Epipolar Resampling of Cross-Track Pushbroom Satellite Imagery Using the Rigorous Sensor Model

Epipolar resampling aims to eliminate the vertical parallax of stereo images. Due to the dynamic nature of the exterior orientation parameters of linear pushbroom satellite imagery and the complexity of reconstructing the epipolar geometry using rigorous sensor models, so far, no epipolar resampling approach has been proposed based on these models. In this paper for the first time it is shown that the orientation of the instantaneous baseline (IB) of conjugate image points (CIPs) in the linear pushbroom satellite imagery can be modeled with high precision in terms of the rows- and the columns-number of CIPs. Taking advantage of this feature, a novel approach is then presented for epipolar resampling of cross-track linear pushbroom satellite imagery. The proposed method is based on the rigorous sensor model. As the instantaneous position of sensors remains fixed, the digital elevation model of the area of interest is not required in the resampling process. Experimental results obtained from two pairs of SPOT and one pair of RapidEye stereo imagery with different terrain conditions shows that the proposed epipolar resampling approach benefits from a superior accuracy, as the remained vertical parallaxes of all CIPs in the normalized images are close to zero

Improvement of linear spectral unmixing results using over-shoot pixels (case study: URMIA lake basin)

End-member extraction could be considered as the most challenging stage of the spectral unmixing process. In this study, a new approach is proposed based on error analysis of Linear Spectral Mixture Model (LSMM) to extract optimal pure pixels. First a number of approximate end-members are identified visually or using N-Finder algorithm then LSMM is applied to identify pixels with proportions greater than one (over-shoots). Over-shoots are then replaced with initial end-members and the LSMM is performed again. This process is continued until reduction of the number of overshoot and under-shoot pixels below 5% of total image pixels. According to the results, the proposed end-member extraction approach satisfies this criterion within a few iterations (2 or 3 runs). The total numbers of under/over shoots are estimated 4.17% and 3.55% of total image pixels respectively for choosing the initial end-members visually and by means of N-Finder algorithm