Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/16159
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dc.contributor.authorSuresh S.
dc.contributor.authorLal S.
dc.date.accessioned2021-05-05T10:29:52Z-
dc.date.available2021-05-05T10:29:52Z-
dc.date.issued2020
dc.identifier.citationInfrared Physics and Technology , Vol. 105 , , p. -en_US
dc.identifier.urihttps://doi.org/10.1016/j.infrared.2019.103172
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/16159-
dc.description.abstractLand cover classification of satellite images has been a very predominant area since the last few years. An increase in the amount of information acquired by satellite imaging systems, urges the need for automatic tools for classification. Satellite images exhibit spatial and/or temporal dependencies in which the conventional machine learning algorithms fail to perform well. In this paper, we propose an improved framework for automated land cover classification using Spatial Spectral Schroedinger Eigenmaps (SSSE) optimized by Cuckoo Search (CS) algorithm. Support Vector Machine (SVM) is adopted for the final thematic map generation following dimensionality reduction and clustering by the proposed approach. The novelty of the proposed framework is that the applicability of optimized SSSE for land cover classification of medium and high resolution multi-spectral satellite images is tested for the first time. The proposed method makes land cover classification system fully automatic by optimizing the algorithm specific image dependent parameter α using CS algorithm. Experiments are carried out over publicly available high and medium resolution multi-spectral satellite image datasets (Landsat 5 TM and IKONOS 2 MS) and hyper-spectral satellite image datasets (Pavia University and Indian Pines) to assess the robustness of the proposed approach. Performance comparisons of the proposed method against state-of-the-art multi-spectral and hyper-spectral land cover classification methods reveal the efficiency of the proposed method. © 2020 Elsevier B.V.en_US
dc.titleA metaheuristic framework based automated Spatial-Spectral graph for land cover classification from multispectral and hyperspectral satellite imagesen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

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