Interactive clustering for SAR image understanding

Abstract:
The increasing amount of high resolution Earth Observation (EO) data during recent years, has brought the content analysis of the provided data into the spotlight. Most of the current content analysis is based on unsupervised methods (e.g., clustering). However, the structure discovered by these methods is not necessarily human understandable. Moreover, they require some prior knowledge of the structure of the data for initialization. In this paper, we propose an interactive method to discover the semantic structure behind SAR image collections. Thus, we use a modified version of k-means, namely weight-balanced k-means, to perform clustering on the given images. The interaction mechanism allows users to provide the clustering method with relevant knowledge about the structure of the data. Experimental results demonstrate that the structure discovered by the proposed interactive method is closer to human understanding of the data.