Semantic interleaving global channel attention for multilabel remote sensing image classification

References

• Alshehri, A., Y. Bazi, N. Ammour, H. Almubarak, and N. Alajlan. 2019. “Deep Attention Neural Network for Multi-Label Classification in Unmanned Aerial Vehicle Imagery.” Institute of Electrical and Electronics Engineers Access 7:119873–119880. https://doi.org/10.1109/ACCESS.2019.2936616.
   Google Scholar
• Bazi, Y. 2019. “Two-Branch Neural Network for Learning Multi-Label Classification in UAV Imagery.” In 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan, 2443–2446. https://doi.org/10.1109/igarss.2019.8898895.
   Google Scholar
• Chaudhuri, B., B. Demir, S. Chaudhuri, and L. Bruzzone. 2018. “Multilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method.” IEEE Transactions on Geoscience and Remote Sensing 56 (2): 1144–1158. https://doi.org/10.1109/TGRS.2017.2760909.
   Web of Science ®Google Scholar
• Chen, Z.-M., X.-S. Wei, P. Wang, and Y. Guo. 2019. “Multi-Label Image Recognition with Graph Convolutional Networks.” In 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 5177–5186. https://doi.org/10.1109/cvpr.2019.00532.
   Google Scholar
• Chenyu, L., B. Zhang, D. Hong, J. Yao, and J. Chanussot. 2023. “LRR-Net: An Interpretable Deep Unfolding Network for Hyperspectral Anomaly Detection.” IEEE Transactions on Geoscience and Remote Sensing 61:1–12. https://doi.org/10.1109/TGRS.2023.3279834.
   Web of Science ®Google Scholar
• Dai, O. E., B. Demir, B. Sankur, and L. Bruzzone. 2018. “A Novel System for Content-Based Retrieval of Single and Multi-Label High-Dimensional Remote Sensing Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (7): 2473–2490. https://doi.org/10.1109/JSTARS.2018.2832985.
   Web of Science ®Google Scholar
• Donghang, Y., H. Guo, X. Qing, L. Jun, C. Zhao, and Y. Lin. 2020. “Hierarchical Attention and Bilinear Fusion for Remote Sensing Image Scene Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13:6372–6383. https://doi.org/10.1109/JSTARS.2020.3030257.
   Web of Science ®Google Scholar
• Fernandez-Beltran, R., A. Plaza, J. Plaza, and F. Pla. 2018. “Hyperspectral Unmixing Based on Dual-Depth Sparse Probabilistic Latent Semantic Analysis.” IEEE Transactions on Geoscience & Remote Sensing 56 (11): 6344–6360. https://doi.org/10.1109/TGRS.2018.2837150.
   Web of Science ®Google Scholar
• Fernandez-Beltran, R., A. Plaza, J. Plaza, and F. Pla. 2020. “Endmember Extraction from Hyperspectral Imagery Based on Probabilistic Tensor Moments.” IEEE Geoscience & Remote Sensing Letters 17 (12): 2120–2124. https://doi.org/10.1109/LGRS.2019.2963114.
   Web of Science ®Google Scholar
• Gao, L., B. Zhao, X. Jia, W. Liao, and B. Zhang. 2017. “Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification.” Remote Sensing 9 (6): 548. https://doi.org/10.3390/RS9060548.
   Web of Science ®Google Scholar
• Gardner, D., and D. Nichols. 2017. Multi-label Classification of Satellite Images with Deep Learning. Technical Report. Palo Alto, CA: Stanford University. http://vision.stanford.edu/teaching/cs231n/reports/2017/pdfs/908.pdf.
   Google Scholar
• Geng, J., J. Fan, H. Wang, M. Xiaorui, L. Baoming, and F. Chen. 2015. “High-resolution SAR image classification via deep convolutional autoencoders.” IEEE Geoscience and Remote Sensing Letters 12 (11): 2351–2355. https://doi.org/10.1109/LGRS.2015.2478256.
   Web of Science ®Google Scholar
• Hamilton, W. L., Z. Ying, and J. Leskovec. 2017. “Inductive Representation Learning on Large Graphs.” In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 1024–1034. Long Beach, CA, USA.
   Google Scholar
• Hong, D., L. Gao, J. Yao, B. Zhang, A. Plaza, and J. Chanussot. 2021. “Graph Convolutional Networks for Hyperspectral Image Classification.” IEEE Transactions on Geoscience & Remote Sensing 59 (7): 5966–5978. https://doi.org/10.1109/TGRS.2020.3015157.
   Web of Science ®Google Scholar
• Hong, D., L. Gao, N. Yokoya, J. Yao, J. Chanussot, Q. Du, and B. Zhang. 2021. “More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification.” IEEE Transactions on Geoscience & Remote Sensing 59 (5): 4340–4354. https://doi.org/10.1109/TGRS.2020.3016820.
   Web of Science ®Google Scholar
• Hong, D., N. Yokoya, J. Chanussot, X. Jian, and X. Xiang Zhu. 2019. “Learning to Propagate Labels on Graphs: An Iterative Multitask Regression Framework for Semi-Supervised Hyperspectral Dimensionality Reduction.” Isprs Journal of Photogrammetry & Remote Sensing 158:35–49. https://doi.org/10.1016/j.isprsjprs.2019.09.008.
   PubMed Web of Science ®Google Scholar
• Hong, D., N. Yokoya, J. Chanussot, and X. Xiang Zhu. 2018. “An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing.” IEEE Transactions on Image Processing 28 (4): 1923–1938. https://doi.org/10.1109/TIP.2018.2878958.
   Web of Science ®Google Scholar
• Hong, D., N. Yokoya, G. Nan, J. Chanussot, and X. X. Zhu. 2019. “Learnable Manifold Alignment (LeMa): A Semi-Supervised Cross-Modality Learning Framework for Land Cover and Land Use Classification.” Isprs Journal of Photogrammetry & Remote Sensing 147:193–205. https://doi.org/10.1016/j.isprsjprs.2018.10.006.
   PubMed Web of Science ®Google Scholar
• Hua, Y., L. Mou, and X. Xiang Zhu. 2019a. “Label Relation Inference for Multi-Label Aerial Image Classification.” In 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan, 5244–5247. https://doi.org/10.1109/igarss.2019.8898934.
   Google Scholar
• Hua, Y., L. Mou, and X. Xiang Zhu. 2019b. “Recurrently Exploring Class-Wise Attention in a Hybrid Convolutional and Bidirectional LSTM Network for Multi-Label Aerial Image Classification.” Isprs Journal of Photogrammetry & Remote Sensing 149:188–199. https://doi.org/10.1016/j.isprsjprs.2019.01.015.
   PubMed Web of Science ®Google Scholar
• Hua, Y., L. Mou, and X. Xiang Zhu. 2020. “Relation Network for Multilabel Aerial Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 58 (7): 4558–4572. https://doi.org/10.1109/TGRS.2019.2963364.
   Web of Science ®Google Scholar
• Huang, R., F. Zheng, and W. Huang. 2021. “Multilabel Remote Sensing Image Annotation with Multiscale Attention and Label Correlation.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14:6951–6961. https://doi.org/10.1109/JSTARS.2021.3091134.
   Web of Science ®Google Scholar
• Hu, J., L. Shen, and G. Sun. 2018. “Squeeze-and-Excitation Networks.” In 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, 7132–7141. https://doi.org/10.1109/cvpr.2018.00745.
   Google Scholar
• Jiang, P.-T., C.-B. Zhang, Q. Hou, M.-M. Cheng, and Y. Wei. 2021. “Layercam: Exploring Hierarchical Class Activation Maps for Localization.” IEEE Transactions on Image Processing 30:5875–5888. https://doi.org/10.1109/TIP.2021.3089943.
   PubMed Web of Science ®Google Scholar
• Ju, Y., J. Dong, and S. Chen. 2021. “Recovering Surface Normal and Arbitrary Images: A Dual Regression Network for Photometric Stereo.” IEEE Transactions on Image Processing 30:3676–3690. https://doi.org/10.1109/TIP.2021.3064230.
   PubMed Web of Science ®Google Scholar
• Ju, Y., Y. Peng, M. Jian, F. Gao, and J. Dong. 2022. “Learning Conditional Photometric Stereo with High-Resolution Features.” Computational Visual Media 8 (1): 105–118. https://doi.org/10.1007/S41095-021-0223-Y.
   Google Scholar
• Kaiming, H., X. Zhang, S. Ren, and J. Sun. 2016. “Deep Residual Learning for Image Recognition.” In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 770–778. https://doi.org/10.1109/cvpr.2016.90.
   Google Scholar
• Kang, J., R. Fernandez-Beltran, D. Hong, J. Chanussot, and A. Plaza. 2020. “Graph Relation Network: Modeling Relations Between Scenes for Multilabel Remote-Sensing Image Classification and Retrieval.” IEEE Transactions on Geoscience and Remote Sensing 59 (5): 4355–4369. https://doi.org/10.1109/TGRS.2020.3016020.
   Web of Science ®Google Scholar
• Kipf, T. N., and M. Welling. 2016. “Semi-Supervised Classification with Graph Convolutional Networks.” CoRr Abs/1609.02907. http://arxiv.org/abs/1609.02907.
   Google Scholar
• Koda, S., A. Zeggada, F. Melgani, and R. Nishii. 2018. “Spatial and Structured SVM for Multilabel Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 56 (10): 5948–5960. https://doi.org/10.1109/TGRS.2018.2828862.
   Web of Science ®Google Scholar
• Liang, J., Y. Deng, and D. Zeng. 2020. “A Deep Neural Network Combined CNN and GCN for Remote Sensing Scene Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13:4325–4338. https://doi.org/10.1109/JSTARS.2020.3011333.
   Web of Science ®Google Scholar
• Li, P., P. Chen, and D. Zhang. 2022. “Cross-Modal Feature Representation Learning and Label Graph Mining in a Residual Multi-Attentional CNN-LSTM Network for Multi-Label Aerial Scene Classification.” Remote Sensing 14 (10): 2424. https://doi.org/10.3390/RS14102424.
   Web of Science ®Google Scholar
• Li, Y., R. Chen, Y. Zhang, M. Zhang, and L. Chen. 2020. “Multi-Label Remote Sensing Image Scene Classification by Combining a Convolutional Neural Network and a Graph Neural Network.” Remote Sensing 12 (23): 4003. https://doi.org/10.3390/RS12234003.
   Web of Science ®Google Scholar
• Li, F., R. Feng, W. Han, and L. Wang. 2020. “An Augmentation Attention Mechanism for High-Spatial-Resolution Remote Sensing Image Scene Classification.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 13:3862–3878. https://doi.org/10.1109/JSTARS.2020.3006241.
   Web of Science ®Google Scholar
• Lin, T.-Y., S. Belongie, and J. Hays. 2013. “Cross-View Image Geolocalization.” In 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA, 891–898. https://doi.org/10.1109/cvpr.2013.120.
   Google Scholar
• Lin, D., J. Lin, Z. J. W. Liang Zhao, Z. Chen, and Z. Chen. 2021. “Multilabel Aerial Image Classification with a Concept Attention Graph Neural Network.” IEEE Transactions on Geoscience and Remote Sensing 60:1–12. https://doi.org/10.1109/TGRS.2020.3041461.
   Web of Science ®Google Scholar
• Liu, Y., Z. Shao, and N. Hoffmann. 2021. “Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions.” arXiv preprint arXiv:2112.05561.
   Google Scholar
• Ma, A., Y. Wan, Y. Zhong, J. Wang, and L. Zhang. 2021. “SceneNet: Remote Sensing Scene Classification Deep Learning Network Using Multi-Objective Neural Evolution Architecture Search.” ISPRS Journal of Photogrammetry and Remote Sensing 172:171–188. https://doi.org/10.1016/j.isprsjprs.2020.11.025.
   Web of Science ®Google Scholar
• Movshovitz-Attias, Y., Y. Qian, M. C. Stumpe, V. D. Shet, S. Arnoud, and L. Yatziv. 2015. “Ontological Supervision for Fine Grained Classification of Street View Storefronts.” In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 1693–1702. https://doi.org/10.1109/cvpr.2015.7298778.
   Google Scholar
• Ni, J., W. Jianhuang, H. Wang, J. Tong, Z. Chen, K. K. Wong, and D. Abbott. 2020. “Global Channel Attention Networks for Intracranial Vessel Segmentation.” Computers in Biology and Medicine 118:103639. https://doi.org/10.1016/J.COMPBIOMED.2020.103639.
   PubMed Web of Science ®Google Scholar
• Ouyang, S., X. Jiahui, W. Chen, Y. Dong, L. Xianju, and L. Jun. 2022. “A Fine-Grained Genetic Landform Classification Network Based on Multimodal Feature Extraction and Regional Geological Context.” IEEE Transactions on Geoscience and Remote Sensing 60:1–14. https://doi.org/10.1109/TGRS.2022.3203606.
   Web of Science ®Google Scholar
• Rasti, B., D. Hong, R. Hang, P. Ghamisi, X. Kang, J. Chanussot, and J. Atli Benediktsson. 2020. “Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep: Overview and Toolbox.” IEEE Geoscience and Remote Sensing Magazine 8 (4): 60–88. https://doi.org/10.1109/MGRS.2020.2979764.
   Web of Science ®Google Scholar
• Simonyan, K., A. Zisserman, and A. Zisserman. 2014. “Learning Local Feature Descriptors Using Convex Optimisation.” IEEE Transactions on Pattern Analysis & Machine Intelligence 36 (8): 1573–1585. arXiv preprint arXiv:1409.1556. https://doi.org/10.1109/TPAMI.2014.2301163.
   PubMed Web of Science ®Google Scholar
• Speer, R., J. Chin, and C. Havasi. 2017. “ConceptNet 5.5: An Open Multilingual Graph of General Knowledge.” In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, California, USA, 4444–4451. https://doi.org/10.1609/aaai.v31i1.11164.
   Google Scholar
• Stivaktakis, R., G. Tsagkatakis, and P. Tsakalides. 2019. “Deep learning for multilabel land cover scene categorization using data augmentation.” IEEE Geoscience and Remote Sensing Letters 16 (7): 1031–1035. https://doi.org/10.1109/LGRS.2019.2893306.
   Web of Science ®Google Scholar
• Szegedy, C., W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. 2015. “Going Deeper with Convolutions.” In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015, Boston, MA, USA, 1–9. https://doi.org/10.1109/cvpr.2015.7298594.
   Google Scholar
• Tan, Q., Y. Liu, X. Chen, and Y. Guoxian. 2017. “Multi-Label Classification Based on Low Rank Representation for Image Annotation.” Remote Sensing 9 (2): 109. https://doi.org/10.3390/RS9020109.
   Web of Science ®Google Scholar
• Tan, X., Z. Xiao, J. Zhu, Q. Wan, K. Wang, and L. Deren. 2022. “Transformer-Driven Semantic Relation Inference for Multilabel Classification of High-Resolution Remote Sensing Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15:1884–1901. https://doi.org/10.1109/JSTARS.2022.3145042.
   Web of Science ®Google Scholar
• Tong, W., W. Chen, W. Han, L. Xianju, and L. Wang. 2020. “Channel-Attention-Based DenseNet Network for Remote Sensing Image Scene Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13:4121–4132. https://doi.org/10.1109/JSTARS.2020.3009352.
   Web of Science ®Google Scholar
• Van der Maaten, L., and G. Hinton. 2008. “Visualizing data using t-SNE.” Journal of Machine Learning Research 9 (11): 2579–2605.
   Google Scholar
• Veličković, P., G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio. 2017. “Graph Attention Networks.” arXiv preprint arXiv:1710.10903.
   Google Scholar
• Vucetic, B., and J. Yuan. 2012. Turbo Codes: Principles and Applications. New York, NY: Springer.
   Google Scholar
• Wang, X., X. Xiong, and C. Ning. 2019. “Multi-label remote sensing scene classification using multi-bag integration.” Institute of Electrical and Electronics Engineers Access 7:120399–120410. https://doi.org/10.1109/ACCESS.2019.2937188.
   Google Scholar
• Wang, J., Y. Yang, J. Mao, Z. Huang, C. Huang, and X. Wei 2016. “CNN-RNN: A Unified Framework for Multi-Label Image Classification.” In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)Las Vegas, NV, USA, 2285–2294. https://doi.org/10.1109/cvpr.2016.251.
   Google Scholar
• Yao, J., B. Zhang, L. Chenyu, D. Hong, and J. Chanussot. 2023. “Extended Vision Transformer (ExVit) for Land Use and Land Cover Classification: A Multimodal Deep Learning Framework.” IEEE Transactions on Geoscience and Remote Sensing 61:1–15. https://doi.org/10.1109/TGRS.2023.3284671.
   Web of Science ®Google Scholar
• Zeggada, A., S. Benbraika, F. Melgani, and Z. Mokhtari. 2018. “Multilabel Conditional Random Field Classification for UAV Images.” IEEE Geoscience and Remote Sensing Letters 15 (3): 399–403. https://doi.org/10.1109/LGRS.2018.2790426.
   Web of Science ®Google Scholar
• Zeggada, A., F. Melgani, and Y. Bazi. 2017. “A Deep Learning Approach to UAV Image Multilabeling.” IEEE Geoscience and Remote Sensing Letters 14 (5): 694–698. https://doi.org/10.1109/LGRS.2017.2671922.
   Web of Science ®Google Scholar
• Zhang, P.-F., W. Hao-Yi, and X. Xin-Shun. 2017. “A Dual-CNN Model for Multi-Label Classification by Leveraging Co-Occurrence Dependencies Between Labels.” In Advances in Multimedia Information Processing–PCM 2017: 18th Pacific-Rim Conference on Multimedia, Harbin, China, September 28-29, 2017, Revised Selected Papers, Part I 18, 315–324. Springer. https://doi.org/10.1007/978-3-319-77380-3_30.
   Google Scholar
• Zhang, M.-L., and Z.-H. Zhou. 2007. “ML-KNN: A Lazy Learning Approach to Multi-Label Learning.” Pattern Recognition 40 (7): 2038–2048. https://doi.org/10.1016/J.PATCOG.2006.12.019.
   Web of Science ®Google Scholar
• Zhang, M.-L., and Z.-H. Zhou. 2013. “A Review on Multi-Label Learning Algorithms.” IEEE Transactions on Knowledge and Data Engineering 26 (8): 1819–1837. https://doi.org/10.1109/TKDE.2013.39.
   Web of Science ®Google Scholar
• Zhao, K., Y. Liu, S. Hao, L. Shaoxing, H. Liu, and L. Zhou. 2021. “Bounding Boxes are All We Need: Street View Image Classification via Context Encoding of Detected Buildings.” IEEE Transactions on Geoscience and Remote Sensing 60:1–11. https://doi.org/10.1109/TGRS.2020.3042202.
   Web of Science ®Google Scholar
• Zhao, W., and D. Shihong. 2016. “Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach.” IEEE Transactions on Geoscience and Remote Sensing 54 (8): 4544–4554. https://doi.org/10.1109/TGRS.2016.2543748.
   Web of Science ®Google Scholar
• Zhou, G., X. Jiahui, W. Chen, L. Xianju, L. Jun, and L. Wang. 2023. “Deep Feature Enhancement Method for Land Cover with Irregular and Sparse Spatial Distribution Features: A Case Study on Open-Pit Mining.” IEEE Transactions on Geoscience and Remote Sensing 61:1–20. https://doi.org/10.1109/TGRS.2023.3336053.
   Web of Science ®Google Scholar
• Zhu, X. X., H. Jingliang, C. Qiu, Y. Shi, J. Kang, L. Mou, H. Bagheri, et al. 2019. “So2Sat LCZ42: A benchmark dataset for global local climate zones classification.” arXiv preprint arXiv:1912.12171
   Google Scholar
• Zhu, P., Y. Tan, L. Zhang, Y. Wang, J. Mei, H. Liu, and W. Mengfan. 2020. “Deep Learning for Multilabel Remote Sensing Image Annotation with Dual-Level Semantic Concepts.” IEEE Transactions on Geoscience and Remote Sensing 58 (6): 4047–4060. https://doi.org/10.1109/TGRS.2019.2960466.
   Web of Science ®Google Scholar