Supervised hyperspectral multi-scale image convolution classification method

• Main Authors: SONG YINING, CHANG YONGLEI, YE FAMAO, LI YATING, XU GUANGYU, CHEN YINGYAO, WANG WEI, PU SHENGLIANG, LIU BO, LIU XIANSAN, XIA YUANPING, XIE XIAOWEI, HUANG DUAN, NIE YUNJU, YU MEI, HE HAIQING
• Format: Patent
• Language: Chinese; English
• Published: 11.06.2021
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; HANDLING RECORD CARRIERS; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS

The invention relates to the field of hyperspectral remote sensing intelligent information processing, in particular to a hyperspectral multi-scale image convolution neural network for realizing fine classification of ground surface coverage in a hyperspectral image scene in a supervised mode, and more particularly relates to a supervised hyperspectral multi-scale image convolution classification method. According to the invention, for a high-dimensional nonlinear hyperspectral data structure, a small amount of known sample information is supervised to be used for training a multi-scale graph convolutional neural network by constructing global, local and spectral index adjacency matrixes, so that the graph convolutional neural network can effectively adapt to hyperspectral data for feature learning and label prediction; therefore, the graph expression capability of the nonlinear characteristics of the hyperspectral data can be enhanced, and the precision of land cover classification and recognition can be imp