Abstract
Improving the matching reliability of low-altitude images is one of the most challenging issues in recent years, particularly for images with large viewpoint variation. In this study, an approach for low-altitude remote sensing image matching that is robust to the geometric transformation caused by viewpoint change is proposed. First, multiresolution local regions are extracted from the images and each local region is normalized to a circular area based on a transformation. Second, interest points are detected and clustered into local regions. The feature area of each interest point is determined under the constraint of the local region which the point belongs to. Then, a descriptor is computed for each interest point by using the classical scale invariant feature transform (SIFT). Finally, a feature matching strategy is proposed on the basis of feature similarity confidence to obtain reliable matches. Experimental results show that the proposed method provides significant improvements in the number of correct matches compared with other traditional methods.
Highlights
In recent years, comprehensive low-altitude remote sensing platforms, such as unmanned aerial vehicle (UAV), have provided new possibilities for high-resolution image acquisition and have been extensively used in many applications (Bulatov et al, 2011; Choi et al, 2011; Colomina et al, 2014; Wallace et al, 2014a, 2014b; Goncalves et al, 2015; Zhou et al, 2015)
The ASIFT method can improve the matching performance under view change, its application is limited because the transformation between the inputted images is not considered and an exhaustive strategy is needed for the feature search
We proposed a novel point feature matching method for low-altitude remote sensing images based on the analysis of image geometric transformation
Summary
Comprehensive low-altitude remote sensing platforms, such as unmanned aerial vehicle (UAV), have provided new possibilities for high-resolution image acquisition and have been extensively used in many applications (Bulatov et al, 2011; Choi et al, 2011; Colomina et al, 2014; Wallace et al, 2014a, 2014b; Goncalves et al, 2015; Zhou et al, 2015). Compared with traditional approaches (e.g., satellite remote sensing and aerial photogrammetry), low-altitude remote sensing platforms have the following inherent advantages: first, the work mode is flexible, efficient, and less affected by weather, and they can take off any time as tasked; second, they are able to obtain large-scale and high-precision remote sensing images; and third, the overlapping degree between images is relatively large It can enhance the reliability of the subsequent processing. In addition to cross-correlation, researchers have proposed a class of frequency domain matching methods based on Fourier spectrum (Zitova et al, 2003) This type of method searches the best match by using image frequency domain information. The advantages and disadvantages of the proposed method and further improvements that can be made
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