Hyperspectral remote sensing image classification based on combined SVM and LDA

Chunsen Zhang; Yiwei Zheng

doi:10.1117/12.2070688

26 November 2014 Hyperspectral remote sensing image classification based on combined SVM and LDA

Chunsen Zhang, Yiwei Zheng

Proceedings Volume 9263, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V; 92632P (2014) https://doi.org/10.1117/12.2070688
Event: SPIE Asia-Pacific Remote Sensing, 2014, Beijing, China

Abstract

This paper presents a novel method for hyperspectral image classification based on the minimum noise fraction (MNF) and an approach combining support vector machine (SVM) and linear discriminant analysis (LDA). A new SVM/LDA algorithm is used for the classification. First, we use MNF method to reduce the dimension and extract features of the image, and then use the SVM/LDA algorithm to transform the extracted features. Next, we train the result of transformation, optimize the parameters through cross-validation and grid search method, then get a optimal hyperspectral image classifier. Finally, we use this classifier to complete classification. In order to verify the proposed method, the AVIRIS Indian Pines image was used. The experimental results show that the proposed method can solve the contradiction between the small amount of samples and high dimension, improve classification accuracy compared to the classical SVM method.

Citation Download Citation

Chunsen Zhang and Yiwei Zheng "Hyperspectral remote sensing image classification based on combined SVM and LDA", Proc. SPIE 9263, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V, 92632P (26 November 2014); https://doi.org/10.1117/12.2070688

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