see citing works

Hyperspectral image reconstruction by deep convolutional neural network for classification

Li, Y., Xie, W., & Li, H. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.10.019 · Cited by 264 · see citing works

DeepFish: Accurate underwater live fish recognition with a deep architecture

Qin, H., Li, X., Liang, J., Peng, Y., & Zhang, C. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.10.122 · Cited by 260 · see citing works

Spectral–Spatial Classification of Hyperspectral Image Based on Deep Auto-Encoder

Ma, X., Wang, H., & Geng, J. (2016) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2016.2517204 · Cited by 260 · see citing works

Multiview Deep Learning for Land-Use Classification

Luus, F., Salmon, B. P., van den Bergh, F., & Maharaj, B. T. (2015) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2015.2483680 · Cited by 257 · see citing works

Effective semantic pixel labelling with convolutional networks and Conditional Random Fields

Paisitkriangkrai, S., Sherrah, J., Janney, P., & van den Hengel, A. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301381 · Cited by 240 · see citing works

Computer Vision – ECCV 2014

Fleet, D. J., Tuytelaars, T., Schiele, B., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10602-1 · Cited by 236 · see citing works

Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine

Chen, C., Li, W., Su, H., et al. (2014) · Remote Sensing · doi:10.3390/rs6065795 · Cited by 233 · see citing works

Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn

Goel, P. K., Prasher, S. O., Patel, R. M., et al. (2003) · Computers and Electronics in Agriculture · doi:10.1016/s0168-1699(03)00020-6 · Cited by 230 · see citing works

Incorporating spatial information in spectral unmixing: A review

Chen, S., & Le, W. (2014) · Remote Sensing of Environment · doi:10.1016/j.rse.2014.03.034 · Cited by 230 · see citing works

Deep Convolutional Neural Networks for pedestrian detection

Tomè, D., Monti, F., Baroffio, L., Bondi, L., Tagliasacchi, M., & Tubaro, S. (2016) · Signal Processing Image Communication · doi:10.1016/j.image.2016.05.007 · Cited by 224 · see citing works

SAR target recognition based on deep learning

Chen, S., & Wang, H. (2014) · 2014 International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2014.7058124 · Cited by 224 · see citing works

Deep convolutional neural networks for ATR from SAR imagery

Morgan, D. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2176558 · Cited by 218 · see citing works

Classification of hyperspectral image based on deep belief networks

Li, T., Zhang, J., & Zhang, Y. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7026039 · Cited by 217 · see citing works

Scene Recognition by Manifold Regularized Deep Learning Architecture

Yuan, Y., Mou, L., & Lu, X. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2359471 · Cited by 214 · see citing works

Scalable stacking and learning for building deep architectures

Deng, L., Yu, D., & Platt, J. C. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6288333 · Cited by 213 · see citing works

Estimating crop yields with deep learning and remotely sensed data

Kuwata, K., & Shibasaki, R. (2015) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2015.7325900 · Cited by 211 · see citing works

Spectral clustering for sensing urban land use using Twitter activity

Frías-Martínez, V., & Frı́as-Martı́nez, E. (2014) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2014.06.019 · Cited by 208 · see citing works

3D Convolutional Neural Networks for landing zone detection from LiDAR

Maturana, D., & Scherer, S. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139679 · Cited by 203 · see citing works

Fully convolutional networks for building and road extraction: Preliminary results

Zhong, Z., Li, J., Cui, W., et al. (2016) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2016.7729406 · Cited by 198 · see citing works

Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks

Kuenzer, C., Ottinger, M., Wegmann, M., Guo, H., Wang, C., Zhang, J., Dech, S. W., & Wikelski, M. C. (2014) · International Journal of Remote Sensing · doi:10.1080/01431161.2014.964349 · Cited by 194 · see citing works

Learning Sparse Codes for Hyperspectral Imagery

Charles, A. S., Olshausen, B. A., & Rozell, C. J. (2011) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2011.2149497 · Cited by 193 · see citing works

Deep learning-based tree classification using mobile LiDAR data

Guan, H., Yu, Y., Ji, Z., et al. (2015) · Remote Sensing Letters · doi:10.1080/2150704x.2015.1088668 · Cited by 185 · see citing works

R-VCANet: A New Deep-Learning-Based Hyperspectral Image Classification Method

Pan, B., Shi, Z., & Xu, X. (2017) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2655516 · Cited by 182 · see citing works

Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine

Wang, J., Song, J., Chen, M., & Yang, Z. (2015) · International Journal of Remote Sensing · doi:10.1080/01431161.2015.1054049 · Cited by 178 · see citing works

Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping

Li, W., Fu, H., Yu, L., Gong, P., Feng, D., Li, C., & Clinton, N. (2016) · International Journal of Remote Sensing · doi:10.1080/01431161.2016.1246775 · Cited by 175 · see citing works

Recurrent Attention Models for Depth-Based Person Identification

Haque, A., Alahi, A., & Fei-Fei, L. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.138 · Cited by 172 · see citing works

Highway and Residual Networks learn Unrolled Iterative EstimationPreprint

Greff, K., Srivastava, R. K., & Schmidhuber, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.07771 · Cited by 169 · see citing works

Use of Neural Networks for Automatic Classification From High-Resolution Images

Frate, F. D., Pacifici, F., Schiavon, G., et al. (2007) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2007.892009 · Cited by 164 · see citing works

A Self-Improving Convolution Neural Network for the Classification of Hyperspectral Data

Ghamisi, P., Chen, Y., & Zhu, X. X. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2016.2595108 · Cited by 159 · see citing works

Using convolutional features and a sparse autoencoder for land-use scene classification

Othman, E. M. A., Bazi, Y., Alajlan, N., Alhichri, H. S., & Melgani, F. (2016) · International Journal of Remote Sensing · doi:10.1080/01431161.2016.1171928 · Cited by 154 · see citing works

Geological Disaster Recognition on Optical Remote Sensing Images Using Deep Learning

Liu, Y., & Wu, L. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.07.144 · Cited by 154 · see citing works

Tensor Deep Stacking Networks

Hutchinson, B., Deng, L., & Yu, D. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.268 · Cited by 153 · see citing works

Contextual deep CNN based hyperspectral classification

Lee, H., & Kwon, H. (2016) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2016.7729859 · Cited by 145 · see citing works

Hyperspectral image classification using two-channel deep convolutional neural network

Yang, J., Zhao, Y., Chan, J. C.-W., & Yi, C. (2016) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2016.7730324 · Cited by 145 · see citing works

Why Deep Neural Networks for Function Approximation?Preprint

Liang, S., & Srikant, R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.04161 · Cited by 145 · see citing works

Differential Evolution Extreme Learning Machine for the Classification of Hyperspectral Images

Bazi, Y., Alajlan, N., Melgani, F., Alhichri, H. S., Malek, S., & Yager, R. R. (2013) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2013.2286078 · Cited by 143 · see citing works

StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation

Levi, D., Garnett, N., & Fetaya, E. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.109 · Cited by 143 · see citing works

A deep learning framework for hyperspectral image classification using spatial pyramid pooling

Yue, J., Mao, S., & Mei, L. (2016) · Remote Sensing Letters · doi:10.1080/2150704x.2016.1193793 · Cited by 143 · see citing works

Building extraction from multi-source remote sensing images via deep deconvolution neural networks

Huang, Z., Cheng, G., Wang, H., Li, H., Shi, L., & Pan, C. (2016) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2016.7729471 · Cited by 134 · see citing works

ArtGAN: Artwork synthesis with conditional categorical GANs

Tan, W. R., Chan, C. S., Aguirre, H. E., & Tanaka, K. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296985 · Cited by 131 · see citing works

Urban Land Use and Land Cover Classification Using Remotely Sensed SAR Data through Deep Belief Networks

Lv, Q., Dou, Y., Niu, X., Xu, J., Xu, J., & Xia, F. (2015) · Journal of Sensors · doi:10.1155/2015/538063 · Cited by 126 · see citing works

Spectral-spatial classification of hyperspectral image using autoencoders

Lin, Z., Chen, Y., Zhao, X., & Wang, G. (2013) · International Conference on Information, Communications & Signal Processing (ICICS) · doi:10.1109/icics.2013.6782778 · Cited by 126 · see citing works

An Innovative Neural-Net Method to Detect Temporal Changes in High-Resolution Optical Satellite Imagery

Pacifici, F., Frate, F. D., Solimini, C., & Emery, W. J. (2007) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2007.902824 · Cited by 125 · see citing works

SatCNN: satellite image dataset classification using agile convolutional neural networks

Zhong, Y., Fei, F., Liu, Y., Zhao, B., Jiao, H., & Zhang, L. (2016) · Remote Sensing Letters · doi:10.1080/2150704x.2016.1235299 · Cited by 124 · see citing works

Deep learning for ocean remote sensing: an application of convolutional neural networks for super-resolution on satellite-derived SST data

Ducournau, A., & Fablet, R. (2016) · IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) · doi:10.1109/prrs.2016.7867019 · Cited by 124 · see citing works

Large patch convolutional neural networks for the scene classification of high spatial resolution imagery

Zhong, Y., Feng, F., & Zhang, L. (2016) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.10.025006 · Cited by 120 · see citing works

Co-saliency detection via looking deep and wide

Zhang, D., Han, J., Li, C., & Wang, J. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298918 · Cited by 119 · see citing works

The application of remote sensing techniques to sugarcane (Saccharum spp. hybrid) production: a review of the literature

Abdel-Rahman, E. M., & Ahmed, F. B. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160701874603 · Cited by 119 · see citing works

Underwater target classification in synthetic aperture sonar imagery using deep convolutional neural networks

Williams, D. P. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900011 · Cited by 119 · see citing works

Cloud detection of remote sensing images by deep learning

Shi, M., Xie, F., Zi, Y., et al. (2016) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2016.7729176 · Cited by 114 · see citing works

Semisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning

Ma, X., Wang, H., & Wang, J. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.09.001 · Cited by 113 · see citing works

A Hierarchical Oil Tank Detector With Deep Surrounding Features for High-Resolution Optical Satellite Imagery

Zhang, L., Shi, Z., & Wu, J. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2467377 · Cited by 112 · see citing works

Survey of geometric and statistical unmixing algorithms for hyperspectral images

Parente, M. C., & Plaza, A. (2010) · Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing · doi:10.1109/whispers.2010.5594929 · Cited by 106 · see citing works

Very High-Resolution Remote Sensing: Challenges and Opportunities [Point of View]

Benediktsson, J. A., Chanussot, J., & Moon, W. M. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2190811 · Cited by 105 · see citing works

Scene classification of high resolution remote sensing images using convolutional neural networks

Cheng, G., Ma, C., Zhou, P., Yao, X., & Han, J. (2016) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2016.7729193 · Cited by 105 · see citing works

Automated Extraction of Urban Road Facilities Using Mobile Laser Scanning Data

Yu, Y., Li, J., Guan, H., et al. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2399492 · Cited by 100 · see citing works

On Understanding Big Data Impacts in Remotely Sensed Image Classification Using Support Vector Machine Methods

Cavallaro, G., Riedel, M., Richerzhagen, M., et al. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2458855 · Cited by 100 · see citing works

A Comprehensive Evaluation of Spectral Distance Functions and Metrics for Hyperspectral Image Processing

Deborah, H., Richard, N., & Hardeberg, J. Y. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2403257 · Cited by 97 · see citing works

Hyperspectral image analysis using deep learning — A review

Petersson, H., Gustafsson, D. K. J., & Bergstrom, D. (2016) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2016.7820963 · Cited by 95 · see citing works

Multi-modal Auto-Encoders as Joint Estimators for Robotics Scene Understanding

Cadena, C., Dick, A., & D. Reid, I. (2016) · Robotics: Science and Systems XII · doi:10.15607/rss.2016.xii.041 · Cited by 93 · see citing works

Very deep learning for ship discrimination in Synthetic Aperture Radar imagery

Schwegmann, C. P., Kleynhans, W., Salmon, B. P., et al. (2016) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2016.7729017 · Cited by 91 · see citing works

Building and road detection from large aerial imagery

Saito, S., & Aoki, Y. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2083273 · Cited by 89 · see citing works

Deep learning in remote sensing applications: A meta-analysis and review

Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.04.015 · Cited by 2,456 · see citing works

Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft

Pasqualetto Cassinis, L., Fonod, R., & Gill, E. (2019) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2019.05.008 · Cited by 156 · see citing works

Deepfakes: current and future trends

Gambín, Á. F., Yazidi, A., Vasilakos, A., Haugerud, H., & Djenouri, Y. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10679-x · Cited by 145 · see citing works

ATSS Deep Learning-Based Approach to Detect Apple Fruits

Biffi, L. J., Mitishita, E., Liesenberg, V., Santos, A. A. D., Gonçalves, D. N., Estrabis, N. V., Silva, J. D. A., Osco, L. P., Ramos, A. P. M., Centeno, J. A. S., et al. (2020) · Remote Sensing · doi:10.3390/rs13010054 · Cited by 77 · see citing works

Development and evaluation of a deep learning model for real-time ground vehicle semantic segmentation from UAV-based thermal infrared imagery

Khoshboresh Masouleh, M., & Shah-Hosseini, R. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.07.009 · Cited by 51 · see citing works