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 | Schools: | School of Electrical and Electronic Engineering | 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 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
1-s2.0-S1319157821003694-main.pdf | 5.78 MB | Adobe PDF | View/Open |
SCOPUSTM
Citations
20
12
Updated on Mar 26, 2024
Web of ScienceTM
Citations
50
5
Updated on Oct 27, 2023
Page view(s)
93
Updated on Mar 29, 2024
Download(s) 50
61
Updated on Mar 29, 2024
Google ScholarTM
Check
Altmetric
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.