SAR images classification using case-based reasoning method

In this paper, we investigate a case-based reasoning (CBR) method for the classification of multi-temporal SAR images with the aid of ancillary information. Our scheme for the problem of multi-temporal SAR images classification comprises four main steps, including SAR image processing, construction of case library, case-based classification and post- classification processing. During the construction of case library, we employ a spatial-temporal analysis technique to remove fake cases, which can guarantee cases with high confidence. In the implementation of case-based classification, we propose a similarity assessment and use it for the case-based matching. After that, we investigate an object-oriented post-classification method which takes the shape of land use region into account, as a result, it leads to a more meaningful classification, and the regenerate land use image or map can be easier compared and combined with usual GIS data. Multi-temporal ENVISAT ASAR images from 2004 to 2005 are used in our experiments, where their resolution are 12.5 times 12.5 m. The study site is located in Beijing, China. During our experiments, we use the land use map of 2004 to assist the construction of the case library. The results of our experiments indicate that the CBR method is very promising for the classification of multi-temporal SAR images, where the overall classification accuracy can reach up to 80%.