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28 October 2006 Remote sensing image texture classification based on Gabor wavelet and support vector machine
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Proceedings Volume 6419, Geoinformatics 2006: Remotely Sensed Data and Information; 64191M (2006) https://doi.org/10.1117/12.713260
Event: Geoinformatics 2006: GNSS and Integrated Geospatial Applications, 2006, Wuhan, China
Abstract
In this paper, a novel algorithm based on Gabor-Wavelet and SVM is proposed. Compared to other algorithms of classification, our method fully exploited the external feature of remote sensing (RS) images extracted by Gabor-Wavelet. In detail, our algorithm can be divided into the following steps. Gabor wavelet decomposition is firstly applied to the fused RS images. The experiment shows that choosing 5 frequencies and 7 phases, that is, 35 Kernel functions, may reach good performance. Then we get a feature vector with several dimensions. Considering that Gabor filtering is not a orthogonal decomposition, principle component analysis (PCA) is used to reduce redundancy. Secondly, feature vectors are divided into two parts- training set and testing set. The training set is put into SVM classifier. The effect of the algorithm for different RS image type was compared. The result shows that our method performs better with fused images, only panchromatic images or multispectral images can not provide enough information for RS image classification.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
LeiGuang Wang, Wenbo Wu, QinLing Dai, and Qianqing Qin "Remote sensing image texture classification based on Gabor wavelet and support vector machine", Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 64191M (28 October 2006); https://doi.org/10.1117/12.713260
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