COMPARATIVE EVALUATION OF VECTOR MACHINE BASED HYPERSPECTRAL CLASSIFICATION METHODS

被引：6

作者：

Karaca, Ali Can ^{[1
]}

Erturk, Alp ^{[1
]}

Gullu, M. Kemal ^{[1
]}

Erturk, Sarp ^{[1
]}

机构：

[1] Kocaeli Univ, Lab Image & Signal Proc KULIS, Elect & Telecomm Eng Dept, Kocaeli, Turkey

来源：

2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2012年

关键词：

Import Vector Machines; Support Vector Machines; Relevance Vector Machines; Hyperspectral Classification;

D O I：

10.1109/IGARSS.2012.6352496

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents a comparison of the classification performance of some vector machine based classification methods, namely, Import Vector Machines (IVM), Support Vector Machines (SVM) and Relevance Vector Machines (RVM), for hyperspectral images. Evaluation is carried out in terms of the number of vectors and classification accuracies. Furthermore, novel to this paper, Discriminative Random Field method with Graph Cut algorithm is applied to the probabilistic classification output of IVM based hyperspectral classification results, and it is shown that this approach significantly increases classification accuracies.

引用

页码：4970 / 4973

页数：4

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