A survey of remote sensing image classification based on CNNs

References

• Alshehhi, R., Marpu, P. R., Woon, W. L., & Mura, M. D. (2017). Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 139–149.
   Web of Science ®Google Scholar
• Bai, X., Zhang, H., & Zhou, J. (2014). Vhr object detection based on structural feature extraction and query expansion. IEEE Transactions on Geoscience and Remote Sensing, 52, 6508–6520.
   Web of Science ®Google Scholar
• Bai, Y., Jiang, D. M., Pei, J. J., Zhang, N., & Bai, Y. (2018). Application of an improved ELU convolution neural network in the SAR image ship detection. Bulletin of Surveying and Mapping, 1, 125–128.
   Google Scholar
• Ball, J. E., Anderson, D. T., & Chan, C. S. (2017). A comprehensive survey of deep learning in remote sensing: theories, tools and challenges for the community. Journal of Applied Remote Sensing, 11, 4.
   Web of Science ®Google Scholar
• Benedek, C., Descombes, X., & Zerubia, J. (2011). Building development monitoring in multitemporal remotely sensed image pairs with stochastic birth-death dynamics. IEEE Transactions on Pattern Analysis & Machine Intelligence, 34, 33–50.
   Web of Science ®Google Scholar
• Bosch, A., & Zisserman, A. (2008). Scene classification using a hybrid generative/discriminative approach. IEEE Computer Society, 30(4), 712–727.
   Google Scholar
• Castelluccio, M., Poggi, G., Sansone, C., & Verdoliva, L. (2015). Land use classification in remote sensing images by convolutional neural networks. Acta Ecologica Sinica, 28(2), 627–635.
   Google Scholar
• Chang, L., Deng, X. M., Zhou, M. Q., Wu, Z. K., Yuan, Y., Yang, S., & Wang, H. A. (2016). Convolutional neural networks in image understanding. Acta Automatica Sinica, 42, 1300–1312.
   Google Scholar
• Chen, Y., Fan, R. S., Wang, J. X., Wu, Z. L., & Sun, R. X. (2017). High resolution Image Classification method combining with minimum noise fraction rotation and convolution neural network. Laser & Optoelectronics Progress, 54, 434–439.
   Google Scholar
• Cheng, G., & Han, J. (2016). A survey on object detection in optical remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 11–28.
   Web of Science ®Google Scholar
• Cheng, G., Han, J., Zhou, P., & Guo, L. (2014). Multi-class geospatial object detection and geographic image classification based on collection of part detectors. Isprs Journal of Photogrammetry & Remote Sensing, 98, 119–132.
   Web of Science ®Google Scholar
• Cheng, G., Zhou, P., & Han, J. (2016). Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 54, 7405–7415.
   Web of Science ®Google Scholar
• Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297.
   Web of Science ®Google Scholar
• Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 886–893), San Diego, CA.
   Google Scholar
• Deng, Z., Lei, L., Sun, H., Zou, H., Zhou, S., & Zhao, J. (2017). An enhanced deep convolutional neural network for densely packed objects detection in remote sensing images. 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) (pp. 1–4), Shanghai, China.
   Google Scholar
• Goodfellow, I. J., Warde-Farley, D., Mirza, M., Courville, A., & Bengio, Y. (2013). Maxout networks. arXiv preprint arXiv:1302.4389.
   Google Scholar
• Guo, Z., Shao, X., Xu, Y., Miyazaki, H., Ohira, W., & Shibasaki, R. (2016). Identification of village building via google earth images and supervised machine learning methods. Remote Sensing, 8, 271.
   Web of Science ®Google Scholar
• Hara, K., Saito, D., & Shouno, H. (2015). Analysis of function of rectified linear unit used in deep learning. International Joint Conference on Neural Networks, Killarney, Ireland.
   Google Scholar
• He, H. Q., Du, J., Chen, T., & Chen, X. Y. (2017). Remote sensing image water body extraction combing NDWI with convolutional neural network. Remote Sensing Information, 32, 82–86.
   Google Scholar
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceeding IEEE Conference Computer Vision and Pattern Recognition,  Las Vegas, NV.
   Google Scholar
• He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778), Boston, MA.
   Google Scholar
• Hecht-Nielsen, R. (1989). Theory of the backpropagation neural network. Neural Networks, 1989. IJCNN. International Joint Conference, Washington, DC.
   Google Scholar
• Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. science, 313(5786), 504–507.
   Web of Science ®Google Scholar
• Huang, G., Liu, Z., Maaten, L. V. D., & Weinberger, K. Q. (2016). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4700–4708), Las Vegas, NV.
   Google Scholar
• Huang, J., Jiang, Z. G., Zhang, H. P., & Yao, Y. (2017). Ship object detection in remote sensing images using convolutional neural networks. Journal of Beijing University of Aeronautics and Astronautics, 43, 1841–1849.
   Google Scholar
• Huang, L., Liu, B., Li, B., Guo, W., Yu, W., Zhang, Z., & Yu, W. (2017). OpenSARShip: A dataset dedicated to sentinel-1 ship interpretation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 1.
   Web of Science ®Google Scholar
• Ishii, T., Nakamura, R., Nakada, H., Mochizuki, Y., & Ishikawa, H. (2015). Surface object recognition with cnn and svm in landsat 8 images. 2015 14th IAPR International Conference on Machine Vision Applications (MVA), Tokyo, Japan.
   Google Scholar
• Jia, K., Li, Q. Z., Tian, Y. C., & Wu, B. F. (2011). A review of classification methods of remote sensing. Spectroscopy and Spectral Analysis, 31, 2618–2623.
   Web of Science ®Google Scholar
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. International Conference on Neural Information Processing Systems (pp. 1097–1105), Lake Tahoe, NV.
   Google Scholar
• Li, D. R. (2008). Development prospect of photogrammetry and remote sensing. Geomatics and Information Science of Wuhan University, 33, 1211–1215.
   Google Scholar
• Li, D. R., Tong, Q. X., Li, R. X., Gong, J. Y., & Zhang, L. P. (2012). Current issues in high-resolution Earth observation technology. Sci China Earth Sci, 55, 1043–1051.
   Web of Science ®Google Scholar
• Li, D. R., Zhang, L. P., & Xia, G. S. (2014). Automatic analysis and mining of remote sensing big data. Acta Geodaetica Et Cartographica Sinica, 43, 1211–1216.
   Google Scholar
• Li, H., Fu, K., Xu, G., Zheng, X., Ren, W., & Sun, X. (2017). Scene classification in remote sensing images using a two-stage neural network ensemble model. Remote Sensing Letters, 8, 557–566.
   Web of Science ®Google Scholar
• Li, J., Zhang, R., & Li, Y. (2016). Multiscale convolutional neural network for the detection of built-up areas in high-resolution SAR images. Geoscience and Remote Sensing Symposium (IGARSS) (pp. 910–913),  Beijing, China.
   Google Scholar
• Li, J. W., Qu, C. W., & Peng, S. J. (2018). Ship classification for unbalanced SAR dataset based on convolutional neural network. Journal of Applied Remote Sensing, 12(3), 035010.
   Web of Science ®Google Scholar
• Li, P., Zang, Y., Wang, C., Li, J., Cheng, M., Luo, L., & Yu, Y. (2016). Road network extraction via deep learning and line integral convolution. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 1599–1602), Beijing, China.
   Google Scholar
• Li, W., Fu, H., Yu, L., & Cracknell, A. (2016). Deep learning based oil palm tree detection and counting for high-resolution remote sensing images. Remote Sensing, 9, 22.
   Web of Science ®Google Scholar
• Li, Z., Li, Y. S., Wu, X., Liu, G., Lu, H., & Tang, M. (2017). Hollow village building detection method using high resolution remote sensing image based on CNN. Transactions of the Chinese Society of Agricultural Machinery, 48, 160–165.
   Google Scholar
• Li, Z. J., Li, X., . J., Liu, T., Xie, J. W., & Yang, S. (2015). Remote sensing cloud computing: Current research and prospect. Journal of Equipment Academy, 26, 95–100.
   Google Scholar
• Lin, M., Chen, Q., & Yan, S. (2013). Network in network. arXiv preprint arXiv:1312.4400.
   Google Scholar
• Liu, W., Wen, Y., Yu, Z., & Yang, M. (2016). Large-margin softmax loss for convolutional neural networks. ICML, 2(3), 7.
   Google Scholar
• Liu, Y., Zhong, Y., Fei, F., Zhu, Q., & Qin, Q. (2018). Scene classification based on a deep random-scale stretched convolutional neural network. Remote Sensing, 10, 444.
   Web of Science ®Google Scholar
• Long, Y., Gong, Y., Xiao, Z., & Liu, Q. (2017). Accurate object localization in remote sensing images based on convolutional neural networks. IEEE Transactions on Geoscience & Remote Sensing, 55, 2486–2498.
   Web of Science ®Google Scholar
• Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60, 91–110.
   Web of Science ®Google Scholar
• Lu, H., Fu, X., He, Y. N., Li, L. G., Zhuang, W. H., & Liu, T. G. (2015). Cultivated land information extraction from high resolution UAV images based on transfer learning. Transactions of the Chinese Society of Agricultural Machinery, 46, 274–279.
   Google Scholar
• Luo, J. C., Zhou, C. H., & Yang, Y. (2001). ANN remote sensing classsification model and its integration approach with Geo-knowledge. Journal of Remote Sensing, 5, 122–129.
   Google Scholar
• Lv, F. H., Shu, N., Gong, Y., Guo, Q., & Qu, X. G. (2017). Regular building extraction from high resolution image based on multilevel-features. Geomatics and Information Science of Wuhan University, 42, 1–5.
   Google Scholar
• Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2017). Convolutional neural networks for large-scale remote-sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 55, 645–657.
   Web of Science ®Google Scholar
• Marmanis, D., Datcu, M., Esch, T., & Stilla, U. (2016). Deep learning earth observation classification using imagenet pretrained networks. IEEE Geoscience and Remote Sensing Letters, 13, 105–109.
   Web of Science ®Google Scholar
• Nogueira, K., Penatti, O. A. B., & Santos, J. A. D. (2017). Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognition, 61, 539–556.
   Web of Science ®Google Scholar
• Nogueira, K., Santos, J. A. D., Fornazari, T., Silva, T. S. F., Morellato, L. P., & Torres, R. D. S. (2017). In Towards vegetation species discrimination by using data-driven descriptors. 2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS) (pp. 1–6), IEEE, Cancun, Mexico.
   Google Scholar
• Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 971–987.
   Web of Science ®Google Scholar
• Penatti, O. A. B., Nogueira, K., & Santos, J. A. D. (2015). Do deep features generalize from everyday objects to remote sensing and aerial scenes domains. Computer Vision and Pattern Recognition Workshops 44–51.
   Google Scholar
• Qu, C., Ren, Y. H., Liu, Y. L., & Li, Y. (2017). Functional classification of urban buildings in high resolution remote sensing images through POI-assisted analysis. Journal of Geo-information Science, 19, 831–837.
   Google Scholar
• Ren, S., Girshick, R., Girshick, R., & Sun, J. (2017). Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis & Machine Intelligence, 39, 1137–1149.
   PubMed Web of Science ®Google Scholar
• Saito, S., & Aoki, Y. (2015). Building and road detection from large aerial imagery. Image Processing: Machine Vision Applications VIII (Vol. 9405, p. 94050K), International Society for Optics and Photonics, San Diego, CA.
   Google Scholar
• Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013). Overfeat: Integrated recognition, localization and detection using convolutional networks. unpublished paper. [Online]. Retrieved from http://arxiv.org/abs/1312.6229
   Google Scholar
• Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
   Google Scholar
• Song, W. J., Liu, P., Wang, L. Z., & Lv, K. (2014). Intelligent processing of remote sensing big data: status and challenges. Journal of Engineering Studies, 6, 259–265.
   Google Scholar
• Tai, C., Xiao, T., Zhang, Y., Wang, X., & Weinan, E. (2015). Convolutional neural networks with low-rank regularization. arXiv preprint arXiv:1511.06067.
   Google Scholar
• Tang, J., Deng, C., Huang, G. B., & Zhao, B. (2014). Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine. IEEE Transactions on Geoscience & Remote Sensing, 53, 1174–1185.
   Web of Science ®Google Scholar
• Tao, C., Tan, Y., Cai, H., & Tian, J. (2011). Airport detection from large ikonos images using clustered sift keypoints and region information. IEEE Geoscience and Remote Sensing Letters, 8, 128–132.
   Web of Science ®Google Scholar
• Wang, W. G., Tian, B., Liu, Y., Liu, L., & Li, J. X. (2017). Study on the electrical devices in UAV images based on region based convolutional neural networks. Journal of Geo-information Science, 19, 256–263.
   Google Scholar
• Weng, Q., Mao, Z., Lin, J., & Guo, W. (2017). Land-use classification via extreme learning classifier based on deep convolutional features. IEEE Geoscience and Remote Sensing Letters, 14, 704–708.
   Web of Science ®Google Scholar
• Wu, G., Shao, X., Guo, Z., Chen, Q., Yuan, W., Shi, X., … Shibasaki, R. (2018). Automatic building segmentation of aerial imagery using multi-constraint fully convolutional networks. Remote Sensing, 10, 407.
   Web of Science ®Google Scholar
• Wu, H., Zhang, H., Zhang, J., & Xu, F. (2015). Typical target detection in satellite images based on convolutional neural networks. IEEE International Conference on Systems (pp. 2956–2961), Las Vegas, NV.
   Google Scholar
• Xia, G. S., Hu, J., Hu, F., Shi, B., Bai, X., Zhong, Y., … Lu, X. (2016). Aid: A benchmark data set for performance evaluation of aerial scene classification. IEEE Transactions on Geoscience & Remote Sensing, 55(7), 3965–3981.
   Web of Science ®Google Scholar
• Xia, G. S., Yang, W., Delon, J., Gousseau, Y., Sun, H., & Maitre, H. (2010). Structural high-resolution satellite image indexing. ISPRS TC VII Symposium - 100 Years ISPRS,  Vienna, Austria.
   Google Scholar
• Xia, M., Cao, G., Wang, G. Y., & Shang, Y. F. (2017). Remote sensing image classification based on deep learning and conditional random fields. Journal of Image and Graphics, 22, 1289–1301.
   Google Scholar
• Xu, S. H., Mu, X. D., Zhao, P., & Ma, J. (2016). Scene classification of remote sensing image based on multi-scale feature and deep neural network. Acta Geodaetica Et Cartographica Sinica, 45, 834–840.
   Google Scholar
• Xu, Y., Wu, L., Xie, Z., & Chen, Z. (2018). Building extraction in very high resolution remote sensing imagery using deep learning and guided filters. Remote Sensing, 10, 144.
   Web of Science ®Google Scholar
• Yang, Y., & Newsam, S. (2010). Bag-of-visual-words and spatial extensions for land-use classification. Sigspatial International Conference on Advances in Geographic Information Systems, New York, NY.
   Google Scholar
• Yao, X. K., Wang, L. H., Huo, H., & Fang, T. (2017). Airplane object detection in high resolution remote sensing imagery based on multi-structure convolutional neural network. Computer Engineering, 43, 259–267.
   Google Scholar
• Zeng, D., Chen, S., Chen, B., & Li, S. (2018). Improving remote sensing scene classification by integrating global-context and local-object features. Remote Sensing, 10, 734.
   Web of Science ®Google Scholar
• Zhang, B. (2017). Current status and future prospects of remote sensing. Bulletin of Chinese Academy of Sciences, 32, 774–784.
   Google Scholar
• Zhang, F., Du, B., Zhang, L., & Xu, M. (2016). Weakly supervised learning based on coupled convolutional neural networks for aircraft detection. IEEE Transactions on Geoscience and Remote Sensing, 54, 5553–5563.
   Web of Science ®Google Scholar
• Zhang, L., Zhang, L., & Du, B. (2016). Deep learning for remote sensing data: A technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine, 4(2), 22–40.
   Web of Science ®Google Scholar
• Zhang, Q., Wang, Y., Liu, Q., Liu, X., & Wang, W. (2016). CNN based suburban building detection using monocular high resolution google earth images. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 661–664), IEEE, Beijing, China.
   Google Scholar
• Zhang, W., Zheng, K., Tang, P., & Zhao, L. J. (2017). Land cover classification with features extracted by deep convolutional neural network. Journal of Image and Graphics, 22, 1144–1153.
   Google Scholar
• Zhang, Y. H., Xia, G. H., Han, X., He, J., Ge, T. T., & Wang, J. G. (2018). Road extraction from multi-source high resolution remote sensing image based on fully convolutional network. 2018 International Conference on Audio, Language and Image Processing (ICALIP) (pp. 201–204), IEEE, Shanghai, China.
   Google Scholar
• Zhang, Z. X., Wang, Y. H., Liu, Q. J., Li, L. L., & Wang, P. (2016). A CNN based functional zone classification method for aerial images. IGARSS,  Beijing, China.
   Google Scholar
• Zhao, B., Zhong, Y., Xia, G. S., & Zhang, L. (2016). Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery. IEEE Transactions on Geoscience & Remote Sensing, 54, 2108–2123.
   Web of Science ®Google Scholar
• Zhao, Y. S. (2013). Principles and methods of analysis of remote sensing applications. Beijing, China: Science Press.
   Google Scholar
• Zhong, Y., Fei, F., & Zhang, L. (2016). Large patch convolutional neural networks for the scene classification of high spatial resolution imagery. Journal of Applied Remote Sensing, 10, 025006.
   Web of Science ®Google Scholar
• Zhou, M., Shi, Z. W., & Ding, H. P. (2017). Aircraft classification in remote-sensing images using convolutional neural networks. Journal of Image and Graphics, 22, 702–708.
   Google Scholar
• Zhou, W., Shao, Z., & Cheng, Q. (2016). Deep feature representations for high-resolution remote sensing scene classification. 2016 4th International Workshop on Earth Observation and Remote Sensing Applications (EORSA) (pp. 338–342), Guangzhou, China.
   Google Scholar
• Zhu, H., Chen, X., Dai, W., Fu, K., Ye, Q., & Jiao, J. (2015). Orientation robust object detection in aerial images using deep convolutional neural network. IEEE International Conference on Image Processing (pp. 3735–3739), Quebec City, QC.
   Google Scholar
• Zhu, Q., Zhong, Y., Liu, Y., Zhang, L., & Li, D. (2018). A deep-local-global feature fusion framework for high spatial resolution imagery scene classification. Remote Sensing, 10, 568.
   Web of Science ®Google Scholar
• Zhu, X. X., Tuia, D., Mou, L. C., et al. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5, 8–36.
   Web of Science ®Google Scholar
• Zou, Q., Ni, L., Zhang, T., & Wang, Q. (2015). Deep learning based feature selection for remote sensing scene classification. IEEE Geoscience & Remote Sensing Letters, 12, 2321–2325.
   Web of Science ®Google Scholar