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Expert","award":["2022AH010095"],"award-info":[{"award-number":["2022AH010095"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Adding an attention module to the deep convolution semantic segmentation network has significantly enhanced the network performance. However, the existing channel attention module focusing on the channel dimension neglects the spatial relationship, causing location noise to transmit to the decoder. In addition, the spatial attention module exemplified by self-attention has a high training cost and challenges in execution efficiency, making it unsuitable to handle large-scale remote sensing data. We propose an efficient vector pooling attention (VPA) module for building the channel and spatial location relationship. The module can locate spatial information better by performing a unique vector average pooling in the vertical and horizontal dimensions of the feature maps. Furthermore, it can also learn the weights directly by using the adaptive local cross-channel interaction. Multiple weight learning ablation studies and comparison experiments with the classical attention modules were conducted by connecting the VPA module to a modified DeepLabV3 network using ResNet50 as the encoder. The results show that the mIoU of our network with the addition of an adaptive local cross-channel interaction VPA module increases by 3% compared to the standard network on the MO-CSSSD. The VPA-based semantic segmentation network can significantly improve precision efficiency compared with other conventional attention networks. Furthermore, the results on the WHU Building dataset present an improvement in IoU and F1-score by 1.69% and 0.97%, respectively. Our network raises the mIoU by 1.24% on the ISPRS Vaihingen dataset. The VPA module can also significantly improve the network\u2019s performance on small target segmentation.<\/jats:p>","DOI":"10.3390\/rs15081980","type":"journal-article","created":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T03:19:54Z","timestamp":1681096794000},"page":"1980","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Adaptive Local Cross-Channel Vector Pooling Attention Module for Semantic Segmentation of Remote Sensing Imagery"],"prefix":"10.3390","volume":"15","author":[{"given":"Xiaofeng","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Big Data and Information Engineering, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1077-0813","authenticated-orcid":false,"given":"Menglei","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Big Data and Information Engineering, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]},{"given":"Yan","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Applied Optimization, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9450-3415","authenticated-orcid":false,"given":"Wenxiang","family":"Jiang","sequence":"additional","affiliation":[{"name":"Institute of Applied Optimization, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]},{"given":"Mengyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Applied Optimization, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]},{"given":"Thomas","family":"Weise","sequence":"additional","affiliation":[{"name":"Institute of Applied Optimization, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]},{"given":"Ming","family":"Tan","sequence":"additional","affiliation":[{"name":"Institute of Applied Optimization, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]},{"given":"Lixiang","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Big Data and Information Engineering, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3806-3993","authenticated-orcid":false,"given":"Xinlu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Big Data and Information Engineering, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]},{"given":"Le","family":"Zou","sequence":"additional","affiliation":[{"name":"Department of Big Data and Information Engineering, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]},{"given":"Chen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Big Data and Information Engineering, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6010912","DOI":"10.1155\/2022\/6010912","article-title":"Research Contribution and Comprehensive Review towards the Semantic Segmentation of Aerial Images Using Deep Learning Techniques","volume":"2022","author":"Anilkumar","year":"2022","journal-title":"Secur. Commun. Netw."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"113058","DOI":"10.1016\/j.rse.2022.113058","article-title":"Cross-sensor domain adaptation for high spatial resolution urban land-cover mapping: From airborne to spaceborne imagery","volume":"277","author":"Wang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zhong, Y.F., Wang, J.J., and Ma, A.L. (2020, January 14\u201319). Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Virtual.","DOI":"10.1109\/CVPR42600.2020.00415"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1080\/01431160903439882","article-title":"Information fusion of aerial images and LIDAR data in urban areas: Vector-stacking, re-classification and post-processing approaches","volume":"32","author":"Huang","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2016, January 7\u201312). Fully Convolutional Networks for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_8","unstructured":"Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","unstructured":"Chen, L., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y.K., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Proceedings of the 15th European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., Wang, J., and Soc, I.C. (2019, January 16\u201320). Deep High-Resolution Representation Learning for Human Pose Estimation. Proceedings of the 32nd IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_13","unstructured":"Sun, K., Zhao, Y., Jiang, B., Cheng, T., Xiao, B., Liu, D., Mu, Y., Wang, X., Liu, W., and Wang, J. (2019). High-resolution representations for labeling pixels and regions. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J. (2018, January 20). UNet++: A Nested U-Net Architecture for Medical Image Segmentation. Proceedings of the Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_15","first-page":"768","article-title":"ANALYZING VISION AT THE COMPLEXITY LEVEL","volume":"14","author":"Tsotsos","year":"1991","journal-title":"Behav. Brain Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.cogsys.2012.02.002","article-title":"A Computational Perspective on Visual Attention","volume":"19\u201320","author":"Vikram","year":"2012","journal-title":"Cognit. Syst. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"11307","DOI":"10.1038\/s41598-020-67529-x","article-title":"Object detection based on an adaptive attention mechanism","volume":"10","author":"Li","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tian, Z., Zhan, R., Hu, J., Wang, W., He, Z., and Zhuang, Z. (2020). Generating Anchor Boxes Based on Attention Mechanism for Object Detection in Remote Sensing Images. Remote Sens., 12.","DOI":"10.3390\/rs12152416"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2359","DOI":"10.3233\/JIFS-211648","article-title":"An object detection network based on YOLOv4 and improved spatial attention mechanism","volume":"42","author":"Chen","year":"2022","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.comcom.2021.09.001","article-title":"Classification of flower image based on attention mechanism and multi-loss attention network","volume":"179","author":"Zhang","year":"2021","journal-title":"Comput. Commun."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"34325","DOI":"10.1007\/s11042-022-12792-5","article-title":"MSANet: Multi-scale attention networks for image classification","volume":"81","author":"Cao","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1653","DOI":"10.1049\/iet-ipr.2019.1462","article-title":"FuSENet: Fused squeeze-and-excitation network for spectral-spatial hyperspectral image classification","volume":"14","author":"Roy","year":"2020","journal-title":"Iet Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","article-title":"Attention mechanisms in computer vision: A survey","volume":"8","author":"Guo","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the 15th European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1109\/LGRS.2020.2988294","article-title":"SCAttNet: Semantic Segmentation Network With Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images","volume":"18","author":"Li","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., Lu, H., and Soc, I.C. (2019, January 16\u201320). Dual Attention Network for Scene Segmentation. Proceedings of the 32nd IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jin, Z., Liu, B., Chu, Q., and Yu, N. (2021, January 11\u201317). ISNet: Integrate Image-Level and Semantic-Level Context for Semantic Segmentation. Proceedings of the 18th IEEE\/CVF International Conference on Computer Vision (ICCV), Virtual.","DOI":"10.1109\/ICCV48922.2021.00710"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8287","DOI":"10.1109\/JSTARS.2021.3104382","article-title":"Light-Weight Semantic Segmentation Network for UAV Remote Sensing Images","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the 31st IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 14\u201319). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_31","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., and Houlsby, N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Chen, X., and Wang, J. (2020, January 23\u201328). Object-contextual representations for semantic segmentation. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK.","DOI":"10.1007\/978-3-030-58539-6_11"},{"key":"ref_33","first-page":"5078731","article-title":"Remote Sensing Image Semantic Segmentation Algorithm Based on Improved ENet Network","volume":"2021","author":"Wang","year":"2021","journal-title":"Sci. Program."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"014512","DOI":"10.1117\/1.JRS.15.014512","article-title":"Road extraction from satellite and aerial image using SE-Unet","volume":"15","author":"Sofla","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.egyr.2021.10.037","article-title":"Improved U-Net based insulator image segmentation method based on attention mechanism","volume":"7","author":"Han","year":"2021","journal-title":"Energy Rep."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Han, L., Zhao, Y., Lv, H., Zhang, Y., Liu, H., and Bi, G. (2022). Remote Sensing Image Denoising Based on Deep and Shallow Feature Fusion and Attention Mechanism. Remote Sens., 14.","DOI":"10.3390\/rs14051243"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, R.R., Tao, F., Liu, X.T., Na, J.M., Leng, H.J., Wu, J.J., and Zhou, T. (2022). RAANet: A Residual ASPP with Attention Framework for Semantic Segmentation of High-Resolution Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14133109"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5757","DOI":"10.1080\/01431161.2021.1986238","article-title":"Spatial-Coordinate Attention and Multi-Path Residual Block Based Oriented Object Detection in Remote Sensing Images","volume":"43","author":"Wang","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, Y., Si, Y., Tong, Z., He, L., Zhang, J., Luo, S., and Gong, Y. (2022). MQANet: Multi-Task Quadruple Attention Network of Multi-Object Semantic Segmentation from Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14246256"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhao, D., Wang, C., Gao, Y., Shi, Z., and Xie, F. (2022). Semantic Segmentation of Remote Sensing Image Based on Regional Self-Attention Mechanism. IEEE Geosci. Remote Sens. Lett., 19.","DOI":"10.1109\/LGRS.2021.3071624"},{"key":"ref_41","first-page":"6512305","article-title":"Multilevel Feature Fusion and Attention Network for High-Resolution Remote Sensing Image Semantic Labeling","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the 30th IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. Acm."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Dollar, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the 30th IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"48","DOI":"10.35534\/er.0402007","article-title":"Research on multi-scale target semantic segmentation for coastal ecological supervision","volume":"4","author":"Chen","year":"2022","journal-title":"Environ. Resour."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"6169","DOI":"10.1109\/TGRS.2020.3026051","article-title":"MAP-Net: Multiple Attending Path Neural Network for Building Footprint Extraction From Remote Sensed Imagery","volume":"59","author":"Zhu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Guo, R., Liu, J., Li, N., Liu, S., Chen, F., Cheng, B., Duan, J., Li, X., and Ma, C. (2018). Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7030110"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Xu, Z., Zhang, W., Zhang, T., and Li, J. (2021). HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13122290"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/1980\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:12:39Z","timestamp":1760123559000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/1980"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,9]]},"references-count":48,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15081980"],"URL":"https:\/\/doi.org\/10.3390\/rs15081980","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,9]]}}}