Hyperspectral Image Classification Based on Capsule Network
Hyperspectral Image Classification Based on Capsule Network作者机构：School of Science China University of Geosciences (Beijing) School of Statistics Beijing Normal University Beijing Zhongdi Runde Petroleum Technology Co. Ltd.
出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))
年 卷 期：2022年第31卷第1期
页 面：146-154页
核心收录：
学科分类：0710[理学-生物学] 0810[工学-信息与通信工程] 08[工学] 081104[工学-模式识别与智能系统] 081002[工学-信号与信息处理] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Capsule network Space-spectrum fusion Hyperspectral Classification Deep learning
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.
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