Multi-sensor image matching based on salient edges has broad prospect in applications, but it is difficult to extract salient edges of real multi-sensor images with noises fast and accurately by using common algorithms. According to the analysis of the features of salient edges, a novel salient edges detection algorithm and its rapid calculation are proposed based on possibility fuzzy C-means (PFCM) kernel clustering using two-dimensional vectors composed of the values of gray and texture. PFCM clustering can overcome the shortcomings that fuzzy C-means (FCM) clustering is sensitive to noises and possibility C-means (PCM) clustering tends to find identical clusters. On this basis, a method is proposed to improve real-time performance by compressing data sets based on the idea of data reduction in the field of mathematical analysis. In addition, the idea that kernel-space is linearly separable is used to enhance robustness further. Experimental results show that this method extracts salient edges for real multi-sensor images with noises more accurately than the algorithm based on force fields and the FCM algorithm; and the proposed method is on average about 56 times faster than the PFCM algorithm in real time and has better robustness. © 2014 Production and hosting by Elsevier Ltd. on behalf of CSAA and BUAA.
Xu, G., Zhao, Y., Guo, R., Wang, B., Tian, Y., & Li, K. (2014). A salient edges detection algorithm of multi-sensor images and its rapid calculation based on PFCM kernel clustering. Chinese Journal of Aeronautics, 27(1), 102–109. https://doi.org/10.1016/j.cja.2013.12.001