Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162770
Title: Very high-resolution satellite image segmentation using variable-length multi-objective genetic clustering for multi-class change detection
Authors: Pal, Ramen
Mukhopadhyay, Somnath
Chakraborty, Debasish
Suganthan, Ponnuthurai Nagaratnam
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Pal, R., Mukhopadhyay, S., Chakraborty, D. & Suganthan, P. N. (2022). Very high-resolution satellite image segmentation using variable-length multi-objective genetic clustering for multi-class change detection. Journal of King Saud University - Computer and Information Sciences. https://dx.doi.org/10.1016/j.jksuci.2021.12.023
Journal: Journal of King Saud University - Computer and Information Sciences
Abstract: The baseline approaches on satellite image segmentation problem suffer from issues like sensitivity towards initialization, local optima solutions, a predefined number of output clusters, single-objective optimization, etc. To solve these challenges, this study proposes a unique pixel-level Multi-Spectral (MS) very high resolution (VHR) image segmentation algorithm based on variable-length multi-objective genetic clustering. We propose a new approach to update solutions by retaining variable length property throughout the optimization process. The resulting clustering algorithm contains a set of near-Pareto-optimal solutions. A map that has a scale of less than 1/10000 is called a large-scale map. We propose a large-scale change detection technique as an application of the proposed image segmentation algorithm. Solving Land-use/Land-Cover (LULC) change detection problems in a congested area is a complex task. This study considers the dataset from Pleiades-HR 1B, and Landsat 5 TM sensors in the experimental study. An extensive quantitative and qualitative analysis is performed to validate the superior performance of the proposed method with different state-of-the-art techniques.
URI: https://hdl.handle.net/10356/162770
ISSN: 1319-1578
DOI: 10.1016/j.jksuci.2021.12.023
Rights: © 2022 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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