Generating Fuzzy Membership Functions for Modeling Wetland Ecosystems from Multispectral Remote Sensing Images

The inherent fuzziness of wetland ecosystems largely accounts for the spectral variability of wetland ecosystems in remote sensing images. In addition, a limited spatial resolution leads to the presence of many mixed pixels in middle-resolution multispectral remote sensing images. Existing methods t...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2024-01, Vol.17, p.1-15
Main Authors: Guo, Jifa, Du, Shihong
Format: Article
Language:English
Subjects:
Aquatic ecosystems
Classification
Clustering
Components
Ecosystems
Fuzziness
Fuzzy set
Grasslands
Image reconstruction
Indexes
Land cover
Land surface
membership function (MF) generation
Membership function generation
Pixels
Remote sensing
Spatial discrimination
Spatial resolution
Spectral variability
Variability
Vegetation mapping
Wetlands
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Summary:The inherent fuzziness of wetland ecosystems largely accounts for the spectral variability of wetland ecosystems in remote sensing images. In addition, a limited spatial resolution leads to the presence of many mixed pixels in middle-resolution multispectral remote sensing images. Existing methods that use a single center or a limited number of endmembers to represent land cover types cannot fully account for the spectral variability of land cover types; moreover, these methods use these limited representations to calculate the membership function (MF), leading to limited classification and mapping performance. To address spectral variability and mixed pixels, this study proposes a novel MF generation method, in which mixed pixels are treated as an auxiliary type, and the clustering and spectral characteristics are integrated to detect the cores of land cover types in spectral space. Then, the spectral diversity can be fully expressed by the core components of the land cover types. The membership values of mixed pixels are calculated from the core components via the sparse reconstruction method. The experiment shows that the proposed method has a substantial increase in classification accuracy over existing methods.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2024.3379371