Abstract
Water-body segmentation in remote sensing image interpretation helps in detecting water bodies such as lakes, ponds, rivers, and reservoirs belonging to high-resolution satellite images. Water bodies of different shape, size and color creates hindrance in accurate detection. Most of the existing water-body segmentation methods failed to obtain the accurate position of water boundaries. An irregular shape of the water body highly restricts the normal watershed algorithm to calculate the accurate distance transform and segment the water body with high accuracy. Therefore, an attempt is made to calculate the Euclidean distance using cosine similarity-based watershed algorithm for accurate segmentation of irregular water bodies. To ensure the faster convergence speed in feature selection process, a Chaotic Forest Optimization Algorithm is utilized for feature selection. A modified convolution neural network (MCNN) classifier utilizing switchable normalization instead of batch normalization is proposed to speed up the training time. MCNN classifier outperforms some of the existing deep neural network classifiers in accuracy and misclassification rate. The proposed method achieves higher segmentation accuracy of 0.919713 and outperforms existing active contour, K-means, fuzzy C-means, and region growing approach.
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All the authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by SG, JS. The first draft of the manuscript was written by SG and all the authors commented on previous versions of the manuscript. All the authors read and approved the final manuscript.
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This article is part of a Topical Collection in Environmental Earth Sciences on Deep learning for earth resource and environmental remote sensing, guest edited by Carlos Enrique Montenegro Marin, Xuyun Zhang and Nallappan Gunasekaran.
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Gautam, S., Singhai, J. Cosine-similarity watershed algorithm for water-body segmentation applying deep neural network classifier. Environ Earth Sci 81, 251 (2022). https://doi.org/10.1007/s12665-022-10376-y
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DOI: https://doi.org/10.1007/s12665-022-10376-y