References
- Antonio-Javier, G., P. Antonio, and G. Pablo. 2018. “Automatic Ship Classification from Optical Aerial Images with Convolutional Neural Networks.” Remote Sensing 10 (4): 511. doi:https://doi.org/10.3390/rs10040511.
- Bi, Q., K. Qin, H. Zhang, J. Xie, Z. Li, and K. Xu. 2019. “APDCNet: Attention Pooling-based Convolutional Neural Network for Aerial Scene Classification.” IEEE Geoscience and Remote Sensing Letters 17 (9): 1603–1607. doi:https://doi.org/10.1109/LGRS.2019.2949930.
- Bi, Q., K. Qin, H. Zhang, Z. Li, K. Xu, et al. 2020b. “RADC-Net: A Residual Attention Based Convolution Network for Aerial Scene Classification.” Neurocomputing 377 :345–359. doi:https://doi.org/10.1016/j.neucom.2019.11.068.
- Bi, Q., K. Qin, Z. Li, H. Zhang, and K. Xu. 2019. “Multiple Instance Dense Connected Convolution Neural Network for Aerial Image Scene Classification.” In 2019 IEEE International Conference on Image Processing:2501-2505. Taipei, China. doi:https://doi.org/10.1109/ICIP.2019.8803322.
- Bi, Q., K. Qin, Z. Li, H. Zhang, K. Xu, G.-S. Xia, et al. 2020a. “A Multiple-Instance Densely-Connected ConvNet for Aerial Scene Classification.” IEEE Transactions on Image Processing 29 :4911–4926. doi:https://doi.org/10.1109/TIP.2020.2975718.
- Cao, R., L. Fang, T. Lu, N. He, et al. 2020. “Self-Attention-Based Deep Feature Fusion for Remote Sensing Scene Classification.” IEEE Geoscience and Remote Sensing Letters 18 (1): 43–47. doi:https://doi.org/10.1109/LGRS.2020.2968550.
- Castelluccio, M., G. Poggi, C. Sansone, and L. Verdoliva. 2015. “Land Use Classification in Remote Sensing Images by Convolutional Neural Networks.” Acta Ecologica Sinica 28 (2): 627–635. doi:https://doi.org/10.1016/S1872-2032(08)60029-3.
- Chaib, S., H. Liu, Y. Gu, and H. Yao. 2017. “”Deep Feature Fusion for VHR Remote Sensing Scene Classification.” IEEE Transactions on Geoscience and Remote Sensing 55 (8): 4775–4784. doi:https://doi.org/10.1109/TGRS.2017.2700322.
- Cheng, G., C. Yang, X. Yao, L. Guo, and J. Han. 2018. “When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs.” IEEE Transactions on Geoscience and Remote Sensing 56 (5): 2811–2821. doi:https://doi.org/10.1109/TGRS.2017.2783902.
- Cheng, G., J. Han, and X. Lu. 2017. “Remote Sensing Image Scene Classification: Benchmark and State of the Art.” Proceedings of the IEEE 105:1865–1883. doi:https://doi.org/10.1109/JPROC.2017.2675998.
- Cheng, G., X. Xie, J. Han, L. Guo, G.-S. Xia, et al. 2020. “Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 :3735–3756. doi:https://doi.org/10.1109/JSTARS.2020.3005403.
- Cheng, G., Z. Li, X. Yao, L. Guo, and Z. Wei. 2017. “Remote Sensing Image Scene Classification Using Bag of Convolutional Features.” IEEE Geoscience and Remote Sensing Letters 14 (10): 1735–1739. doi:https://doi.org/10.1109/LGRS.2017.2731997.
- Dalal, N., and B. Triggs. 2005. “Histograms of Oriented Gradients for Human Detection.” In Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA. 886–893. doi:https://doi.org/10.1109/CVPR.2005.177.
- Douze, M., J. Revaud, C. Schmid, and H. Jegou. 2013. “Stable Hyper-pooling and Query Expansion for Event Detection.” In IEEE International Conference on Computer Vision:1825-1832. Sydney, NSW, Australia. doi:https://doi.org/10.1109/ICCV.2013.229.
- Han, X., Y. Zhong, L. Cao, and L. Zhang. 2017. “Pre-trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification.” Remote Sensing 9 (8): 848. doi:https://doi.org/10.3390/rs9080848.
- He, K., X. Zhang, and S. Ren 2016. “Deep Residual Learning for Image Recognition.” In 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA. 770–778. doi:https://doi.org/10.1109/CVPR.2016.90.
- He, N., L. Fang, S. Li, A. Plaza, and J. Plaza. 2018. “Remote Sensing Scene Classification Using Multilayer Stacked Covariance Pooling.” IEEE Transactions on Geoscience and Remote Sensing 56 (12): 6899–6910. doi:https://doi.org/10.1109/TGRS.2018.2845668.
- Hu, F., G.-S. Xia, J. Hu, and L. Zhang. 2015. “Transferring Deep Convolutional Neural Networks for the Scene Classification of High-resolution Remote Sensing Imagery.” Remote Sensing 7 (11): 14680–14707. doi:https://doi.org/10.3390/rs71114680.
- Hu, J., L. Shen, and G. Sun. 2018. “Squeeze-and-excitation Networks.” In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition:7132-7141. doi:https://doi.org/10.1109/CVPR.2018.00745.
- Huang, G., Z. Liu, L. Van der Maaten, et al. 2017. “Densely Connected Convolutional Networks.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700–4708. Salt Lake City, UT, USA. doi:https://doi.org/10.1109/CVPR.2017.243.
- Jetley, S., N. A. Lord, N. Lee, and P. H. Torr. 2018. “Learn to Pay Attention.” In 2018 International Conference on Learning Representations:1–14. Vancouver, BC, Canada.
- Jgou, H., M. Douze, C. Schmid, and P. Prez. 2010. “Aggregating Local Descriptors into a Compact Image Representation.” In 2010 IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA. 3304–3311. doi:https://doi.org/10.1109/CVPR.2010.5540039.
- Kalantidis, Y., C. Mellina, and S. Osindero. 2016. “Cross-dimensional Weighting for Aggregated Deep Convolutional Features.” In European Conference on Computer Vision, Amsterdam, Netherlands. 685–701. doi:https://doi.org/10.1007/978-3-319-46604-0_48.
- Krizhevsky, A., I. Sutskever, and G. Hinton. 2012. “ImageNet Classification with Deep Convolutional Neural Networks.” Advances in Neural Information Processing Systems 25 (2): 1097–1105. doi:https://doi.org/10.1145/3065386.
- Larochelle, H., and G. E. Hinton. 2010. “Learning to Combine Foveal Glimpses with a Third-order Boltzmann Machine.” In NIPS’10: Proceedings of the 23rd International Conference on Neural Information Processing Systems 1:1243–1251.
- Lazebnik, S., C. Schmid, and J. Ponce. 2006. “Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories.” In 2006 IEEE Computer Society on Computer Vision and Pattern Recognition, 2169–2178. doi:https://doi.org/10.1109/CVPR.2006.68.
- Li, B., W. Su, H. Wu, R. Li, W. Zhang, W. Qin, S. Zhang, et al. 2019. “Aggregated Deep Fisher Feature for VHR Remote Sensing Scene Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (9): 3508–3523. doi:https://doi.org/10.1109/JSTARS.2019.2934165.
- Li, E., J. Xia, P. Du, C. Lin, and A. Samat. 2017. “Integrating Multilayer Features of Convolutional Neural Networks for Remote Sensing Scene Classification.” IEEE Transactions on Geoscience & Remote Sensing 55 (10): 5653–5665. doi:https://doi.org/10.1109/TGRS.2017.2711275.
- Li, Q., Q. Peng, and C. Yan. 2017. “Multiple VLAD Encoding of CNNs for Image Classification.” Computing in Science & Engineering 20 (2): 52–63. doi:https://doi.org/10.1109/MCSE.2018.108164530.
- Liang, Y., S. T. Monteiro, and E. S. Saber. 2016. “Transfer Learning for High Resolution Aerial Image Classification.” 2016 IEEE Applied Imagery Pattern Recognition Workshop:1–8. doi:https://doi.org/10.1109/AIPR.2016.8010600.
- Lin, R., J. Xiao, and J. Fan. 2018. “Nextvlad: An Efficient Neural Network to Aggregate Frame-level Features for Large-scale Video Classification.” In European Conference on Computer Vision, Munich, Germany. 206–218. doi:https://doi.org/10.1007/978-3-030-11018-5_19.
- Liu, B., J. Meng, W.-Y. Xie, S. Shao, Y. Li, Y. Wang, et al. 2019. “Weighted Spatial Pyramid Matching Collaborative Representation for Remote-sensing-image Scene Classification.” Remote Sensing 11 (5): 518. doi:https://doi.org/10.3390/rs11050518.
- Liu, B.-D., W.-Y. Xie, J. Meng, Y. Li, and Y. Wang. 2018. “Hybrid Collaborative Representation for Remote-sensing Image Scene Classification.” Remote Sensing 10 (2): 1934. doi:https://doi.org/10.3390/rs10121934.
- Liu, Y., Y. Liu, and L. Ding. 2018. “Scene Classification Based on Two-stage Deep Feature Fusion.” IEEE Geoscience and Remote Sensing Letters 15 (2): 183–186. doi:https://doi.org/10.1109/LGRS.2017.2779469.
- Lowe, D. G. 2004. “Distinctive Image Features from Scale-invariant Keypoints.” International Journal of Computer Vision 60 (2): 91–110. doi:https://doi.org/10.1023/B:VISI.0000029664.99615.94.
- Lu, X., H. Sun, and X. Zheng. 2019. “A Feature Aggregation Convolutional Neural Network for Remote Sensing Scene Classification.” IEEE Transactions on Geoscience and Remote Sensing 57 (10): 7894–7906. doi:https://doi.org/10.1109/TGRS.2019.2917161.
- Lv, Y., X. Zhang, W. Xiong, Y. Cui, M. Cai, et al. 2019. “An End-to-end Local-global-fusion Feature Extraction Network for Remote Sensing Image Scene Classification.” Remote Sensing 11 (24): 3006. doi:https://doi.org/10.3390/rs11243006.
- Ma, L., Y. Liu, X. Zhang, Y. Ye, G. Yin, B. A. Johnson, et al. 2019. “”Deep Learning in Remote Sensing Applications: A Meta-analysis and Review.”.” ISPRS Journal of Photogrammetry and Remote Sensing 152 :166–177. doi:https://doi.org/10.1016/j.isprsjprs.2019.04.015.
- Nogueira, K., O. A. Penatti, and J. A. Dos Santos. 2017. “Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification.” Pattern Recognition 61: 539–556. doi:https://doi.org/10.1016/j.patcog.2016.07.001.
- Ojala, T., M. Pietikainen, and T. Maenpaa. 2002. “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns.” IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (7): 971–987. doi:https://doi.org/10.1109/TPAMI.2002.1017623.
- Oliva, A., and A. Torralba. 2001. “Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope.” International Journal of Computer Vision 42 (3): 145–175. doi:https://doi.org/10.1023/A:1011139631724.
- Park, J., S. Woo, J.-Y. Lee, and I. S. Kweon. 2018. “BAM: Bottleneck Attention Module.” arXiv:1807.06514.
- Penatti, O. A., K. Nogueira, and J. A. dos Santos. 2015. “Do Deep Features Generalize from Everyday Objects to Remote Sensing and Aerial Scenes Domains?” In 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA. 44–51. doi:https://doi.org/10.1109/CVPRW.2015.7301382.
- Perronnin, F., J. Snchez, and T. Mensink. 2010. “Improving the Fisher Kernel for Large-scale Image Classification.” In European Conference on Computer Vision, Heraklion, Crete, Greece. 143–156. doi:https://doi.org/10.1007/978-3-642-15561-1_11.
- Rao, M., F. Khan, J. Weijer, M. Molinier, and J. Laaksonen. 2017. “Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 138: 74–85. doi:https://doi.org/10.1016/j.isprsjprs.2018.01.023.
- Simonyan, K., and A. Zisserman. 2014. “Very Deep Convolutional Networks for Large-scale Image Recognition.” Computer Science arXiv:1409.1556. 1-14.
- Sun, H., S. Li, X. Zheng, and X. Lu. 2019. “Remote Sensing Scene Classification by Gated Bidirectional Network.” IEEE Transactions on Geoscience and Remote Sensing 58 (1): 82–96. doi:https://doi.org/10.1109/TGRS.2019.2931801.
- Swain, M. J., and D. H. Ballard. 1991. “Color Indexing.” International Journal of Computer Vision 7 (1): 11–32. doi:https://doi.org/10.1007/BF00130487.
- Szegedy, C., W. Liu, Y. Jia, P. Sermanet, and A. Rabinovich 2015. “Going Deeper with Convolutions.” In 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA. 1–12. doi:https://doi.org/10.1109/CVPR.2015.7298594.
- Wang, B., X. Lu, X. Zheng, and X. Li. 2019a. “Semantic Descriptions of High-resolution Remote Sensing Images.” IEEE Geoscience and Remote Sensing Letters 16 (8): 1274–1278. doi:https://doi.org/10.1109/LGRS.2019.2893772.
- Wang, F., M. Jiang, C. Qian, S. Yang, and X. Tang 2017a. “Residual Attention Network for Image Classification.” In 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA. 6450–6458. doi:https://doi.org/10.1109/CVPR.2017.683.
- Wang, G., B. Fan, S. Xiang, and C. Pan. 2017b. “Aggregating Rich Hierarchical Features for Scene Classification in Remote Sensing Imagery.” IEEE Journal of Selected Topics in Applied Earth 10 (9): 4104–4115. doi:https://doi.org/10.1109/JSTARS.2017.2705419.
- Wang, Q., S. Liu, J. Chanussot, and X. Li. 2019b. “Scene Classification with Recurrent Attention of VHR Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 57 (2): 1155–1167. doi:https://doi.org/10.1109/TGRS.2018.2864987.
- Woo, S., J. Park, J.-Y. Lee, and I. S. Kweon. 2018. “CBAM: Convolutional Block Attention Module.” In 2018 European Conference on Computer Vision, Munich, Germany. 3–19. doi:https://doi.org/10.1007/978-3-030-01234-2_1.
- Xia, G., J. Hu, F. Hu, B. Shi, X. Bai, Y. Zhong, L. Zhang, et al. 2017. “AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification.” IEEE Transaction Geoscience and Remote Sensing 55 (7): 3965–3981. doi:https://doi.org/10.1109/TGRS.2017.2685945.
- Xiu, S., Y. Wen, H. Yuan, C. Xiao, W. Zhan, X. Zou, C. Zhou, and S. Chhattan Shah. 2019. “A Multi-Feature and Multi-Level Matching Algorithm Using Aerial Image and AIS for Vessel Identification.” Sensors 19 (6): 1–19. doi:https://doi.org/10.3390/s19061317.
- Yang, S., and D. Ramanan. 2015. “Multi-scale Recognition with DAG-CNNs.” In 2015 IEEE International Conference on Computer Vision, Santiago, Chile. 1215–1223. doi:https://doi.org/10.1109/ICCV.2015.144.
- Yang, Y., and S. Newsam. 2010. “Bag-of-visual-words and Spatial Extensions for Land-use Classification.” In 18th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems, San Jose, CA, USA. 270–279. doi:https://doi.org/10.1145/1869790.1869829.
- Yin, Q., R. Zhang, X. L. Shao, W. Anggono, and W. Anggono. 2019. “CNN and RNN Mixed Model for Image Classification.” MATEC Web of Conferences 277 (4): 02001. doi:https://doi.org/10.1051/matecconf/201927702001.
- Yu, Y., and F. Liu. 2018. “Aerial Scene Classification via Multilevel Fusion Based on Deep Convolutional Neural Networks.” IEEE Geoscience and Remote Sensing Letters 15 (2): 287–291. doi:https://doi.org/10.1109/LGRS.2017.2786241.
- Yuan, B., S. Li, and N. Li. 2018. “Multiscale Deep Features Learning for Land-use Scene Recognition.” Journal of Applied Remote Sensing 12 (1): 1. doi:https://doi.org/10.1117/1.JRS.12.015010.
- Zhang, L., L. Zhang, and B. Du. 2016. “Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art.” IEEE Geoscience and Remote Sensing 4 (2): 22–40. doi:https://doi.org/10.1109/MGRS.2016.2540798.
- Zhang, X., and S. Du. 2015. “A Linear Dirichlet Mixture Model for Decomposing Scenes: Application to Analyzing Urban Functional Zonings.” Remote Sensing of Environment 169: 37–49. doi:https://doi.org/10.1016/j.rse.2015.07.017.
- Zhao, F., X. Mu, Z. Yang, Z. Yi, et al. 2019. “A Novel Two-stage Scene Classification Model Based on Feature Variable Significance in High-resolution Remote Sensing.” Geocarto International 35 (14): 1603–1614. doi:https://doi.org/10.1080/10106049.2019.1583772.
- Zhao, W., and S. Du. 2016. “Scene Classification Using Multi-scale Deeply Described Visual Words.” International Journal of Remote Sensing 37 (17): 4119–4131. doi:https://doi.org/10.1080/01431161.2016.1207266.
- Zheng, X., Y. Yuan, and X. Lu. 2019. “A Deep Scene Representation for Aerial Scene Classification.” IEEE Transactions on Geoscience and Remote Sensing 57 (7): 4799–4809. doi:https://doi.org/10.1109/TGRS.2019.2893115.
- Zou, Q., L. Ni, T. Zhang, and Q. Wang. 2015. “Deep Learning Based Feature Selection for Remote Sensing Scene Classification.” IEEE Geoscience and Remote Sensing Letters 12 (11): 2321–2325. doi:https://doi.org/10.1109/LGRS.2015.2475299.