RPC estimation via feature points for urban areas

Abstract

Rational function model (RFM), which is composed of 80 rational polynomial coefficients (RPCs), has been widely used to establish a functional relationship between the image space and the object space in photogrammetry and remote sensing. In order to gain precise RPCs, a set of ground control points (GCPs) need to be selected. And in all most applications, many GCPs selected are located in the center of one object in order to increase the measurement precision. Note that, for a special area, such as urban areas which consist of many buildings, have many feature points which are usually located in the corner. This paper proposes an improved method using feature points instead of non-feature points to solve RPCs. Evaluation of aerial data shows that the RPCs can reach a high fitting accuracy by means of these feature points as GCPs, more than those using non-feature points. An important advantage is that this method can enhance edge protection of buildings, so that it can applied in subsequent applications, such as shadow removing, target recognition and so on. Moreover, this method can remain computation accuracy of buildings even if the number of GCPs decreases.