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These factors make it more complicated for classical convolutional neural networks to obtain ideal results when performing a feature classification on remote-sensing images. To address this issue, we proposed an attention-based multiscale max-pooling dense network (DMAU-Net), which is based on U-Net for ground object classification. The network is designed with an integrated max-pooling module that incorporates dense connections in the encoder part to enhance the quality of the feature map, and thus improve the feature-extraction capability of the network. Equally, in the decoding, we introduce the Efficient Channel Attention (ECA) module, which can strengthen the effective features and suppress the irrelevant information. To validate the ground object classification performance of the multi-pooling integration network proposed in this paper, we conducted experiments on the Vaihingen and Potsdam datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). We compared DMAU-Net with other mainstream semantic segmentation models. The experimental results show that the DMAU-Net proposed in this paper effectively improves the accuracy of the feature classification of high-resolution remote-sensing images. The feature boundaries obtained by DMAU-Net are clear and regionally complete, enhancing the ability to optimize the edges of features.<\/jats:p>","DOI":"10.3390\/rs15051328","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T03:02:32Z","timestamp":1678071752000},"page":"1328","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["DMAU-Net: An Attention-Based Multiscale Max-Pooling Dense Network for the Semantic Segmentation in VHR Remote-Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8500-4303","authenticated-orcid":false,"given":"Yang","family":"Yang","sequence":"first","affiliation":[{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"3D Information Collection and Application Key Lab of Education Ministry, Capital Normal University, Beijing 100048, China"},{"name":"Beijing State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4226-5042","authenticated-orcid":false,"given":"Junwu","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"3D Information Collection and Application Key Lab of Education Ministry, Capital Normal University, Beijing 100048, China"},{"name":"Beijing State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China"}]},{"given":"Yanhui","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"3D Information Collection and Application Key Lab of Education Ministry, Capital Normal University, Beijing 100048, China"},{"name":"Beijing State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China"}]},{"given":"Bibo","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"3D Information Collection and Application Key Lab of Education Ministry, Capital Normal University, Beijing 100048, China"},{"name":"Beijing State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China"}]},{"given":"Zhigang","family":"Yang","sequence":"additional","affiliation":[{"name":"Surveying and Mapping Institute, Lands and Resource Department of Guangdong Province, Guangzhou 510670, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Su, Y., Cheng, J., Bai, H., Liu, H., and He, C. 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