Bag-of-Visual-Words Model for Classification of Interferometric SAR Images

摘要

This work introduces a well-accepted image representation model in image analysis, namely the Bag-of-Visual-Wordsrn(BoVW), to interferometric SAR (InSAR) images. As the low-level local features, Gabor- and fractional Fourierrntransform (FrFT)-based feature descriptors are used. The supervised classification results with BoVW-Gabor andrnBoVW-FrFT features are compared to those with global Gabor and global FrFT features. Although the global Gaborrnfeatures are better than the global FrFT features, by the implementation of BoVW model, FrFT outperforms Gaborrnfeatures. Also, the classification performances of different baseline acquisitions for the same scenes are compared. Forrneach baseline, the mean and individual class accuracies are improved by using BoVW-FrFT features.