Hyperspectral Image Classification Based on Capsule Network  

作　　者：MA Qiaoyu ZHANG Xin ZHANG Chunlei ZHOU Heng 

机构地区：[1]School of Science,China University of Geosciences(Beijing),Beijing 100083,China [2]School of Statistics,Beijing Normal University,Beijing 100875,China [3]Beijing Zhongdi Runde Petroleum Technology Co.,Ltd.,Beijing 100083,China

出　　处：《Chinese Journal of Electronics》2022年第1期146-154,共9页电子学报（英文版）

摘　　要：The conventional convolutional neural network performs not well enough in the ground objects classification because of its insufficient ability in maintaining sensitive spectral information and characterizing the covariance of spatial structure, resulting from the narrow sensitive frequency band and complex spatial structure with diversity of hyperspectral remote sensing data which caused more serious phenomena of “same material, different spectra” and “different material, same spectra”.Therefore, an improved capsule network is proposed and introduced into hyperspectral image target recognition. A convolution structure combining shallow features and multi-scale depth features is put forward to reduce the phenomena of “different material, same spectra” firstly,and then the diversity of the spatial structure is expressed by the capsule vector and sub-capsule division in channel wise, so that the averaging effect of the convolution process is weakened in the spectral domain and the spatial domain to reduce the phenomena of “same material, different spectra”. By comparing the experimental results on the hyperspectral data sets such as Indian Pines,Salinas, Tea Tree and Xiongan, the capsule network shows strong spatial structure expression ability, flexible deep and shallow feature fusion ability in multi-scale, and its accuracy in target recognition is better than that of conventional convolutional neural networks, so it is suitable for the recognition of complex targets in hyperspectral images.

关 键 词：Capsule network Space-spectrum fusion HYPERSPECTRAL CLASSIFICATION Deep learning 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP751[自动化与计算机技术—控制科学与工程]