A two-branch multiscale spectral-spatial feature extraction network for hyperspectral image classification  

作　　者：Aamir Ali Caihong Mu Zeyu Zhang Jian Zhu Yi Liu 

机构地区：[1]Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education,Collaborative Innovation Center of Quantum Information of Shaanxi Province,School of Artificial Intelligence,Xidian University,Xi'an 710071,China [2]school of Electronic Engineering,Xidian University,Xi'an 710071,China

出　　处：《Journal of Information and Intelligence》2024年第3期224-235,共12页信息与智能学报（英文）

基　　金：supported by the National Natural Science Foundation of China(62077038,61672405,62176196 and 62271374)。

摘　　要：In the field of hyperspectral image(HSI)classification in remote sensing,the combination of spectral and spatial features has gained considerable attention.In addition,the multiscale feature extraction approach is very effective at improving the classification accuracy for HSIs,capable of capturing a large amount of intrinsic information.However,some existing methods for extracting spectral and spatial features can only generate low-level features and consider limited scales,leading to low classification results,and dense-connection based methods enhance the feature propagation at the cost of high model complexity.This paper presents a two-branch multiscale spectral-spatial feature extraction network(TBMSSN)for HSI classification.We design the mul-tiscale spectral feature extraction(MSEFE)and multiscale spatial feature extraction(MSAFE)modules to improve the feature representation,and a spatial attention mechanism is applied in the MSAFE module to reduce redundant information and enhance the representation of spatial fea-tures at multiscale.Then we densely connect series of MSEFE or MSAFE modules respectively in a two-branch framework to balance efficiency and effectiveness,alleviate the vanishing-gradient problem and strengthen the feature propagation.To evaluate the effectiveness of the proposed method,the experimental results were carried out on bench mark HsI datasets,demonstrating that TBMSSN obtained higher classification accuracy compared with several state-of-the-art methods.

关 键 词：Hyperspectral image classification Multiscale spectral-spatial information Two-branch architecture 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]