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This paper proposed a novel local feature descriptor for multisource remote sensing image matching that is robust to significant geometric and illumination differences. In the proposed registration method, traditional SIFT algorithm is applied for local feature extraction and a novel descriptor, named local order pattern based self-similarity descriptor, LOPSS descriptor, is constructed for each extracted...
High-precision and robust matching of multi-source remote sensing image matching is not easy to achieve because of non-linear intensity differences and significant geometric distortions. A new registration method is proposed by integrating the scale-invariant feature transform (SIFT) and optimization of local self-similarity mutual information (LSS_MI). This method consists of two main steps. In the...
Aiming at the high accuracy and speed requirements of images registration for multiband data or hyperspectral data, a new method which combines scale invariant feature transform (SIFT) with vegetation index analysis is put forward. Firstly, feature points extracted by SIFT algorithm are classified into two sets — points on vegetation area and points on non-vegetation area, which is based on vegetation...
This paper proposes a new remote sensing image algorithm based on virtual triangle similarity, position control and mutual information constraint (VIS-PCMIC), aiming at the optical image automatic registration of affine transformation. This algorithm can be simply defined into three steps. Firstly, the initial matching feature points and transformation model parameters are obtained according to the...
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