Stacked Local Feature Detector for Hyperspectral Image

Abstract

Abstract. Images registration is an important task in hyperspectral image processing, while almost all local feature point based image matching algorithm is designed for single band image only and miss the advantage of abundant spectral information. Therefor, in this paper we propose a novel local feature detector for hyperspectral image. This method, which is named stacked local feature detector (HSI-SFD), stack all local feature points detected from every single spectral band. With redundant information, local feature miss-detected at noise pixel can be filtered out by the stack count threshold, leading to more reliable and robust local features. To verify the algorithm, a hyperspectral image matching dataset is built. Multiview hyperspectral images of the several different flat targets are taken by scan-line hyperspectral camera in library. Each image contains 270 bands within 400–1000 nm. Groundtruth transformation matrix between images is computed from corresponding points selected by hands. Experiment on the dataset shows that features points miss-detected at texture-less area can be inhibited by HSI-SFD. Both keypoint repeatability and matching accuracy increase significantly. Precision and matching score increase up to 10% for some scene.