scholarly journals Improved Parameter Estimation of the Line-Based Transformation Model for Remote Sensing Image Registration

2017 ◽  
Vol 3 (3) ◽  
pp. 32
Author(s):  
◽  
◽  
Keyword(s):  
Remote Sensing ◽  
Parameter Estimation ◽  
Image Registration ◽  
Remote Sensing Image ◽  
Transformation Model

scholarly journals A Robust False Matching Points Detection Method for Remote Sensing Image Registration

2015 ◽  
Vol XL-7/W3 ◽  
pp. 699-702
Author(s):  
X. J. Shan ◽  
P. Tang
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Detection Method ◽  
Graph Transformation ◽  
Remote Sensing Image ◽  
Transformation Model ◽  
Superior Performance ◽  
Geometric Distortion ◽  
Remote Sensing Images ◽  
Accuracy Result

Given the influences of illumination, imaging angle, and geometric distortion, among others, false matching points still occur in all image registration algorithms. Therefore, false matching points detection is an important step in remote sensing image registration. Random Sample Consensus (RANSAC) is typically used to detect false matching points. However, RANSAC method cannot detect all false matching points in some remote sensing images. Therefore, a robust false matching points detection method based on Knearest- neighbour (K-NN) graph (KGD) is proposed in this method to obtain robust and high accuracy result. The KGD method starts with the construction of the K-NN graph in one image. K-NN graph can be first generated for each matching points and its K nearest matching points. Local transformation model for each matching point is then obtained by using its K nearest matching points. The error of each matching point is computed by using its transformation model. Last, L matching points with largest error are identified false matching points and removed. This process is iterative until all errors are smaller than the given threshold. In addition, KGD method can be used in combination with other methods, such as RANSAC. Several remote sensing images with different resolutions and terrains are used in the experiment. We evaluate the performance of KGD method, RANSAC + KGD method, RANSAC, and Graph Transformation Matching (GTM). The experimental results demonstrate the superior performance of the KGD and RANSAC + KGD methods.

A Hybrid Method for Multi-sensor Remote Sensing Image Registration Based on Salience Region

2014 ◽  
Vol 33 (7) ◽  
pp. 2293-2317 ◽  
Author(s):  
Jichao Jiao ◽  
Zhongliang Deng ◽  
Baojun Zhao ◽  
John Femiani ◽  
Xin Wang
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Hybrid Method ◽  
Remote Sensing Image
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