Remote Sensing Imagery Segmentation: A Hybrid Approach

In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thr...

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Autores principales:	Shreya Pare, Himanshu Mittal, Mohammad Sajid, Jagdish Chand Bansal, Amit Saxena, Tony Jan, Witold Pedrycz, Mukesh Prasad
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	image segmentation remote sensing images multilevel Rényi’s entropy cuckoo search optimization algorithms Science Q
Acceso en línea:	https://doaj.org/article/d574c8ae68f6496f8f746ef672d2f527
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id	oai:doaj.org-article:d574c8ae68f6496f8f746ef672d2f527
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spelling	oai:doaj.org-article:d574c8ae68f6496f8f746ef672d2f5272021-11-25T18:54:43ZRemote Sensing Imagery Segmentation: A Hybrid Approach10.3390/rs132246042072-4292https://doaj.org/article/d574c8ae68f6496f8f746ef672d2f5272021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4604https://doaj.org/toc/2072-4292In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accuracy with high temporal difficulty due to the absence of spatial information. To mitigate this issue, this paper presents a new Rényi’s entropy and modified cuckoo search-based robust automatic multi-thresholding algorithm for remote sensing image analysis. In the proposed method, the modified cuckoo search algorithm is combined with Rényi’s entropy thresholding criteria to determine optimal thresholds. In the modified cuckoo search algorithm, the Lévy flight step size was modified to improve the convergence rate. An experimental analysis was conducted to validate the proposed method, both qualitatively and quantitatively against existing metaheuristic-based thresholding methods. To do this, the performance of the proposed method was intensively examined on high-dimensional remote sensing imageries. Moreover, numerical parameter analysis is presented to compare the segmented results against the gray-level co-occurrence matrix, Otsu energy curve, minimum cross entropy, and Rényi’s entropy-based thresholding. Experiments demonstrated that the proposed approach is effective and successful in attaining accurate segmentation with low time complexity.Shreya PareHimanshu MittalMohammad SajidJagdish Chand BansalAmit SaxenaTony JanWitold PedryczMukesh PrasadMDPI AGarticleimage segmentationremote sensing imagesmultilevel Rényi’s entropycuckoo searchoptimization algorithmsScienceQENRemote Sensing, Vol 13, Iss 4604, p 4604 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	image segmentation remote sensing images multilevel Rényi’s entropy cuckoo search optimization algorithms Science Q
spellingShingle	image segmentation remote sensing images multilevel Rényi’s entropy cuckoo search optimization algorithms Science Q Shreya Pare Himanshu Mittal Mohammad Sajid Jagdish Chand Bansal Amit Saxena Tony Jan Witold Pedrycz Mukesh Prasad Remote Sensing Imagery Segmentation: A Hybrid Approach
description	In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accuracy with high temporal difficulty due to the absence of spatial information. To mitigate this issue, this paper presents a new Rényi’s entropy and modified cuckoo search-based robust automatic multi-thresholding algorithm for remote sensing image analysis. In the proposed method, the modified cuckoo search algorithm is combined with Rényi’s entropy thresholding criteria to determine optimal thresholds. In the modified cuckoo search algorithm, the Lévy flight step size was modified to improve the convergence rate. An experimental analysis was conducted to validate the proposed method, both qualitatively and quantitatively against existing metaheuristic-based thresholding methods. To do this, the performance of the proposed method was intensively examined on high-dimensional remote sensing imageries. Moreover, numerical parameter analysis is presented to compare the segmented results against the gray-level co-occurrence matrix, Otsu energy curve, minimum cross entropy, and Rényi’s entropy-based thresholding. Experiments demonstrated that the proposed approach is effective and successful in attaining accurate segmentation with low time complexity.
format	article
author	Shreya Pare Himanshu Mittal Mohammad Sajid Jagdish Chand Bansal Amit Saxena Tony Jan Witold Pedrycz Mukesh Prasad
author_facet	Shreya Pare Himanshu Mittal Mohammad Sajid Jagdish Chand Bansal Amit Saxena Tony Jan Witold Pedrycz Mukesh Prasad
author_sort	Shreya Pare
title	Remote Sensing Imagery Segmentation: A Hybrid Approach
title_short	Remote Sensing Imagery Segmentation: A Hybrid Approach
title_full	Remote Sensing Imagery Segmentation: A Hybrid Approach
title_fullStr	Remote Sensing Imagery Segmentation: A Hybrid Approach
title_full_unstemmed	Remote Sensing Imagery Segmentation: A Hybrid Approach
title_sort	remote sensing imagery segmentation: a hybrid approach
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d574c8ae68f6496f8f746ef672d2f527
work_keys_str_mv	AT shreyapare remotesensingimagerysegmentationahybridapproach AT himanshumittal remotesensingimagerysegmentationahybridapproach AT mohammadsajid remotesensingimagerysegmentationahybridapproach AT jagdishchandbansal remotesensingimagerysegmentationahybridapproach AT amitsaxena remotesensingimagerysegmentationahybridapproach AT tonyjan remotesensingimagerysegmentationahybridapproach AT witoldpedrycz remotesensingimagerysegmentationahybridapproach AT mukeshprasad remotesensingimagerysegmentationahybridapproach
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Remote Sensing Imagery Segmentation: A Hybrid Approach

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