Combining pyramid representation and AdaBoost for urban scene classification using high-resolution synthetic aperture radar images

This study presents a new algorithm called pyramid representation (PR)-AdaBoost, which combines PR and AdaBoost for urban area classification using high-resolution synthetic aperture radar (SAR) images. PR is used to hierarchically represent local feature sets and AdaBoost is used to choose proper features from the PR vector and effectively discriminate categories. The authors evaluate the proposed algorithm on a data set consisting of high-resolution SAR images of five different categories of scene and on a real TerraSAR-X image. The experimental results have shown that PR-AdaBoost can achieve higher classification accuracy than AdaBoost based on global representation or local representation such as bag-of-features. It also outperforms classical classifiers such as nearest neighbour, boosted distance and support vector machine based on the same representation.