{"title":"Hyperspectral Band Selection via Joint Volume Gradient","authors":"Songyi Xiao;Liangliang Zhu;Shouzhi Li;Luyan Ji;Xiurui Geng","doi":"10.1109/JSTARS.2024.3457671","DOIUrl":null,"url":null,"abstract":"Unsupervised band selection (BS) is a crucial research direction in the domain of hyperspectral image (HSI) processing. In recent years, volume-based criteria have garnered considerable attention, with the volume-gradient-based BS (VGBS) algorithm being particularly notable. However, we have identified that VGBS inherently suffers from the local extremum problem due to its reliance on the original volume gradient formula, which only permits the removal of a single band per iteration. To address this issue, we introduce the concept of joint volume gradient (JVG) through a novel determinant formula for the high-order mixed product expansion. We then propose an enhanced version of VGBS, termed JVG-based BS (JVGBS), which allows for the simultaneous deletion of multiple bands. Moreover, a simplified objective function of JVG is developed to mitigate the high computational complexity associated with calculating volume metrics when a small number of bands is removed at once. Regarding the complexity imposed by the large cardinality of traversing matrix column combinations, we provide an exemplary algorithm employing groupwise strategies to achieve rapid computational acceleration. Experimental results on Gaofen-5 and publicly available hyperspectral datasets demonstrate that the proposed algorithms have rather superior performance against state-of-the-art competitors in terms of both computational complexity and classification accuracy.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670289","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/10670289/","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
Unsupervised band selection (BS) is a crucial research direction in the domain of hyperspectral image (HSI) processing. In recent years, volume-based criteria have garnered considerable attention, with the volume-gradient-based BS (VGBS) algorithm being particularly notable. However, we have identified that VGBS inherently suffers from the local extremum problem due to its reliance on the original volume gradient formula, which only permits the removal of a single band per iteration. To address this issue, we introduce the concept of joint volume gradient (JVG) through a novel determinant formula for the high-order mixed product expansion. We then propose an enhanced version of VGBS, termed JVG-based BS (JVGBS), which allows for the simultaneous deletion of multiple bands. Moreover, a simplified objective function of JVG is developed to mitigate the high computational complexity associated with calculating volume metrics when a small number of bands is removed at once. Regarding the complexity imposed by the large cardinality of traversing matrix column combinations, we provide an exemplary algorithm employing groupwise strategies to achieve rapid computational acceleration. Experimental results on Gaofen-5 and publicly available hyperspectral datasets demonstrate that the proposed algorithms have rather superior performance against state-of-the-art competitors in terms of both computational complexity and classification accuracy.
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.