Semantic and spatial‐spectral feature fusion transformer network for the classification of hyperspectral image  

作　　者：Erxin Xie Na Chen Jiangtao Peng Weiwei Sun Qian Du Xinge You 

机构地区：[1]Hubei Key Laboratory of Applied Mathematics,Faculty of Mathematics and Statistics,Hubei University,Wuhan,China [2]Department of Geography and Spatial Information Techniques,Ningbo University,Ningbo,China [3]Department of Electrical and Computer Engineering,Mississippi State University,Mississippi State,Mississippi,USA [4]School of Electronic Information and Communications,Huazhong University of Science and Technology,Wuhan,China

出　　处：《CAAI Transactions on Intelligence Technology》2023年第4期1308-1322,共15页智能技术学报（英文）

基　　金：supported in part by the Natural Science Foundation of Hubei Province under Grant 2021CFA087;by the National Natural Science Foundation of China under Grant Nos.42171351,42122009,41971296.

摘　　要：Recently,transformer‐based networks have been introduced for the classification of hyperspectral image(HSI).Although transformer‐based methods can well capture spectral sequence information,their ability to fuse different types of information contained in HSI is still insufficient.To exploit rich spectral,spatial and semantic information in HSI,a novel semantic and spatial‐spectral feature fusion transformer(S3FFT)network is proposed in this study.In the proposed S3FFT method,spatial attention and efficient channel attention(ECA)modules are employed for the extraction of shallow spatialspectral features.Then,a transformer‐based module is designed to extract advanced fused features and to produce the pseudo‐label and class probability of each pixel for semantic feature extraction.Finally,the semantic,spatial and spectral features are combined by the transformer for classification.Compared with traditional deep learning methods and recently transformer‐based methods,the proposed S3FFT shows relatively better results on three HSI datasets.

关 键 词：image classification machine learning 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]