The small target detection based on local directional contrast associated with directional entropy

Abstract

As the tough problem in ATR (Automatic Target Recognition), the detection of small targets in complex background is an open topic for the lack of sufficient information. Following the technical route of contrast feature, in this paper, the directivity is integrated into the metric of contrast associated with entropy to enhance the target information as well as to suppress the stubborn clutter edge. To speed up the algorithm, the Spectral Residual Approach is introduced to extract ROI (Region of Interest), and then DCDEMap (Directional Contrast and Directional Entropy Map) is created by the re-measurement of contrast and entropy based on the partition of the sliding window in 8 directions. Additional, in order to provide a more adaptive detection threshold for multiple targets in heterogeneous background, the original image is segmented into multiple homogeneous parts by OTSU. Later, the global statistical information from DCDEMap is introduced into the threshold decision process, thereby avoiding the false alarm. The experiments demonstrate that the proposed method has satisfactory performance in Pd (Probability of detection) and Fa (False alarm rate) for various scenarios compared with the state of art algorithms, especially for the clutter edge, it shows prominent suppression ability.