JBC: Joint Boost Clustering method for synthesis aperture radar images

摘要

A clustering method based on Joint Boost for Synthesis Aperture Radar images is proposed. In this method, we follow the steps of Joint Boost, but substitute weak learns with basic clustering algorithm. We compute the sharing features between samples in order to reduce clustering times. The proposed clustering method, JBC constructs a new training set by random sampling from the original dataset, then selects the best feature and the best clusters for sharing, and calculates a distribution over the training samples using current shared feature and clusters, and finally a basic clustering algorithm (e.g. K-mean) is applied to partition the new training set. The final clustering solution is produced by aggregating the obtained partitions. The clustering results for SAR images show that the proposed method has a good performance.