Ensemble of Support Vector Machines for spectral-spatial classification of hyperspectral and multispectral images

被引：0

作者：

Shad, Rouzbeh ^{[1
]}

Seyyed-Al-hosseini, Seyyed Tohid ^{[1
]}

Mehrani, Yaser Maghsoodi ^{[2
]}

Ghaemi, Marjan ^{[1
]}

机构：

[1] Ferdowsi Univ Mashhad, Engn Fac, Civil Engn Dept, Mashhad, Iran

[2] KN Toosi Univ Technol, Geodesy & Geomat Fac, Remote Sensing Dept, Tehran, Iran

来源：

MULTIMEDIA TOOLS AND APPLICATIONS | 2023年 / 82卷 / 27期

关键词：

Support vector machine; Spectral and spatial information; Direct summation of kernels; Weighted summation of kernels; Ensemble classifiers; Satellite images; MULTICLASS CLASSIFICATION;

D O I：

10.1007/s11042-023-14972-3

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：42119 / 42146

页数：28

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