Abstract
Abstract. In this paper, we present an improved algorithm used for low altitude aerial image automatic matching based on SIFT operator. Compared to traditional photogrammetry based on platforms such as satellites and aerospace aircrafts, the platforms of low-altitude remote sensing system have relatively lighter weight, therefore existing rotation angle and scale differences in the stereo-images. In addition, there appears fracture lines and the discontinuities of parallax in the elevation undulating area. The characteristics above make it unsuitable for the traditional photogrammetry matching method based on grey scale correlation and the matching search strategy based on continuous parallax. In this paper an improved SIFT(Scale-invariant feature transform) operator is applied to the automatic matching of low-altitude aerial images. Several improvements were made to enhance the feature recurrence rate, matching correct rate and speed of matching. Firstly, we applied the theory of zero-crossing in SIFT feature extraction introducing the image geometry feature in scale space detection. Secondly, correlation coefficient is used as similarity measure instead of Euclidean distance in SIFT algorithm. Thirdly, we proposed a new matching strategy based on principal orientation constrain to shorten the search distance compared to the global search in SIFT algorithm. To demonstrate the feasibility of the approach, experiments were carried with four groups low-altitude remote sensing stereo-images from different sensors, and presented different distortions. Results showed that the improved algorithm has higher feature recurrence rate, matching correct rate and speed of matching dealing with different scale, large rotation angle, affine distortion and nonlinear distortion of low-altitude remote sensing stereo-images.
Highlights
These years, the rapid development of platform and system in low-altitude remote sensing result in many applications in quickly update of large-scale remote sensing image, stitch of panoramic images and construction of three-dimensional digital city model
In this paper SIFT operator is applied to the automatic matching of low-altitude aerial images
Feature extraction has taken on the four groups of images with the same SIFT algorithm, the image matching were taken by SIFT matching method based on minimum of Euclidean distance and improved matching method based on correlation coefficient with principal constrain
Summary
These years, the rapid development of platform and system in low-altitude remote sensing result in many applications in quickly update of large-scale remote sensing image, stitch of panoramic images and construction of three-dimensional digital city model. Compared to traditional photogrammetry based on platforms such as satellites and aerospace aircrafts, lowaltitude aerial remote sensing platforms such as airships and unmanned aerial vehicles are low cost, autonomous or remote control and relatively independent on the weather conditions. The relatively light weight of low-altitude remote sensing platforms results in poor flight stability, and existing rotation angle and image scale differences in the stereoimages. The characteristics above make it unsuitable for the traditional photogrammetry matching method based on grey value correlation and the matching strategy based on continuous parallax. Several improvements were made to enhance the feature recurrence rate, matching correct rate and speed of matching
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