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Automatic Registration of Coastal Remotely Sensed Imagery by Affine Invariant Feature Matching with Shoreline Constraint

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A new approach based on Affine Invariant Feature Matching (AIFM) with a filtering technique is proposed for automatic registration of remotely sensed image in coastal areas. The novelty of this approach is an automatic filtering technique using RANdom SAmple Consensus (RANSAC) with shoreline constraint for AIFM to remove all wrong matches and simultaneously keep as many correct matches as possible. To implement it, a progressive threshold strategy (from small value to large value) is presented to determine an appropriate RANSAC threshold, in which the progressive process is guided by shoreline constraint. The proposed approach (with filtering) is compared with standard AIFM (without filtering) using two typical image pairs in coastal areas. The experimental results indicate that the proposed approach can always provide much better matching results than standard AIFM.
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Keywords: Coastal remote sensing; affine invariant matching; filtering; image registration; shoreline constraint

Document Type: Research Article

Affiliations: Jiangsu Provincial Key Laboratory of Geographical Information Science and Technology, Nanjing University, Nanjing, China

Publication date: January 2, 2014

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