One of the biggest challenges in multispectral image interest point detection is the variation of radiation. Many methods have been proposed to address this problem. However, the detection performance is still unstable. In this paper, a robust point detector is proposed. Firstly, image illumination space is constructed by using a parameters adaptive method. Secondly, a phase congruency based interest point detection algorithm is adopted to compute candidate points in illumination space. Then, all interest point candidates are mapped back to the original image and a non-maximum suppression step is added to find final interest points. Finally, the feature scale values of all interest points are calculated based on the Laplacian function. The experimental results show that the proposed method performs better than other traditional methods in feature repeatability rate and repeated features number for multispectral images.
Illumination variability is one of the most important issues affecting imagery matching performances and still remains a critical problem in the literature, although different levels of improvement have been reported in recent years. This study proposes an illumination robust image matching method. There are three steps in the proposed method: first, local regions are extracted and matched from the input images by using a multiresolution region detector and an illumination robust shape descriptor; second, an algorithm is proposed to estimate the overlapping areas of images and enhance them based on the region matches; finally, general feature detectors and descriptors are combined to process the previous results for illumination robust matching. Experimental results demonstrate that the proposed matching method provides significant improvement in robustness for illumination change images compared with traditional methods.