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.
To cope with the rapid development of the real applications for infrared small targets, the researchers have tried their best to pursue more robust detection methods. At present, the contrast measure-based method has become a promising research branch. Following the framework, in this paper, a speeded-up contrast measure scheme is proposed based on the saliency detection and density clustering. First, the saliency region is segmented by saliency detection method, and then, the Multi-scale contrast calculation is carried out on it instead of traversing the whole image. Second, the target with a certain “integrity” property in spatial is exploited to distinguish the target from the isolated noises by density clustering. Finally, the targets are detected by a self-adaptation threshold. Compared with time-consuming MPCM (Multiscale Patch Contrast Map), the time cost of the speeded-up version is within a few seconds. Additional, due to the use of “clustering segmentation”, the false alarm caused by heavy noises can be restrained to a lower level. The experiments show that our method has a satisfied FASR (False alarm suppression ratio) and real-time performance compared with the state-of-art algorithms no matter in cloudy sky or sea-sky background.
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