摘要
目前应用最为广泛的积雪覆盖区域图(SCA)可由中分辨率成像光谱仪(MODIS)获取,常被用于积雪覆盖时空变化的研究中.由于受云遮挡的影响,MODIS积雪产品存在较大区域的数据缺失.为了消除云遮挡的影响,本文构建一种降噪自编码神经网络模型,建立雪粒径与复杂地形、土地覆盖类型之间的复杂的映射关系,实现云下积雪参数的补全,提高积雪产品的覆盖面积.本文选取开都河流域为研究区域,将MODIS反演得到的积雪产品数据与地形地物数据结合,并通过降噪自编码神经网络(Denoising Autoencoder Artificial Neural Network)、极值雪线法相结合的方法来定量地回归补全高山复杂地形下由于云覆盖导致的积雪缺失数据,从而得到无缺失的逐日雪盖数据.其中,降噪自编码神经网络融合多特征数据,建立地形特征与雪粒径数据之间的非线性映射关系,从而来补全云层下的雪粒径数据;极值雪线法主要用来去除低海拔地区误报值,进一步提高雪盖提取精度.采用MODIS积雪产品对去云结果开展精度验证,本文所提出的去云方法的精度超过86%,有效地提高了雪盖提取精度.因此,本文所提的算法可以有效地去除复杂地形区域的云覆盖.
Snow cover is one of the important parameters in the study of hydrometeorology.At present,the most widely used Snow Cover Area(SCA)can be obtained by Moderate-resolution Imaging Spectroradiometer(MODIS),which is often used in the study of temporal and spatial changes of snow cover.However,large area snow data missing existed in MODIS snow cover products due to the cloud occlusion.To address this,we take the Kaidu River basin as the research region,and combine the snow product data retrieved from MODIS carried on the Terra and Aqua satellites with the topographic feature data,then use a denoising autoencoder artificial neural network and the extreme snow line method to quantitatively complement the snow data loss caused by cloud occlusion in complex alpine terrain.The denoising autoencoder artificial neural network combines multi-feature data to establish a nonlinear mapping relationship between topographic features and snow grain size,which is then used to complement the missing snow grain size data.The extreme snow line method is used to remove the false report value in low altitude area and obtain the snow cover image with high precision.In contrast verification,the accuracy of the proposed cloud removal method is over 86%,which effectively improves the snow cover detection.Therefore,the approach proposed in this paper can effectively remove cloud occlusion from snow products in complex terrain areas.
作者
张永宏
陈帅
王剑庚
朱灵龙
陈诗伟
ZHANG Yonghong;CHEN Shuai;WANG Jiangeng;ZHU Linglong;CHEN Shiwei(School of Automation,Nanjing University of Information Science&Technology,Nanjing 210044;School of Atmospheric Physics,Nanjing University of Information Science&Technology,Nanjing 210044;School of Electronics&Information Engineering,Nanjing University of Information Science&Technology,Nanjing 210044)
出处
《南京信息工程大学学报（自然科学版）》
CAS
北大核心
2023年第2期169-179,共11页
Journal of Nanjing University of Information Science & Technology（Natural Science Edition）
基金
国家自然科学基金(41875027,41871238)。
关键词
降噪自编码神经网络
极值雪线法
复杂地形
去云
denoising autoencoder artificial neural network
extreme snow line method
complex terrain
cloud removal
