Multi-Scale LBP Texture Feature Learning Network for Remote Sensing Interpretation of Land Desertification

作     者：Wang, Wuli Jiang, Yumeng Wang, Ge Guo, Fangming Li, Zhongwei Liu, Baodi 

作者机构：Cina Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China China Univ Petr East China Coll Control Sci & Engn Qingdao 266580 Peoples R China 

出 版 物：《REMOTE SENSING》 (遥感)

年 卷 期：2022年第14卷第14期

页      面：3486页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1002[医学-临床医学] 070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：Joint Funds of the Fundamental Research Funds for the Central Universities [27R2117001A] National Natural Science Foundation of China [U1906217] Shandong Social Science Planning [21CSDJ74] Fundamental Research Funds for the Central Universities [22CX01004A-8] 

主　　题：desertification land cover classification extreme learning machine local binary patterns Horqin Left Wing Rear Banner 

摘      要：Land desertification is a major challenge to global sustainable development. 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 same spectrum different matter, different spectrum same object, 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 homospectrum and heterospectrum, and significantly improves the accuracy of the remote sensing interpretation of land desertification.