High resolution polarimetric SAR target classification with neural network

An improved version of the SOFM/LVQ classifier currently used in an ATR system for SAR imagery is presented. This classifier was originally designed to construct a few number of templates to represent a set of targets with different orientations. The classifier accepts an input target data, computes distances of this data with those representative templates, and then classifies this data to the target class with the shortest distance, With this distance discriminator, a good classification performance was obtained when only target data were tested. However, the simple distance measure produces poor classification results when unknown targets such as natural or manmade clutters are present and when each target is represented by a small number of templates. We correct this deficiency by incorporating an entropy measure into the original classifier. With this entropy discriminator, our system rejects a majority of the false alarms while maintaining a high correct classification rate with a relatively few templates for each target