A New Neural Approach of Supervised Change Detection in SAR Images Using Training Data Generation with Concurrent Self-Organizing Maps

摘要

This paper proposes a new neural approach for supervised change detection in Synthetic Aperture Radar (SAR) images using Training Data Generation based on Concurrent Self-Organizing Maps (TDG-CSOM). The proposed model is based on the idea to substitute the authentically labeled sample set with the virtual training data generated by the CSOM system. The proposed change detection algorithm has the following processing stages: (a) TDG-CSOM; (b) Multi-Layer Perceptron (MLP) classifier training using the virtual dataset obtained by TDG-CSOM; (c) concatenation of the corresponding pixels belonging to the SAR bi-temporal image; (d) MLP classification to obtain the change map. The proposed method is implemented and evaluated using a TerraSAR-X image acquired in the Fukushima region, Japan, before and after tsunami. The experimental results confirm the effectiveness of the proposed approach.