A novel locally active learning method for SAR image classification
Tengchuan Wang, Yuanxiang Li, Huilin Xiong · 2014
In this paper, we present a novel locally active learning method for synthetic aperture radar (SAR) image classification. This method aims at reducing the labeling acquisition cost but at the same time retaining the classification accuracy. Based on active learning framework, the most informative samples are selected so that the required number of samples can be reduced greatly. At each iteration, we use local area as the candidates for choosing the training samples so that the ground survey is easy to take and thus the time and cost for labeling could be further reduced. The experiments on TerraSAR-X SAR images show that the proposed method obtains a promising performance for SAR image classification.