Deep Learning for Hyperspectral Image Classification: An Overview
Open accessAbstract
Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine learning methods. In addition, hyperspectral imaging often deals with an inherently nonlinear relation between the captured spectral information and the corresponding materials. In recent years, deep learning has been recognized as a powerful feature-extraction tool to effectively address nonlinear problems and widely used in a number of image processing tasks. Motivated by those successful applications, deep learning has also been introduced to classify HSIs and demonstrated good performance. This survey paper presents a systematic review of deep learning-based HSI classification literatures and compares several strategies for this topic. Specifically, we first summarize the main challenges of HSI classification which cannot be effectively overcome by traditional machine learning methods, and also introduce the advantages of deep learning to handle these problems. Then, we build a framework that divides the corresponding works into spectral-feature networks, spatial-feature networks, and spectral-spatial-feature networks to systematically review the recent achievements in deep learning-based HSI classification. In addition, considering the fact that available training samples in the remote sensing field are usually very limited and training deep networks require a large number of samples, we include some strategies to improve classification performance, which can provide some guidelines for future studies on this topic. Finally, several representative deep learning-based classification methods are conducted on real HSIs in our experiments.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Deep learning can quickly discover deep-layer and discriminative feature representations from training data.
“In addition, deep learning can quickly discover the deep-layer and discriminative feature representations from the training data [103].”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [103]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · 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
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · 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
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · 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
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235 · see citing works
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008 · 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
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061 · see citing works
Classification of hyperspectral remote sensing images with support vector machines
Melgani, F., & Bruzzone, L. (2004) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2004.831865 · Cited by 4,426 · see citing works
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
Williams, R. J., & Zipser, D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.2.270 · Cited by 4,369 · see citing works
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., & Fraundorfer, F. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762307 · Cited by 3,400 · see citing works
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
Self-citationConditional Image Synthesis With Auxiliary Classifier GANsPreprint
Odena, A., Olah, C., & Shlens, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.09585 · Cited by 2,968 · see citing works
Generating Sequences With Recurrent Neural Networks
Graves, A. (2013) · arXiv · doi:10.48550/arxiv.1308.0850 · Cited by 2,894 · 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
Self-citationDeep 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
Zhong, Z., Li, J., Luo, Z., & Chapman, M. A. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2755542 · Cited by 1,981 · 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
Classification of hyperspectral data from urban areas based on extended morphological profiles
Benediktsson, J. A., Palmason, J. A., & Sveinsson, J. R. (2005) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2004.842478 · Cited by 1,399 · see citing works
Self-citationDeep 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
Self-citationSpectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network
Li, Y., Zhang, H., & Shen, Q. (2017) · Remote Sensing · doi:10.3390/rs9010067 · Cited by 1,340 · 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 16× · see citing works
“In order to use DBN as a classifier, one can add a logistic regression layer at the end of the network to form a spectral classifier.”
BackgroundInfluentialSelf-citationZhao, W., & Du, S. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2543748 · Cited by 1,217 · see citing works
Hu, F., Xia, G.-S., Hu, J., & Zhang, L. (2015) · Remote Sensing · doi:10.3390/rs71114680 · Cited by 1,199 · see citing works
Hyperspectral Image Classification Using Dictionary-Based Sparse Representation
Chen, Y., Nasrabadi, N. M., & Tran, T. D. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2129595 · Cited by 1,158 · see citing works
Self-citationComposite Kernels for Hyperspectral Image Classification
Camps‐Valls, G., Gómez‐Chova, L., Muñoz-Marı́, J., Vila‐Francés, J., & Calpe‐Maravilla, J. (2006) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2005.857031 · Cited by 1,069 · 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
Going Deeper With Contextual CNN for Hyperspectral Image Classification
Lee, H., & Kwon, H. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2725580 · Cited by 1,041 · see citing works
3-D Deep Learning Approach for Remote Sensing Image Classification
Hamida, A. B., Benoît, A., Lambert, P., & Amar, C. B. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2818945 · Cited by 929 · see citing works
Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., & Plaza, A. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762087 · Cited by 909 · see citing works
Self-citationCascaded Recurrent Neural Networks for Hyperspectral Image Classification
Hang, R., Liu, Q., Hong, D., & Ghamisi, P. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2899129 · Cited by 841 · see citing works
Self-citationHyperspectral 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
Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis
Bandos, T. V., Bruzzone, L., & Camps‐Valls, G. (2009) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2008.2005729 · Cited by 746 · see citing works
Generative Adversarial Networks for Hyperspectral Image Classification
Zhu, L., Chen, Y., Ghamisi, P., & Benediktsson, J. A. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2805286 · Cited by 733 · see citing works
Self-citationLi, J., Bioucas‐Dias, J. M., & Plaza, A. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2162649 · Cited by 732 · 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
Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering
Kang, X., Li, S., & Benediktsson, J. A. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2264508 · Cited by 704 · see citing works
Self-citationMultisource Remote Sensing Data Classification Based on Convolutional Neural Network
Xu, X., Li, W., Ran, Q., Du, Q., Gao, L., & Zhang, B. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2756851 · Cited by 699 · see citing works
Advanced Spectral Classifiers for Hyperspectral Images: A review
Ghamisi, P., Plaza, J., Chen, Y., Li, J., & Plaza, A. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2016.2616418 · Cited by 687 · see citing works
Self-citationDeep 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
He, L., Li, J., Liu, C., & Li, S. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2765364 · Cited by 630 · see citing works
Self-citationDiverse Region-Based CNN for Hyperspectral Image Classification
Zhang, M., Li, W., & Du, Q. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2809606 · Cited by 609 · see citing works
A new deep convolutional neural network for fast hyperspectral image classification
Paoletti, M. E., Haut, J. M., Plaza, J., & Plaza, A. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.11.021 · Cited by 606 · see citing works
Li, J., Bioucas‐Dias, J. M., & Plaza, A. (2010) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2010.2060550 · Cited by 592 · see citing works
Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization
Shutao Li, A., Dian, R., Fang, L., & Bioucas‐Dias, J. M. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2836307 · Cited by 584 · see citing works
Self-citationHyperspectral Image Classification With Deep Learning Models
Yang, X., Ye, Y., Li, X., Lau, R. Y. K., Zhang, X., & Huang, X. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2815613 · Cited by 582 · see citing works
Hyperspectral Image Classification With Deep Feature Fusion Network
Song, W., Shutao Li, A., Fang, L., & Lu, T. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2794326 · Cited by 576 · see citing works
Self-citationDeep Pyramidal Residual Networks for Spectral–Spatial Hyperspectral Image Classification
Paoletti, M. E., Haut, J. M., Fernández-Beltrán, R., Plaza, J., Plaza, A., & Pla, F. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2860125 · Cited by 534 · 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
Learning and Transferring Deep Joint Spectral–Spatial Features for Hyperspectral Classification
Yang, J., Zhao, Y., & Chan, J. C.-W. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2698503 · Cited by 488 · see citing works
Deep learning for remote sensing image classification: A survey
Li, Y., Zhang, H., Xue, X., Jiang, Y., & Shen, Q. (2018) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1264 · Cited by 486 · see citing works
Generalized Composite Kernel Framework for Hyperspectral Image Classification
Li, J., Marpu, P., Plaza, A., et al. (2013) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2012.2230268 · Cited by 484 · see citing works
Semi-Supervised Deep Learning Using Pseudo Labels for Hyperspectral Image Classification
Wu, H., & Prasad, S. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2772836 · Cited by 466 · see citing works
Hyperspectral Image Classification With Independent Component Discriminant Analysis
Villa, A., Benediktsson, J. A., Chanussot, J., & Christian Jutten, B. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2153861 · Cited by 429 · see citing works
Self-citationEstimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method
Liang, L., Di, L., Zhang, L., et al. (2015) · Remote Sensing of Environment · doi:10.1016/j.rse.2015.04.032 · Cited by 418 · see citing works
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
Licciardi, G., Marpu, P., Chanussot, J., et al. (2011) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2011.2172185 · Cited by 398 · see citing works
Fang, L., Shutao Li, A., Duan, W., Ren, J., & Benediktsson, J. A. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2445767 · Cited by 371 · see citing works
Self-citationLearning to Diversify Deep Belief Networks for Hyperspectral Image Classification
Zhong, P., Gong, Z., Shutao Li, A., & Schönlieb, C. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2675902 · Cited by 357 · see citing works
Capsule Networks for Hyperspectral Image Classification
Paoletti, M. E., Haut, J. M., Fernández-Beltrán, R., Plaza, J., Plaza, A., Li, J., & Pla, F. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2871782 · Cited by 348 · see citing works
Limitations of Principal Components Analysis for Hyperspectral Target Recognition
Prasad, S., & Bruce, L. M. (2008) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2008.2001282 · Cited by 340 · see citing works
Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network
Cao, X., Zhou, F., Xu, L., Meng, D., Xu, Z., & Paisley, J. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2799324 · Cited by 331 · see citing works
Ghamisi, P., Maggiori, E., Shutao Li, A., de Souza, R. M., Tarablaka, Y., Moser, G., Giorgi, A. D., Fang, L., Chen, Y., Chi, M., et al. (2018) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2018.2854840 · Cited by 325 · see citing works
Self-citationSpectral–Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation
Fang, L., Li, S., Kang, X., & Benediktsson, J. A. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2318058 · Cited by 321 · see citing works
Self-citationMei, S., Ji, J., Hou, J., Li, X., & Du, Q. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2693346 · Cited by 312 · see citing works
Exploring Hierarchical Convolutional Features for Hyperspectral Image Classification
Cheng, G., Li, Z., Han, J., Yao, X., & Guo, L. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2841823 · Cited by 310 · see citing works
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
Self-citationRandom-Selection-Based Anomaly Detector for Hyperspectral Imagery
Du, B., & Zhang, L. (2010) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2010.2081677 · Cited by 293 · 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
Semisupervised hyperspectral image classification based on Generative Adversarial Networks
Zhan, Y., Hu, D., Wang, Y., & Yu, X. (2018) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2780890 · Cited by 283 · see citing works
A spatial–spectral kernel-based approach for the classification of remote-sensing images
Fauvel, M., Chanussot, J., & Benediktsson, J. A. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.03.035 · Cited by 281 · see citing works
Self-citationConvolutional Recurrent Neural Networks forHyperspectral Data Classification
Wu, H., & Prasad, S. (2017) · Remote Sensing · doi:10.3390/rs9030298 · Cited by 275 · see citing works
Hyperspectral Images Classification With Gabor Filtering and Convolutional Neural Network
Chen, Y., Zhu, L., Ghamisi, P., Jia, X., Li, G., & Tang, L. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2764915 · Cited by 270 · see citing works
Self-citationFeature Extraction With Multiscale Covariance Maps for Hyperspectral Image Classification
He, N., Paoletti, M. E., Haut, J. M., Fang, L., Shutao Li, A., Plaza, A., & Plaza, J. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2860464 · Cited by 270 · see citing works
Self-citationSupervised Deep Feature Extraction for Hyperspectral Image Classification
Liu, B., Yu, X., Zhang, P., Yu, A., Fu, Q., & Wei, X. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2769673 · Cited by 265 · see citing works
Haut, J., Paoletti, M. E., Plaza, J., Li, J. Y., & Plaza, A. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2838665 · Cited by 263 · 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
Hyperspectral and LiDAR Data Fusion Using Extinction Profiles and Deep Convolutional Neural Network
Ghamisi, P., Höfle, B., & Zhu, X. X. (2016) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2016.2634863 · Cited by 253 · see citing works
Self-citationDeep Fusion of Remote Sensing Data for Accurate Classification
Chen, Y., Li, C., Ghamisi, P., Jia, X., & Gu, Y. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2704625 · Cited by 246 · see citing works
Self-citationA Recurrent Neural Network that Learns to Count
Rodriguez, P. F., Wiles, J., & Elman, J. L. (1999) · Connection Science · doi:10.1080/095400999116340 · Cited by 245 · see citing works
Deformable Convolutional Neural Networks for Hyperspectral Image Classification
Zhu, J., Fang, L., & Ghamisi, P. (2018) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2018.2830403 · Cited by 244 · see citing works
Self-citationLiang, H., & Li, Q. (2016) · Remote Sensing · doi:10.3390/rs8020099 · Cited by 238 · see citing works
Jiao, L., Liang, M., Chen, H., Yang, S., Liu, H., & Cao, X. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2710079 · Cited by 217 · see citing works
Estimating Soil Salinity Under Various Moisture Conditions: An Experimental Study
Yang, X., & Yu, Y. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2646420 · Cited by 211 · see citing works
Zhong, Y., & Zhang, L. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2162589 · Cited by 209 · 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
Classification of Hyperspectral Imagery Using a New Fully Convolutional Neural Network
Li, J., Zhao, X. B., Li, Y., Du, Q., Xi, B., & Hu, J. (2018) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2786272 · Cited by 180 · see citing works
Hyperspectral Image Classification via Multiple-Feature-Based Adaptive Sparse Representation
Fang, L., Wang, C., Li, S., & Benediktsson, J. A. (2017) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2017.2664480 · Cited by 172 · see citing works
Self-citationYokoya, N., Chan, J. C.-W., & Segl, K. (2016) · Remote Sensing · doi:10.3390/rs8030172 · Cited by 171 · see citing works
Target detection based on a dynamic subspace
Du, B., & Zhang, L. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.07.005 · Cited by 167 · see citing works
Deep Learning With Attribute Profiles for Hyperspectral Image Classification
Aptoula, E., Özdemir, M. C., & Yanikoglu, B. A. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2016.2619354 · Cited by 156 · see citing works
Classification of Hyperspectral Images by Gabor Filtering Based Deep Network
Kang, X., Li, C., Li, S., & Lin, H. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2767185 · Cited by 151 · see citing works
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
Two-Stream Deep Architecture for Hyperspectral Image Classification
Hao, S., Wang, W., Ye, Y., Nie, T., & Bruzzone, L. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2778343 · Cited by 140 · see citing works
Zhang, M., Gong, M., Mao, Y., Li, J., & Wu, Y. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2876123 · Cited by 126 · see citing works
Extinction Profiles Fusion for Hyperspectral Images Classification
Fang, L., He, N., Shutao Li, A., Ghamisi, P., & Benediktsson, J. A. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2768479 · Cited by 124 · see citing works
Self-citationSelf-Taught Feature Learning for Hyperspectral Image Classification
Kemker, R., & Kanan, C. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2651639 · Cited by 120 · see citing works
Hyperspectral Image Classification With Squeeze Multibias Network
Fang, L., Liu, G., Shutao Li, A., Ghamisi, P., & Benediktsson, J. A. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2865953 · Cited by 116 · see citing works
Self-citationRecursive Autoencoders-Based Unsupervised Feature Learning for Hyperspectral Image Classification
Zhang, X., Liang, Y., Li, C., Huyan, N., Jiao, L., & Zhou, H. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2737823 · Cited by 96 · see citing works
Probabilistic Fusion of Pixel-Level and Superpixel-Level Hyperspectral Image Classification
Shutao Li, A., Lu, T., Fang, L., Jia, X., & Benediktsson, J. A. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2603190 · Cited by 82 · see citing works
Self-citationFrom Subpixel to Superpixel: A Novel Fusion Framework for Hyperspectral Image Classification
Lu, T., Shutao Li, A., Fang, L., Jia, X., & Benediktsson, J. A. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2691906 · Cited by 81 · see citing works
Self-citationDeep Learning With Grouped Features for Spatial Spectral Classification of Hyperspectral Images
Zhou, X., Li, S., Tang, F., Qin, K., Hu, S., & Liu, S. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2016.2630045 · Cited by 69 · see citing works
Self-citationDeep Hashing Neural Networks for Hyperspectral Image Feature Extraction
Fang, L., Liu, Z., & Song, W. (2019) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2019.2899823 · Cited by 67 · see citing works
Self-citationHyperspectral Image Classification Based on Nonlinear Spectral–Spatial Network
Pan, B., Shi, Z., Zhang, N., & Xie, S. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2016.2608963 · Cited by 60 · see citing works
Spatial Discontinuity-Weighted Sparse Unmixing of Hyperspectral Images
Zhang, S., Li, J., Wu, Z., et al. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2825457 · Cited by 59 · see citing works
Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification
Singhal, V., Aggarwal, H. K., Tariyal, S., & Majumdar, A. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2704590 · Cited by 53 · see citing works
Lv, Q., Niu, X., Dou, Y., Xu, J., & Lei, Y. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2016.2517178 · Cited by 46 · see citing works
Encoding Spectral and Spatial Context Information for Hyperspectral Image Classification
Sun, X., Zhou, F., Dong, J., Gao, F., Mu, Q., & Wang, X. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2759168 · Cited by 35 · see citing works
Greedy deep dictionary learning for hyperspectral image classification
Tariyal, S., Aggarwal, H. K., & Majumdar, A. (2016) · Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing · doi:10.1109/whispers.2016.8071740 · Cited by 13 · see citing works
Hyperspectral images classification with hybrid deep residual network
Song, W., Li, S., & Li, Y. (2017) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2017.8127433 · Cited by 5 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 4
- [31][31] A. Krizhevsky, I. Sutskever, and G. E. Hinton, 'Imagenet classification with deep convolutional neural networks,' in Proc. Adv. Neural Inf. Process. Syst. , 2012, pp. 1097-1105. -
- [34][34] A. Bordes, X. Glorot, J. Weston, and Y. Bengio, 'Joint learning of words and meaning representations for open-text semantic parsing,' in Proc. Int. Conf. Art. Intell. Stat , 2012, pp. 127-135. -
- [45][45] I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, 'Generative adversarial nets,' in Proc. Adv. Neural Inf. Process. Syst. , 2014, pp. 2672-2680. -
- [102][102] S. Sabour, N. Frosst, and G. E. Hinton, 'Dynamic routing between capsules,' in in Proc. Adv. Neural Inf. Process. Syst , 2017, pp. 38563866. -
Cited by
Sorted by citations
Dif-Fusion: Toward High Color Fidelity in Infrared and Visible Image Fusion With Diffusion Models
Yue, J., Fang, L., Xia, S., Deng, Y., & Ma, J. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3322046 · Cited by 304 · see citing works
Self-citationManifold, B., Men, S., Hu, R., & Fu, D. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00309-y · Cited by 152 · see citing works
Automated Visual Defect Classification for Flat Steel Surface: A Survey
Luo, Q., Fang, X., Su, J., Zhou, J., Zhou, B., Yang, C., Liu, L., Gui, W., & Tian, L. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3030167 · Cited by 148 · see citing works
- Authors
- Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., & Benediktsson, J. A.
- Affiliations
- Harbin Institute of Technology (CN)
- Helmholtz-Zentrum Dresden-Rossendorf (DE)
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2019-09
- Volume
- 57
- Issue
- 9
- Pages
- 6690-6709
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2942454403
- Cited by
- 2,007
- References
- 114
- Citations by year
- 2026: 254 · 2025: 352 · 2024: 398 · 2023: 324 · 2022: 286 · 2021: 225 · 2020: 153 · 2019: 15
- FWCI
- 113.62
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Geoscience and Remote Sensing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61890962, 61520106001, 61771192)
- Science and Technology Plan Project Fund of Hunan Province (CX2018B171, 2017RS3024, 2018TP1013)
- Science and Technology Talents Program of Hunan Association for Science and Technology (2017TJ-Q09)
- National Key Research and Development Program of China (2018YFB1305200)
- Topics
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Keywords
- hyperspectral image classification, deep learning, spectral-spatial fusion, limited training data, remote sensing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
