A Self-organizing Principle for Segmenting and Super-resolving ISAR Images

We present and illustrate the use of a bottleneck system for the segmentation and super-resolution of ISAR targets. The system is shown to be comprised of three basic subsystems: a compressing transformation, a bottleneck processor, and a decompressing transformation. We describe each subsystem and discuss the processing responsible for segmentation and super-resolution within this framework. Results using this network are assessed and issues regarding performance are introduced. 1. Introduction Feature extraction is critical in many signal and image processing applications but our ability to automatically extract features from data is very limited. In preselecting features, we rely too much and too often on our apriori knowledge of the problem. This methodology can be problematic when such apriori knowledge is scarce and also hinders our ability to quantify the quality of the features chosen. In our opinion, feature extraction should be based on self- organizing methods because the..