Efficient and Robust Feature Matching for High-Resolution Satellite Stereos

Abstract

Feature matching between high-resolution satellite stereos plays an important role in satellite image orientation. However, images of changed regions, weak-textured regions and occluded regions may generate low-quality matches or even mismatches. Furthermore, matching throughout the entire satellite images often has extremely high time cost. To compute good matching results at low time cost, this paper proposes an image block selection method for high-resolution satellite stereos, which processes feature matching in several optimal blocks instead of the entire images. The core of the method is to formulate the block selection into the optimization of an energy function, and a greedy strategy is designed to compute an approximate solution. The experimental comparisons on various satellite stereos show that the proposed method could achieve similar matching accuracy and much lower time cost when compared with some state-of-the-art satellite image matching methods. Thus, the proposed method is a good compromise between matching accuracy and matching time, which has great potential in large-scale satellite applications.

New Method for Reduced-Number IMU Estimation in Observing Human Joint Motion

“…Each method used normalized cross-correlation as the core technique. The pattern-matching method consisted of two stages: learning and matching [31][32][33]. First, the…”

“…Each method used normalized cross-correlation as the core technique. The pattern-matching method consisted of two stages: learning and matching [ 31 , 32 , 33 ]. First, the formula extracted gray worth and/or edge gradient information from the model image throughout the educational stage.…”