Open Set SAR Target Recognition Using Class Boundary Extracting

Typically, synthetic aperture radar (SAR) target recognition uses supervised learning techniques to identify target types. A problem is when identifying the new untrained classes and the false targets, they will be misjudged as known classes. In this paper, an open set recognition method is proposed to provide a rejection option for classifier so that the new untrained target type is identified. The main idea is to model closed category boundaries for the known classes and detect the new classes beyond the reasonable scope of known data. The edge exemplar selection method based on local statistical information is proposed to extract the class boundaries. And the boundaries are used to train the open set recognition/outlier detection model. Experimental results demonstrate that the proposed O_SAR outperforms the other state of the arts on the accuracy of false target rejection and muti-class open set recognition.