DEM orientation based on local feature correspondence with global DEMs

Abstract

Thanks to rational polynomial coefficients (RPCs), which are provided by vendors to end users, digital elevation models (DEMs) can be simply derived from satellite stereo images. However, DEMs are influenced by systematic errors in the rational function model (RFM), known as RPC biases. Global DEMs (GDEMs), such as the Shuttle Radar Topography Mission (SRTM), which is the most inexpensive solution, can be applied to improve the accuracy of the relative RFM-derived DEMs. In this article, an automatic and robust local feature-based DEM matching and orientation approach is proposed in order to improve the accuracy of the relative RFM-derived DEMs without the use of ground control points (GCPs). The proposed approach consists of four main steps: (1) combined local feature extraction; (2) computation of the distinctive order-based self-similarity (DOBSS) descriptor; (3) a feature correspondence and local consistency checking process; and (4) a relative RFM-derived DEM orientation process using three-dimensional (3D) transformation models, including 3D rigid, 3D similarity and 3D affine transformations. This technique can avoid the sensitivity of conventional 3D DEM matching methods to initial values, monotonous areas and local distortions. Experimental results on two CARTOSAT-1 derived DEMs demonstrate the superior performance of the proposed DEM matching method over state-of-the-art methods, including SIFT, DAISY, LIOP, LBP, and BRISK descriptors, in terms of the number of correct matches (NCM) and DEM orientation accuracy. The results also show that the proposed method is able to significantly improve the geometric accuracy of the relative RFM-derived DEMs.