Water‐body segmentation from satellite images using Kapur's entropy‐based thresholding method. (14th June 2020)

• Record Type: Journal Article
• Authors: Aalan Babu, A; Mary Anita Rajam, V
• Is Part Of: Computational intelligence. Volume 36:Number 3(2020)
• Page Start: 1242
• Page End: 1260
• Publication Date: 2020-06-14
• Subjects: entropy‐based -- image segmentation -- morphological operations -- satellite imagery -- threshold; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12339
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Water body segmentation helps in extracting water bodies like lake, pond, river, and reservoir from high resolution satellite images. This also helps in discovering new water bodies. But, extraction of water bodies from satellite images is much complicated, mainly due to the severe disparity in size, shape, and appearance of the water bodies. In this article, Kapur's entropy‐based thresholding method is proposed for the segmentation of water bodies from Very High Resolution (VHR) satellite images. The dataset used in this article is AIRS (Aerial Imagery for Roof Segmentation) dataset, with VHR satellite images, from which only the images with water bodies are considered. Experimental results show that the proposed method yields better segmentation performance with an overall accuracy of 98.43% and Structural Similarity Index rate of 0.9712.