{"title":"Spectral–Spatial Feature Extraction Network With SSM–CNN for Hyperspectral–Multispectral Image Collaborative Classification","authors":"Qingwang Wang;Xingxing Fan;Jiangbo Huang;Shuai Li;Tao Shen","doi":"10.1109/JSTARS.2024.3464681","DOIUrl":null,"url":null,"abstract":"Multisource remote sensing (RS) image classification is a significant research area in Earth observation, aiming to achieve more comprehensive and accurate classification of land cover by integrating data from different sensors. Due to differences in imaging mechanisms and information imbalance between multisource data, multisource RS image classification faces two major challenges as follows. 1) Synergistically capturing features from different modalities to fully exploit complementary information. 2) Adaptively fusing multisource features to overcome the imbalance between modalities and avoid redundant information. This article proposes a spectral–spatial feature extraction network with SSM–CNN (SSFNet) for the collaborative classification of hyperspectral images (HSI) and multispectral images (MSI). Specifically, SSFNet captures long-range spectral correlations in HSI through a bidirectional state–space model (SSM) and learns local correlations between adjacent channels through spectral grouping, achieving global–local spectral information mining in HSI. Simultaneously, joint spatial feature extraction for HSI and MSI data is performed using embedded weight-shared residual feature extractor based on convolutional neural network. This process involves adaptively identifying the importance of features through privatized factors in batch normalization and accurately replacing redundant features. In addition, a spatial attention module is used to further enhance spatial feature representation. Finally, to better accommodate feature distributions and enhance classification outcomes, the extracted spectral–spatial features are combined using weighted fusion, allowing for dynamic integration. Experimental results on two datasets demonstrate that the proposed SSFNet significantly outperforms other competing methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10684557","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10684557/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Multisource remote sensing (RS) image classification is a significant research area in Earth observation, aiming to achieve more comprehensive and accurate classification of land cover by integrating data from different sensors. Due to differences in imaging mechanisms and information imbalance between multisource data, multisource RS image classification faces two major challenges as follows. 1) Synergistically capturing features from different modalities to fully exploit complementary information. 2) Adaptively fusing multisource features to overcome the imbalance between modalities and avoid redundant information. This article proposes a spectral–spatial feature extraction network with SSM–CNN (SSFNet) for the collaborative classification of hyperspectral images (HSI) and multispectral images (MSI). Specifically, SSFNet captures long-range spectral correlations in HSI through a bidirectional state–space model (SSM) and learns local correlations between adjacent channels through spectral grouping, achieving global–local spectral information mining in HSI. Simultaneously, joint spatial feature extraction for HSI and MSI data is performed using embedded weight-shared residual feature extractor based on convolutional neural network. This process involves adaptively identifying the importance of features through privatized factors in batch normalization and accurately replacing redundant features. In addition, a spatial attention module is used to further enhance spatial feature representation. Finally, to better accommodate feature distributions and enhance classification outcomes, the extracted spectral–spatial features are combined using weighted fusion, allowing for dynamic integration. Experimental results on two datasets demonstrate that the proposed SSFNet significantly outperforms other competing methods.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.