Abstract
Abstract. Automatic image registration is a vital yet challenging task, particularly for multi-sensor remote sensing images. Given the diversity of the data, it is unlikely that a single registration algorithm or a single image feature will work satisfactorily for all applications. Focusing on this issue, the mainly contribution of this paper is to propose an automatic optical-to-SAR image registration method using –level and refinement model: Firstly, a multi-level strategy of coarse-to-fine registration is presented, the visual saliency features is used to acquire coarse registration, and then specific area and line features are used to refine the registration result, after that, sub-pixel matching is applied using KNN Graph. Secondly, an iterative strategy that involves adaptive parameter adjustment for re-extracting and re-matching features is presented. Considering the fact that almost all feature-based registration methods rely on feature extraction results, the iterative strategy improve the robustness of feature matching. And all parameters can be automatically and adaptively adjusted in the iterative procedure. Thirdly, a uniform level set segmentation model for optical and SAR images is presented to segment conjugate features, and Voronoi diagram is introduced into Spectral Point Matching (VSPM) to further enhance the matching accuracy between two sets of matching points. Experimental results show that the proposed method can effectively and robustly generate sufficient, reliable point pairs and provide accurate registration.
Highlights
Image registration is the process of transforming the different set of data into one coordinate system, may be said as the process of overlaying two or more images of the same scene taken at different times, from different viewpoints or from different sensors (Dawn et al, 20100)
To avoid failed registration caused by poor feature extraction, we propose a simultaneous feature extraction and matching method using an iterative and refinement model for optical and Synthetic Aperture Radar (SAR) images
Sift matching method is applied on segmented optical image and rough registered SAR image
Summary
Image registration is the process of transforming the different set of data into one coordinate system, may be said as the process of overlaying two or more images of the same scene taken at different times, from different viewpoints or from different sensors (Dawn et al, 20100). In contrast to intensity-based methods, the featurebased ones do not work directly with image intensity values These approaches are based on the extraction of the salient structures and features of images. (2) The common theme in all of these featurebased matching techniques is that each method relies on a single feature extraction algorithm for extracting the primitives to be matched. Based on the above problems, an automatic optical-to-SAR image registration method using multi-level, iterative and refinement model is proposed. To avoid failed registration caused by poor feature extraction, we propose a simultaneous feature extraction and matching method using an iterative and refinement model for optical and SAR images. A multi-level framework that provides coarse-to-fine registration behaved as local feature (visual salience feature) geometry feature (area and line features) intensity feature is proposed to refine the result step by step. The experiments show that by using multiple feature extraction and feature matching algorithms, the precision and reliability of matching results can be significantly increased
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