Small targets in infrared images are usually detected by using the relative characteristics of small targets and backgrounds. Compared with conventional descriptors, detectability of infrared small targets (DIST) is a better metric of the relative characteristics of small targets and backgrounds. However, the quantification method of DIST cannot describe the situation of darker targets in brighter background, which is common due to the intense reflection of sunlight. Moreover, its value is not normalized, so that it is not well suited to evaluate the degree of difference between small targets and backgrounds. An improved quantification method of DIST is proposed to address these problems, and a detecting method is developed based on the improved DIST. The experimental results show the validity of the improved quantification approach of DIST.
This paper presents a suited approach to deal with the template coarse matching of synthetic aperture radar (SAR) and
optical images based on wavelet subbands and Hausdorff distance. Firstly, we analyze the discrepancy of imaging
character between SAR and optical images. Secondly, we occupy the max flat wavelet decomposition to obtain the
template images, which also can suppress the speckle in SAR. Thirdly, improved canny algorithm is employed to extract
the edge feature of SAR and optical images after image enhancement, respectively. Finally, this paper applies the
modified Hausdorff distance with the optimum coefficient to the similarity measurement for the matching. The
experimental results demonstrate the validity of the proposed methodology.