Object detection with few training data: Detection of subsiding troughs in SAR interferograms

Abstract

Subsiding troughs that are the result of mining activities can be detected in SAR interferograms as approximately elliptic shapes against the noisy background. Despite large areas being covered by interferogram, the number of positive samples, which can be used for automatic learning, is limited. In this paper we propose two alternative methods for the detection of subsiding troughs: the first one is designed to detect any circular shapes and does not require any learning set and the second is based on automatic learning but requires a reduced number of positive samples. The two proposed methods can support manual inspection of large areas in SAR interferograms.