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

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Subpixel_Mapping_of_Hyperspectral_Image_Based_on_Multiscale_and_Multifeature.pdf (4.02 MB)

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https://ieeexplore.ieee.org/abstract/document/10288216

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https://doi.org/10.1109/TGRS.2023.3325825
 http://hdl.handle.net/11603/30591

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection

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https://orcid.org/0000-0002-0881-9219

Date

2023-10-19

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journal articles
postprints
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Citation of Original Publication

Song, 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.

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Abstract

The 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.