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.

Date Published: 28 October 2006
PDF: 7 pages
Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 64191M (28 October 2006); doi: 10.1117/12.713260

Published in SPIE Proceedings Vol. 6419:
Geoinformatics 2006: Remotely Sensed Data and Information
Liangpei Zhang; Xiaoling Chen, Editor(s)