Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Gradient-based learning applied to document recognition

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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works

Distinctive Image Features from Scale-Invariant Keypoints

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You Only Look Once: Unified, Real-Time Object Detection

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · 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

Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works

Histograms of Oriented Gradients for Human Detection

Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · 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

Speeded-Up Robust Features (SURF)

Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557 · cited 3× · see citing works

Extreme learning machine: Theory and applications

Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.126 · Cited by 13,285 · see citing works

Learning Spatiotemporal Features with 3D Convolutional Networks

Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.510 · Cited by 9,593 · see citing works

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248 · see citing works

Video Google: a text retrieval approach to object matching in videos

Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367 · see citing works

Stereo Processing by Semiglobal Matching and Mutual Information

Hirschmüller, H. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1166 · Cited by 4,199 · see citing works

Learning a Similarity Metric Discriminatively, with Application to Face Verification

Chopra, S., Hadsell, R., & LeCun, Y. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.202 · Cited by 4,036 · see citing works

Bag-of-visual-words and spatial extensions for land-use classification

Yang, Y., & Newsam, S. D. (2010) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/1869790.1869829 · Cited by 3,077 · 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

Region-based convolutional networks for accurate object detection and segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2437384 · Cited by 2,912 · 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

A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation

Mayer, N., Ilg, E., Hausser, P., Fischer, P., Cremers, D., Dosovitskiy, A., & Brox, T. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.438 · Cited by 2,488 · see citing works

AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification

Xia, G.-S., Hu, J., Hu, F., Shi, B., Bai, X., Zhong, Y., Zhang, L., & Lu, X. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2685945 · Cited by 2,423 · see citing works

Theory of the backpropagation neural network

Hecht-Nielsen (1989) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1989.118638 · Cited by 2,221 · see citing works

Neural Networks: Tricks of the Trade: Second Edition

(2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8 · Cited by 2,116 · cited 1× · 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

Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data

Kussul, N., Лавренюк, М. С., Skakun, S. V., & Шелестов, А. Ю. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2681128 · Cited by 1,799 · 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

Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes

Chen, X., Zhao, H., Li, P., & Yin, Z. (2006) · Remote Sensing of Environment · doi:10.1016/j.rse.2005.11.016 · Cited by 1,735 · see citing works

Self-taught learning

Raina, R., Battle, A. J., Lee, H., Packer, B., & Ng, A. Y. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273592 · Cited by 1,514 · 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

Deep Recurrent Neural Networks for Hyperspectral Image Classification

Mou, L., Ghamisi, P., & Zhu, X. X. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2636241 · Cited by 1,397 · see citing works

Target Classification Using the Deep Convolutional Networks for SAR Images

Chen, S., Wang, H., Xu, F., & Jin, Y. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2551720 · Cited by 1,359 · see citing works

LIFT: Learned Invariant Feature Transform

Yi, K. M., Trulls, E., Lepetit, V., & Fua, P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_28 · Cited by 1,306 · 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 · see citing works

Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach

Zhao, W., & Du, S. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2543748 · Cited by 1,217 · see citing works

Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery

Hu, F., Xia, G.-S., Hu, J., & Zhang, L. (2015) · Remote Sensing · doi:10.3390/rs71114680 · Cited by 1,199 · 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

Long-term Recurrent Convolutional Networks for Visual Recognition and Description

Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., & Darrell, T. (2014) · doi:10.21236/ada623249 · Cited by 1,081 · 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

Deep learning based feature selection for remote sensing scene classification

Zou, Q., Ni, L., Zhang, T., & Wang, Q. (2015) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2015.2475299 · Cited by 956 · 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

Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest

Alparone, L., Wald, L., Chanussot, J., et al. (2007) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2007.904923 · Cited by 894 · see citing works

Hyperspectral Pansharpening: A Review

Loncan, L., Almeida, L. B., Bioucas‐Dias, J. M., Briottet, X., Chanussot, J., Dobigeon, N., Fabre, S., Liao, W., Licciardi, G., Simões, M., et al. (2015) · HAL · doi:10.1109/mgrs.2015.2440094 · Cited by 827 · see citing works

Computing the stereo matching cost with a convolutional neural network

Žbontar, J., & LeCun, Y. (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.2015.7298767 · Cited by 816 · 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

Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning

Han, J., Zhang, D., Cheng, G., Guo, L., & Ren, J. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2374218 · Cited by 734 · 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 Network With Data Augmentation for SAR Target Recognition

Ding, J., Chen, B., Liu, H., & Huang, M. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2015.2513754 · Cited by 713 · 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

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

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

Multimodal Classification of Remote Sensing Images: A Review and Future Directions

Gómez‐Chova, L., Tuia, D., Moser, G., & Camps‐Valls, G. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2449668 · Cited by 510 · see citing works

Polarimetric SAR Image Classification Using Deep Convolutional Neural Networks

Zhou, Y., Wang, H., Xu, F., & Jin, Y. Q. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2016.2618840 · Cited by 509 · see citing works

Orientation robust object detection in aerial images using deep convolutional neural network

Zhu, H., Chen, X., Dai, W., Fu, K., Ye, Q., & Jiao, J. (2015) · 2015 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2015.7351502 · Cited by 483 · see citing works

Dense Semantic Labeling of Subdecimeter Resolution Images With Convolutional Neural Networks

Volpi, M., & Tuia, D. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2616585 · Cited by 460 · 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

Wide-Area Image Geolocalization with Aerial Reference Imagery

Workman, S., Souvenir, R., & Jacobs, N. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.451 · Cited by 412 · see citing works

Learning deep representations for ground-to-aerial geolocalization

Lin, T.-Y., Cui, Y., Belongie, S., & Hays, J. H. (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.2015.7299135 · Cited by 409 · 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

Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks

Audebert, N., Saux, B. L., & Lefèvre, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54181-5_12 · Cited by 386 · see citing works

Bag-of-Visual-Words Scene Classifier With Local and Global Features for High Spatial Resolution Remote Sensing Imagery

Zhu, Q., Zhong, Y., Zhao, B., Xia, G.-S., & Zhang, L. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2015.2513443 · Cited by 381 · see citing works

Dirichlet-Derived Multiple Topic Scene Classification Model for High Spatial Resolution Remote Sensing Imagery

Zhao, B., Zhong, Y., Xia, G.-S., & Zhang, L. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2496185 · Cited by 361 · see citing works

Data Fusion and Remote Sensing: An ever-growing relationship

Schmitt, M., & Zhu, X. X. (2016) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2016.2561021 · Cited by 359 · see citing works

Geographic Image Retrieval Using Local Invariant Features

Yang, Y. H., & Newsam, S. D. (2012) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2012.2205158 · Cited by 349 · 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

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

Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection

Lyu, H., Lu, H. M., & Mou, L. (2016) · Remote Sensing · doi:10.3390/rs8060506 · Cited by 333 · see citing works

MSTAR extended operating conditions: a tutorial

Keydel, E. R., Lee, S. W., & Moore, J. T. (1996) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.242059 · Cited by 313 · see citing works

Localizing and Orienting Street Views Using Overhead Imagery

Vo, N., & Hays, J. H. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46448-0_30 · Cited by 297 · see citing works

Unsupervised Spectral–Spatial Feature Learning via Deep Residual Conv–Deconv Network for Hyperspectral Image Classification

Mou, L., Ghamisi, P., & Zhu, X. X. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2748160 · Cited by 295 · see citing works

Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images

Zhang, P., Gong, M., Su, L., Liu, J., & Li, Z. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.02.013 · Cited by 294 · 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

Hyperspectral Image Superresolution by Transfer Learning

Yuan, Y., Zheng, X., & Lu, X. (2017) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2655112 · Cited by 266 · 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

Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection

Zhang, F., Du, B., Zhang, L., & Xu, M. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2569141 · Cited by 257 · see citing works

SAR Image segmentation based on convolutional-wavelet neural network and markov random field

Duan, Y., Liu, F., Jiao, L., Zhao, P., & Zhang, L. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.11.015 · Cited by 243 · see citing works

SAR ATR by a combination of convolutional neural network and support vector machines

Wagner, S. (2016) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2016.160061 · Cited by 233 · see citing works

Convolutional Neural Network Based Automatic Object Detection on Aerial Images

Ševo, I., & Avramović, A. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2016.2542358 · Cited by 229 · 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

Sea Ice Concentration Estimation During Melt From Dual-Pol SAR Scenes Using Deep Convolutional Neural Networks: A Case Study

Wang, L., Scott, K. A., Xu, L., & Clausi, D. A. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2543660 · Cited by 217 · see citing works

Joint 3D Scene Reconstruction and Class Segmentation

Häne, C., Zach, C., Cohen, A., Angst, R., & Pollefeys, M. (2013) · 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.2013.20 · Cited by 213 · see citing works

Superpixel-Based Difference Representation Learning for Change Detection in Multispectral Remote Sensing Images

Gong, M., Zhan, T., Zhang, P., & Miao, Q. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2650198 · Cited by 212 · see citing works

Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval

Zhou, W., Newsam, S. D., Li, C., & Shao, Z. (2017) · Remote Sensing · doi:10.3390/rs9050489 · Cited by 202 · see citing works

Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding

Han, J., Zhou, P., Zhang, D., Cheng, G., Guo, L., Liu, Z., Bu, S., & Wu, J. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2013.12.011 · Cited by 200 · see citing works

Semantic segmentation of urban scenes by learning local class interactions

Volpi, M., & Ferrari, V. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301377 · Cited by 184 · see citing works

Cataloging Public Objects Using Aerial and Street-Level Images — Urban Trees

Wegner, J. D., Branson, S., Hall, D., Schindler, K., & Perona, P. (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.2016.647 · Cited by 178 · see citing works

WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks

Durand, T., Thome, N., & Cord, M. (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.2016.513 · Cited by 168 · see citing works

Unsupervised Feature Learning Via Spectral Clustering of Multidimensional Patches for Remotely Sensed Scene Classification

Hu, F., Xia, G.-S., Wang, Z., Huang, X., Zhang, L., & Sun, H. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2444405 · Cited by 164 · see citing works

Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis

Volpi, M., Camps‐Valls, G., & Tuia, D. (2015) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2015.02.005 · Cited by 159 · 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

Discriminant deep belief network for high-resolution SAR image classification

Zhao, Z., Jiao, L., Zhao, J., Gu, J., & Zhao, J. K. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.05.028 · Cited by 149 · see citing works

BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

Santara, A., Mani, K., Hatwar, P., Singh, A., Garg, A., Padia, K., & Mitra, P. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2705073 · Cited by 148 · see citing works

HD Maps: Fine-Grained Road Segmentation by Parsing Ground and Aerial Images

Máttyus, G., Wang, S., Fidler, S., & 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.2016.393 · Cited by 142 · see citing works

Remote Sensing Image Fusion with Convolutional Neural Network

Zhong, J., Yang, B. F., Huang, G., et al. (2016) · Sensing and Imaging · doi:10.1007/s11220-016-0135-6 · Cited by 141 · see citing works

On the location dependence of convolutional neural network features

Workman, S., & Jacobs, N. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301385 · Cited by 136 · see citing works

Stacked Sparse Autoencoder in PolSAR Data Classification Using Local Spatial Information

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Deep Supervised and Contractive Neural Network for SAR Image Classification

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Learning High-level Features for Satellite Image Classification With Limited Labeled Samples

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Semantic Labeling of Aerial and Satellite Imagery

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Wishart Deep Stacking Network for Fast POLSAR Image Classification

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

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Classification of Polarimetric SAR Images Using Multilayer Autoencoders and Superpixels

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Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest

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

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Modeling and Detection of Geospatial Objects Using Texture Motifs

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A new research paradigm for global land cover mapping

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Look Wider to Match Image Patches With Convolutional Neural Networks

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Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions

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Joint height estimation and semantic labeling of monocular aerial images with CNNS

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Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping

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Very deep learning for ship discrimination in Synthetic Aperture Radar imagery

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Object Detection Based on Sparse Representation and Hough Voting for Optical Remote Sensing Imagery

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Fusing Meter-Resolution 4-D InSAR Point Clouds and Optical Images for Semantic Urban Infrastructure Monitoring

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An Emulator Toolbox to Approximate Radiative Transfer Models with Statistical Learning

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Benchmarking classification of earth-observation data: From learning explicit features to convolutional networks

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SAR ATR based on displacement- and rotation-insensitive CNN

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Zero-Shot Learning of SAR Target Feature Space With Deep Generative Neural Networks

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Support-Based Implementation of Bayesian Data Fusion for Spatial Enhancement: Applications to ASTER Thermal Images

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SAR-Based Terrain Classification Using Weakly Supervised Hierarchical Markov Aspect Models

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Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

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Hierarchical Recognition System for Target Recognition from Sparse Representations

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Deep Learning for Generic Object Detection: A Survey

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Deep Learning for Hyperspectral Image Classification: An Overview

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Perspectives in machine learning for wildlife conservation

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Self-Supervised Learning in Remote Sensing: A review

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Detecting mammals in UAV images: best practices to address a substantially imbalanced dataset with deep learning

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Towards the use of artificial intelligence on the edge in space systems: Challenges and opportunities

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On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID

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Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis

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Tackling climate change with machine learning

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A bird's-eye view of deep learning in bioimage analysis

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Deep Learning in Hyperspectral Unmixing: A Review

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Blind Hyperspectral Unmixing Using Autoencoders: A Critical Comparison

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Benchmarking and scaling of deep learning models for land cover image classification

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Applications and Challenges of Artificial Intelligence in Space Missions

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Recently emerging trends in big data analytic methods for modeling and combating climate change effects

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On the foundations of Earth foundation models

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Print Defect Mapping with Semantic Segmentation

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Survey of Machine Learning Applications of Convolutional Neural Networks to Medical Image Analysis

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Exploring Deep Learning for View-Based 3D Model Retrieval

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A Survey on Deep Learning Architectures and Frameworks for Cancer Detection in Medical Images Analysis

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