Combining Interval Type-2 Fuzzy Clustering Method with Preprocessing Model for High-Resolution Remote Sensing Images
Article No.: 53, Pages 1 - 8
Abstract
Abstract: Aiming at the problem that the classification accuracy decreases due to the increase of heterogeneity in homogeneous areas of high-resolution remote sensing images and the increase of similarity in different areas, an interval type-2 fuzzy clustering method combined with the image preprocessing model is proposed. First, the spatial information and grayscale information of adjacent pixels in the image are used to filter the image to reduce the impact of outlier pixels. Then, the double fuzzy factor is used to construct an interval type two fuzzy clustering model to improve the algorithm's ability to deal with uncertainty. Finally, the maximum membership criterion is used to defuzzify the fuzzy membership matrix to obtain clear classification results. Experimental results on WorldView-2 and QuickBird high-resolution remote sensing images show that the proposed algorithm can effectively suppress the influence of regional noise and achieve higher classification accuracy.
Keywords: High-resolution remote sensing images; preprocessing model; interval type-2 fuzzy clustering; uncertainty
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Published In
January 2024
506 pages
ISBN:9798400718199
DOI:10.1145/3653804
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Published: 01 June 2024
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CVDL 2024
CVDL 2024: The International Conference on Computer Vision and Deep Learning
January 19 - 21, 2024
Changsha, China
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