Snow Cover Mapping for Mountainous Areas by Fusion of MODIS L1B and Geographic Data Based on Stacked Denoising Auto-Encoders

作     者：Xi Kan Yonghong Zhang Linglong Zhu Liming Xiao Jiangeng Wang Wei Tian Haowen Tan 

作者机构：School of Atmospheric ScienceNanjing University of Information Science and TechnologyNanjing210044China School of Information and ControlNanjing University of Information Science and TechnologyNanjing210044China School of Atmospheric PhysicsNanjing University of Information Science and TechnologyNanjing210044China School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing210044China Department of Computer EngineeringChosun UniversityGwangju501-759South Korea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2018年第57卷第10期

页      面：49-68页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was supported by National Natural Science Foundation of China(Grant Nos.41661144039,91337102,41401481) and Natural Science Foundation of Jiangsu Province of China(Grant No.BK20140997) 

主　　题：Snow cover remote sensing deep learning Qinghai-Tibetan Plateau MODIS L1B 

摘      要：Snow cover plays an important role in meteorological and hydrological ***,the accuracies of currently available snow cover products are significantly lower in mountainous areas than in plains,due to the serious snow/cloud confusion problem caused by high altitude and complex *** at this problem,an improved snow cover mapping approach for mountainous areas was proposed and applied in Qinghai-Tibetan *** this work,a deep learning framework named Stacked Denoising Auto-Encoders(SDAE)was employed to fuse the MODIS multispectral images and various geographic datasets,which are then classified into three categories:Snow,cloud and snow-free ***,two independent SDAE models were trained for snow mapping in snow and snow-free seasons respectively in response to the seasonal variations of meteorological *** proposed approach was verified using in-situ snow depth records,and compared to the most widely used snow products MOD10A1 and *** comparison results show that our method got the best performance:Overall accuracy of 98.95%and F-measure of 73.84%.The results indicated that our method can effectively improve the snow recognition accuracy,and it can be further extended to other multi-source remote sensing image classification issues.