Fast-match is a fast and effective algorithm for template matching. However, when matching colour images, the images are converted into greyscale images. The colour information is lost in this process, resulting in errors in areas with distinctive colours but similar greyscale values An improved fast-match algorithm that utilises all three RGB channels to construct colour sum-of-absolute-differences (CSAD) is proposed, thus improving the sum-of-absolute-differences distance used in fast-match. In this algorithm, each pixel in the image is categorised by clustering them using density-based spatial clustering of applications with noise (DBSCAN) algorithm over the RGB vector, then the number of pixels in each category and the cumulative RGB values for each RGB channel are calculated to identify the centroid of each category. The RGB vector centroid is used as the CSAD decision criteria, and inverse of number of pixels in each category is used as the differentiating coefficient to construct a new similarity measure. Experiment results demonstrate that this algorithm has significant higher accuracy for matching colour images than the original fast-match algorithm.

ABSTRACT:Geometric calibration is able to provide high-accuracy geometric coordinates of spaceborne SAR image through accurate geometric parameters in the Range-Doppler model by ground control points (GCPs). However, it is very difficult to obtain GCPs that covering large-scale areas, especially in the mountainous regions. In addition, the traditional calibration method is only used for single platform SAR images and can't support the hybrid geometric calibration for multi-platform images. To solve the above problems, a hybrid geometric calibration method for multi-platform spaceborne SAR images with sparse GCPs is proposed in this paper. First, we calibrate the master image that contains GCPs. Secondly, the point tracking algorithm is used to obtain the tie points (TPs) between the master and slave images. Finally, we calibrate the slave images using TPs as the GCPs. We take the Beijing-TianjinHebei region as an example to study SAR image hybrid geometric calibration method using 3 TerraSAR-X images, 3 TanDEM-X images and 5 GF-3 images covering more than 235 kilometers in the north-south direction. Geometric calibration of all images is completed using only 5 GCPs. The GPS data extracted from GNSS receiver are used to assess the plane accuracy after calibration. The results after geometric calibration with sparse GCPs show that the geometric positioning accuracy is 3m for TSX/TDX images and 7.5m for GF-3 images.

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