An optimized feature selection approach using sand Cat Swarm optimization for hyperspectral image classification

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dc.authorscopusidAmir Seyyedabbasi / 57202833910
dc.authorscopusidAlaa Ali Hameed / 56338374100
dc.authorwosidAmir Seyyedabbasi / HJH-7387-2023
dc.authorwosidAlaa Ali Hameed / ABI-8417-2020
dc.contributor.authorHameed, Alaa Ali
dc.contributor.authorJamil, Akhtar
dc.contributor.authorSeyyedabbasi, Amir
dc.date.accessioned2025-04-18T07:34:44Z
dc.date.available2025-04-18T07:34:44Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü
dc.description.abstractIntegrating metaheuristic algorithms and optimization techniques with remote sensing technology has accelerated the advent of advanced methodologies for analyzing hyperspectral images (HSIs). These images, rich in detail across a broad spectral range, are pivotal for diverse applications. However, the high dimensionality of data poses challenges for obtaining optimal results therefore, a preprocessing step is necessary to reduce the dimensionality of the data to select the most effective features before the application of machine learning models. This study introduces a novel methodology that integrates Back Propagation (BP) and Variable Adaptive Momentum (BPVAM) with Sand Cat Swarm Optimization (SCSO) for the classification of hyperspectral images. Utilizing SCSO for the optimal feature selection followed by BPVAM generated more accurate classification maps. The fusion of the unique strengths of SCSO with the flexibility of BPVAM has significantly boosted the precision, efficiency, and adaptability of HSI classification. The effectiveness of our method is demonstrated using two benchmark hyperspectral datasets and validated through a comprehensive comparison with other benchmark optimization techniques, including Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). Our findings indicate that our approach enhances classification accuracy that is comparable to the stateof-the-art methods in the domain of hyperspectral data analysis.
dc.identifier.citationHameed, A. A., Jamil, A., & Seyyedabbasi, A. (2024). An optimized feature selection approach using sand Cat Swarm optimization for hyperspectral image classification. Infrared Physics & Technology, 141, 105449.
dc.identifier.doi10.1016/j.infrared.2024.105449
dc.identifier.endpage14
dc.identifier.issn1350-4495
dc.identifier.issn1879-0275
dc.identifier.scopus2-s2.0-85199010931
dc.identifier.scopusqualityQ2
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1016/j.infrared.2024.105449
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6457
dc.identifier.volume141
dc.identifier.wosWOS:001274594200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorHameed, Alaa Ali
dc.institutionauthorSeyyedabbasi, Amir
dc.institutionauthoridAmir Seyyedabbasi / 0000-0001-5186-4499
dc.institutionauthoridAlaa Ali Hameed / 0000-0002-8514-9255
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofInfrared physics and technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectHyperspectral Image Classification
dc.subjectSand Cat Swarm Optimization Algorithm Optimization
dc.subjectBackpropagation With Variable Adaptive Momentum
dc.subjectFeature Selection
dc.titleAn optimized feature selection approach using sand Cat Swarm optimization for hyperspectral image classification
dc.typeArticle

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