Title : ( Ensemble of Support Vector Machines for spectral-spatial classification of hyperspectral and multispectral images )
Authors: Rouzbeh Shad , seyyed tohid seyyedalhosseini , Yaser Maghsoudi , Marjan Ghaemi ,
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Abstract
Previous studies on different satellite images have not yet introduced a single attribute with the highest accuracy for different applications. In this paper, a novel classification system with the highest strength against possible noises is offered using Support Vector Machine (SVM) and its performance is evaluated on the selected satellite images. So, an optimal high-strength classifier with the sufficient level of accuracy is proposed executing Composite Kernels and Ensemble of Classifiers. Results obtained from applying this method on IKONOS (91.65%) and AVIRIS (97.71%) satellite images (in Tehran and Indian Pine study areas) showed that the proposed method accuracy is higher than the Direct Summation of Kernels, Weighted Summation of Kernels, Cross Information Kernels and Extracted Features techniques. The main reason for this significant difference is the wide range and variety of input features.
Keywords
Support vector machine . Spectral and spatial information . Direct summation of kernels. Weighted summation of kernels. Ensemble classifiers. Satellite images
@article{paperid:1093993,
author = {Shad, Rouzbeh and Seyyedalhosseini, Seyyed Tohid and یاسر مقصودی and مرجان قائمی},
title = {Ensemble of Support Vector Machines for spectral-spatial classification of hyperspectral and multispectral images},
journal = {Multimedia Tools and Applications},
year = {2023},
volume = {82},
number = {27},
month = {November},
issn = {1380-7501},
pages = {42119--42146},
numpages = {27},
keywords = {Support vector machine . Spectral and spatial information . Direct summation of
kernels. Weighted summation of kernels. Ensemble classifiers. Satellite images},
}
%0 Journal Article
%T Ensemble of Support Vector Machines for spectral-spatial classification of hyperspectral and multispectral images
%A Shad, Rouzbeh
%A Seyyedalhosseini, Seyyed Tohid
%A یاسر مقصودی
%A مرجان قائمی
%J Multimedia Tools and Applications
%@ 1380-7501
%D 2023
