Subpixel Mapping of Hyperspectral Image Based on Multi-scale and Multi-feature

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dc.contributor.authorSong, Meiping
dc.contributor.authorLi, Lan
dc.contributor.authorZhang, Chunyun
dc.contributor.authorShi, Pengliang
dc.contributor.authorZhao, Liaoying
dc.contributor.authorXue, Bai
dc.date.accessioned2023-11-08T15:06:56Z
dc.date.available2023-11-08T15:06:56Z
dc.date.issued2023-10-19
dc.description.abstractThe ubiquity of mixed pixels in hyperspectral images makes it difficult for traditional classification techniques to determine the spatial distribution of land-cover classes accurately. Subpixel mapping (SPM) technology is an effective method to solve this problem. Aiming at taking the multiple scales and the spatial features into account, an SPM method based on multiscale and multifeature (MSMF) is proposed, so as to effectively improve the accuracy of SPM. First, the maximum linearization index (MLI) method of the nonredundant complete straight-line (CSL) set is designed to identify the linear distribution feature of land-cover (LC) classes. Then, different methods are applied to different spatial features and unified together finally, where the template matching iterative exchange is used for the linear distribution classes, and the multiscale spatial dependence (MSD) iterative exchange method combined with area perimeter is used for the planar distribution classes. Experiments on three remote sensing images are carried out to evaluate the performance of MSMF. The results show that the proposed method can effectively improve the accuracy of SPM.en
dc.description.sponsorshipThis work was supported in part by the National Natural Science Foundation of China under Grant 61971082 and Grant 61890964.en
dc.description.urihttps://ieeexplore.ieee.org/abstract/document/10288216en
dc.format.extent15 pagesen
dc.genrejournal articlesen
dc.genrepostprintsen
dc.identifierdoi:10.13016/m2rjsy-whxt
dc.identifier.citationSong, Meiping, Lan Li, Chunyun Zhang, Pengliang Shi, Liaoying Zhao, and Bai Xue. “Subpixel Mapping of Hyperspectral Image Based on Multi-Scale and Multi-Feature.” IEEE Transactions on Geoscience and Remote Sensing, 2023, 1–1. https://doi.org/10.1109/TGRS.2023.3325825.en
dc.identifier.urihttps://doi.org/10.1109/TGRS.2023.3325825
dc.identifier.urihttp://hdl.handle.net/11603/30591
dc.language.isoenen
dc.publisherIEEEen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rights© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.titleSubpixel Mapping of Hyperspectral Image Based on Multi-scale and Multi-featureen
dc.typeTexten
dcterms.creatorhttps://orcid.org/0000-0002-0881-9219en

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