Three-dimensional reconstruction of multiplatform stereo data with variance component estimation

In this paper, we address a problem of 3-D reconstruction with generalized stereo data from multiple platforms of remote sensing. Nowadays, rational function model (RFM)-based 3-D reconstruction with stereo images obtained from a single platform of remote sensing like a satellite or an airborne platform has been widely investigated, but there are little attentions to be paid to the problem of 3-D reconstruction with stereo images from multiple platforms in the existing literature. In order to make full use of the generalized stereo images from different platforms with different rigorous sensor models for 3-D reconstruction, we need to form the least squares estimation model of the corresponding RFM-based forward-intersection task after collecting observations from different platforms. However, resolutions of the stereo images from different platforms are greatly different so that the observations in the corresponding least squares problem are mathematically seriously unbalanced. To solve this problem for achieving precise reconstruction, we first model how the spatial resolution of the observation images of different platforms changes pixel by pixel and then embed the variance-component-estimation technique into the RFM-based 3-D reconstruction procedure to adaptively adjust weights for different observations. Experiments are conducted on simulated and real data sets. Experimental results show that the proposed algorithm can efficiently fulfill the 3-D reconstruction task for multiplatform stereo images with noticeable improvement over the classical RFM-based 3-D reconstruction method in terms of precision.