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Therefore, the timely and accurate monitoring of the land desertification status can provide scientific decision support for desertification control. The existing automatic interpretation methods are affected by factors such as \u201csame spectrum different matter\u201d, \u201cdifferent spectrum same object\u201d, staggered distribution of desertification areas, and wide ranges of ground objects. We propose an automatic interpretation method for the remote sensing of land desertification that incorporates multi-scale local binary pattern (MSLBP) and spectral features based on the above issues. First, a multi-scale convolutional LBP feature extraction network is designed to obtain the spatial texture features of remote sensing images and fuse them with spectral features to enhance the feature representation capability of the model. Then, considering the continuity of the distribution of the same kind of ground objects in local space, we designed an adaptive median filtering method to process the probability map of the extreme learning machine (ELM) classifier output to improve the classification accuracy. Four typical datasets were developed using GF-1 multispectral imagery with the Horqin Left Wing Rear Banner as the study area. Experimental results on four datasets show that the proposed method solves the problem of ill classification and omission in classifying the remote sensing images of desertification, effectively suppresses the effects of \u201chomospectrum\u201d and \u201cheterospectrum\u201d, and significantly improves the accuracy of the remote sensing interpretation of land desertification.<\/jats:p>","DOI":"10.3390\/rs14143486","type":"journal-article","created":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T22:38:50Z","timestamp":1658443130000},"page":"3486","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Multi-Scale LBP Texture Feature Learning Network for Remote Sensing Interpretation of Land Desertification"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0688-0961","authenticated-orcid":false,"given":"Wuli","family":"Wang","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, Cina University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yumeng","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, Cina University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ge","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, Cina University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangming","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, Cina University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongwei","family":"Li","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, Cina University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1408-5514","authenticated-orcid":false,"given":"Baodi","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Converse, R.L., Lippitt, C.D., and Lippitt, C.L. 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