Fractional Impervious Surface Mapping on Multispectral Images With Visible Shadows via a Bundle-Based Sparse Unmixing Model Article Swipe
Y.Y. Liu
,
Dacheng Li
,
Xiong Wei
,
Qijin Han
,
Lingling Ma
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2025.3568537
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2025.3568537
Impervious surface abundance (ISA) is an important indicator for monitoring urbanization and environmental disaster management processes. Commonly used spectral unmixing techniques extract ISA in the form of mixed pixels; however, in previous multispectral unmixing studies based on ISA estimation, scholars have seldom simultaneously considered urban shading and endmember variability, which has severely affected the accuracy of the results. To solve this problem, we propose a combination of endmember bundle extraction based on superpixel segmentation and sparse unmixing for the ISA estimation of multispectral images containing visible shadows (EESS), considering the city district of Taiyuan as an example. The results show that the endmember bundles extracted via EESS are comparable with the manually identified endmember bundles in terms of accuracy, and the error differences in the unmixing results do not exceed 3%. In addition, the sparse unmixing algorithm combined with the endmember bundle extraction technique can effectively distinguish impervious surfaces, vegetation, and soils, and the root-mean-square error, mean absolute error, and systematic error of the whole area are much lower than those of the traditional spectral unmixing method, which are approximately 5.52%, 5.86%, and 8.24% lower than those of FCLSUMANU, respectively. Further analyses reveal that adding two types of endmembers, namely, urban shadows and mountain shadows, to the urban component model can effectively mitigate the impact of shadows on ISA estimation. Furthermore, the consistency of the EESS method across Sentinel and Landsat data demonstrates its strong generalizability, enabling it to adapt to the characteristics of different sensors.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jstars.2025.3568537
- OA Status
- gold
- References
- 68
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4410204556Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/jstars.2025.3568537Digital Object Identifier
- Title
-
Fractional Impervious Surface Mapping on Multispectral Images With Visible Shadows via a Bundle-Based Sparse Unmixing ModelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
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Y.Y. Liu, Dacheng Li, Xiong Wei, Qijin Han, Lingling MaList of authors in order
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https://doi.org/10.1109/jstars.2025.3568537Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/jstars.2025.3568537Direct OA link when available
- Concepts
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Multispectral image, Impervious surface, Bundle adjustment, Computer science, Computer vision, Remote sensing, Artificial intelligence, Surface (topology), Geology, Image (mathematics), Mathematics, Geometry, Ecology, BiologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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68Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_location.raw_source_name | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| primary_location.landing_page_url | https://doi.org/10.1109/jstars.2025.3568537 |
| publication_date | 2025-01-01 |
| publication_year | 2025 |
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