{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:15:54Z","timestamp":1771467354848,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T00:00:00Z","timestamp":1644364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61702374"],"award-info":[{"award-number":["No.61702374"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&amp;D Program of China","award":["No.2018YFB0505001"],"award-info":[{"award-number":["No.2018YFB0505001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Over the past few years, deep learning algorithms have held immense promise for better multi-spectral (MS) optical remote sensing image (RSI) analysis. Most of the proposed models, based on convolutional neural network (CNN) and fully convolutional network (FCN), have been applied successfully on computer vision images (CVIs). However, there is still a lack of exploration of spectra correlation in MS RSIs. In this study, a deep neural network with a spectrum separable module (DSSM) is proposed for semantic segmentation, which enables the utilization of MS characteristics of RSIs. The experimental results obtained on Zurich and Potsdam datasets prove that the spectrum-separable module (SSM) extracts more informative spectral features, and the proposed approach improves the segmentation accuracy without increasing GPU consumption.<\/jats:p>","DOI":"10.3390\/rs14040818","type":"journal-article","created":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T21:26:48Z","timestamp":1644442008000},"page":"818","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["DSSM: A Deep Neural Network with Spectrum Separable Module for Multi-Spectral Remote Sensing Image Segmentation"],"prefix":"10.3390","volume":"14","author":[{"given":"Hongming","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Software Engineering, Tongji University, 4800 Caoan Road Jiading District, Shanghai 201804, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4232-2158","authenticated-orcid":false,"given":"Rui","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, 4800 Caoan Road Jiading District, Shanghai 201804, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6569-2613","authenticated-orcid":false,"given":"Letong","family":"Han","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, 4800 Caoan Road Jiading District, Shanghai 201804, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0352-9730","authenticated-orcid":false,"given":"Hongfei","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, 4800 Caoan Road Jiading District, Shanghai 201804, China"}]},{"given":"Zeju","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, 4800 Caoan Road Jiading District, Shanghai 201804, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3755-4870","authenticated-orcid":false,"given":"Bowen","family":"Du","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, 4800 Caoan Road Jiading District, Shanghai 201804, China"},{"name":"Department of Computer Science, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1612-4844","authenticated-orcid":false,"given":"Sicong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Tongji University, 1239 Siping Road Yangpu District, Shanghai 200082, China"}]},{"given":"Qin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, 4800 Caoan Road Jiading District, Shanghai 201804, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4483","DOI":"10.1109\/TGRS.2015.2400462","article-title":"Robust rooftop extraction from visible band images using higher order CRF","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2017.12.007","article-title":"Semantic labeling in very high resolution images via a self-cascaded convolutional neural network","volume":"145","author":"Liu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.3390\/rs3081777","article-title":"Segment-based land cover mapping of a suburban area\u2014Comparison of high-resolution remotely sensed datasets using classification trees and test field points","volume":"3","author":"Matikainen","year":"2011","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2320","DOI":"10.1016\/j.rse.2011.04.032","article-title":"Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP\/OLS nighttime light data","volume":"115","author":"Zhang","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1109\/TCYB.2016.2531179","article-title":"Joint dictionary learning for multispectral change detection","volume":"47","author":"Lu","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.rse.2017.11.026","article-title":"Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover","volume":"205","author":"Goldblatt","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_7","unstructured":"O\u2019Shea, K., and Nash, R. (2015). An introduction to convolutional neural networks. arXiv."},{"key":"ref_8","first-page":"640","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Long","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","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 International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5398","DOI":"10.1109\/JSTARS.2020.3021098","article-title":"BAS Net: Boundary-Aware Semi-Supervised Semantic Segmentation Network for Very High Resolution Remote Sensing Images","volume":"13","author":"Sun","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","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":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, J., Lin, S., Ding, L., and Bruzzone, L. (2020). Multi-scale context aggregation for semantic segmentation of remote sensing images. Remote Sens., 12.","DOI":"10.3390\/rs12040701"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, G., Li, C., Wei, W., Jing, W., Wo\u017aniak, M., Bla\u017eauskas, T., and Dama\u0161evi\u010dius, R. (2019). Fully convolutional neural network with augmented atrous spatial pyramid pool and fully connected fusion path for high resolution remote sensing image segmentation. Appl. Sci., 9.","DOI":"10.3390\/app9091816"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Radoux, J., Bourdouxhe, A., Coos, W., Dufr\u00eane, M., and Defourny, P. (2019). Improving ecotope segmentation by combining topographic and spectral data. Remote Sens., 11.","DOI":"10.3390\/rs11030354"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.isprsjprs.2018.04.014","article-title":"Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning","volume":"145","author":"Kemker","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Peng, D., Zhang, Y., and Guan, H. (2019). End-to-end change detection for high resolution satellite images using improved unet++. Remote Sens., 11.","DOI":"10.3390\/rs11111382"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/0034-4257(91)90017-Z","article-title":"Normalized difference vegetation index measurements from the Advanced Very High Resolution Radiometer","volume":"35","author":"Goward","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2617","DOI":"10.1016\/S0042-6989(02)00297-3","article-title":"A spectral histogram model for texton modeling and texture discrimination","volume":"42","author":"Liu","year":"2002","journal-title":"Vis. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep learning for remote sensing data: A technical tutorial on the state of the art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"258619","DOI":"10.1155\/2015\/258619","article-title":"Deep convolutional neural networks for hyperspectral image classification","volume":"2015","author":"Hu","year":"2015","journal-title":"J. Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Makantasis, K., Karantzalos, K., Doulamis, A., and Doulamis, N. (2015, January 26\u201331). Deep supervised learning for hyperspectral data classification through convolutional neural networks. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326945"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5293","DOI":"10.1109\/TGRS.2017.2705073","article-title":"BASS net: Band-adaptive spectral-spatial feature learning neural network for hyperspectral image classification","volume":"55","author":"Santara","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1109\/LGRS.2016.2595108","article-title":"A self-improving convolution neural network for the classification of hyperspectral data","volume":"13","author":"Ghamisi","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, H., and Shen, Q. (2017). Spectral\u2013spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sens., 9.","DOI":"10.3390\/rs9010067"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep feature extraction and classification of hyperspectral images based on convolutional neural networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Volpi, M., and Ferrari, V. (2015, January 7\u201312). Semantic segmentation of urban scenes by learning local class interactions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301377"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"293","DOI":"10.5194\/isprsannals-I-3-293-2012","article-title":"The ISPRS benchmark on urban object classification and 3D building reconstruction","volume":"I-3","author":"Rottensteiner","year":"2012","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Minaee, S., Boykov, Y.Y., Porikli, F., Plaza, A.J., Kehtarnavaz, N., and Terzopoulos, D. (2021). Image segmentation using deep learning: A survey. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2021.3059968"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"106062","DOI":"10.1016\/j.knosys.2020.106062","article-title":"Evolution of image segmentation using deep convolutional neural network: A survey","volume":"201","author":"Sultana","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_38","unstructured":"Wu, J. (2017). Introduction to Convolutional Neural Networks, National Key Lab for Novel Software Technology, Nanjing University."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., and Torr, P.H. (2015, January 11\u201318). Conditional random fields as recurrent neural networks. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.179"},{"key":"ref_40","unstructured":"Chen, L.C., 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_41","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":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_44","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 European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2811","DOI":"10.1109\/TGRS.2017.2783902","article-title":"When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs","volume":"56","author":"Cheng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","unstructured":"Sherrah, J. (2016). Fully convolutional networks for dense semantic labelling of high-resolution aerial imagery. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.isprsjprs.2017.11.009","article-title":"Classification with an edge: Improving semantic image segmentation with boundary detection","volume":"135","author":"Marmanis","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Pan, X., Gao, L., Zhang, B., Yang, F., and Liao, W. (2018). High-resolution aerial imagery semantic labeling with dense pyramid network. Sensors, 18.","DOI":"10.3390\/s18113774"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Nemoto, K., Hamaguchi, R., Imaizumi, T., and Hikosaka, S. (2018, January 22\u201327). Classification of rare building change using cnn with multi-class focal loss. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517563"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"39401","DOI":"10.1109\/ACCESS.2018.2856088","article-title":"An end-to-end neural network for road extraction from remote sensing imagery by multiple feature pyramid network","volume":"6","author":"Gao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep learning classification of land cover and crop types using remote sensing data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1109\/TGRS.2016.2543748","article-title":"Spectral\u2013spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach","volume":"54","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5277","DOI":"10.1109\/TGRS.2019.2961681","article-title":"Lightweight spectral\u2013spatial squeeze-and-excitation residual bag-of-features learning for hyperspectral classification","volume":"58","author":"Roy","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5966","DOI":"10.1109\/TGRS.2020.3015157","article-title":"Graph convolutional networks for hyperspectral image classification","volume":"59","author":"Hong","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/4\/818\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:17:09Z","timestamp":1760134629000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/4\/818"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,9]]},"references-count":55,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14040818"],"URL":"https:\/\/doi.org\/10.3390\/rs14040818","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,9]]}}}