Abstract
There are many applications for SAR image change detection, from military and agriculture to detection and management. But in fact, there is the speckle noise in SAR images inevitably. Therefore, the difficulty to detect change is increased. For purpose of reducing the interference of noise, we propose an unsupervised feature learning method using the non-negative matrix factorization algorithm and an improved sparse coding algorithm. First, non-negative matrix factorization method is used to obtain a dictionary which contains spatial structure information. Then, in order to increase the discriminate ability, we extract features for each pixel and apply sparse coding. Finally, the result of SAR image change detection is generated by applying simple k-means clustering method to divide the learned features into two different clusters. The superior performance of the proposed method is verified on several real SAR image datasets through comparisons with several existing change detection techniques.
SAR Image Change Detection Based on Data Optimization and Self-Supervised Learning