Improved phase congruency based interest point detection for multispectral remote sensing images

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.