Abstract:

In order to further improve the accuracy of river target detection in synthetic aperture radar (SAR) images, a method of river target detection based on multi-features and wavelet support vector machine (WSVM) in SAR images is proposed. Firstly, gray features of pixel neighborhood are represented by the mean ratioTexture features are extracted by Gabor wavelet. The training samples are constructed by fusion of the extracted gray features and texture features. Then, the normalized feature matrix is inputted into the WSVM for training. Each pixel in the images is classified by the trained WSVM. Finally, the similar regionals with rivers such as shadows, lakes are removed according to the regional connectivity, areas and shape features of rivers. A large number of experimental results show that compared with other methods of river target detection, the proposed method has more completely detection, error regions of classification are much less and edges of rivers are preserved better.