Very high resolution image matching based on local features and k‐means clustering

Abstract

AbstractImage matching is a critical process in photogrammetry and remote sensing. Automatic and reliable feature matching using well‐distributed points in very high resolution images is a difficult task due to significant relief displacement caused by tall buildings and ground relief. In this paper a robust and efficient image‐matching approach is proposed, consisting of two main steps. In the first step, three sets of local features – Harris points, UR‐SIFT and MSER – are extracted over the entire image. A SIFT (scale‐invariant feature transform) descriptor is then created for each extracted feature, and an initial cross‐matching verification is performed using the Euclidean distance between feature descriptors. In the second step, an approach based on k‐means clustering is performed to achieve accurate matching without mismatched features, followed by a consistency check using a local affine transformation model for each cluster. The proposed method is successfully applied to matching various aerial and satellite images and the results demonstrate its robustness and capability.