Discovering Novel Categories in Sar Images in Open Set Conditions

Abstract

In this paper, we deal with the issue of discovering data of novel categories for Synthetic Aperture Radar (SAR) images under open-set conditions. The traditional SAR image classification methods are trained under the closed-set setting where all categories in testing data are seen in training data. It does not always meet the requirements of the real SAR imagery interpretation applications. With a labelled SAR image dataset, we propose a multi-stage approach to effectively pick out images belonging to new classes in another unlabelled dataset and then cluster them into correct number of novel categories. To do so, our pipeline is composed of three major steps: (1) train a powerful feature extractor leveraging both the labelled and unlabelled dataset by semi-supervised inference; (2) identify the unknown data by openset detection; (3) cluster these unknown data based on the features generated by the extractor to discover novel categories. The proposed method is validated on a Sentinel-1 SAR image dataset OpenSARUrban [1].