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Aiming at there are long matching time and many wrong matching in the traditional SIFT algorithm, An image registration algorithm based on improved SIFT feature is put forward. First of all, through setting the number of extreme points in the feature point detection, feature points is found according to the DOG space structure from coarse to fine, and the improved SIFT feature descriptor generation algorithm is used. The preliminary matched point pairs are obtained by the nearest neighbor matching criterion, and the bilateral matching method is used for screening the preliminary matched point. Then, the second matching will be done by the similar measurement method based on mahalanobis distance, and RANSAC algorithm is used to calculate the affine transform model. Finally, the transformed image is resampled and interpolated through the bilinear interpolation method. Experimental results show that the algorithm can realize image registration effectively. Image registration technique is an important research content in computer vision and image processing in the, which are widely used in vehicle matching navigation and positioning, cruise missile terminal guidance, target tracking and recognition, image mosaic[1-6]. SIFT algorithm[3-5]can achieve image registration when there are translation, rotation, affine transformation between two images, even for images took by arbitrary angles. And SIFT feature is the milestone of local feature study. But there are long matching time and many wrong matching in the traditional SIFT algorithm, it is difficult to meet the requirement of fast image registration. This paper puts forward an image registration algorithm based on improved SIFT feature, which is robust for image rotation, affine and scale change, and is better than traditional SIFT algorithm.

Aiming at anhydrous bridge automatically identification in aerial images, an anhydrous bridge recognition algorithm based on the geometric characteristics is proposed. Firstly, the original image is do threshold segmentation to get binary image. Secondly, binary image is do morphological processed to get bridge area enhanced image and bridge area corrosion image, and these two bridge area are subtracted to extract suspected bridge area based on bridge rectangle feature. Finally, bridge regional area is positioned according to the straight-line characteristics of the bridge. Experimental results show the proposed algorithm can accurately identify the anhydrous bridge effectively. Key words: aerial image; anhydrous bridges identification; edge detection ; straight line extraction ; geometric features

Bridge recognition algorithm based on straight-line characteristic is proposed in order to automatically recognize bridge from aerial images, which includes the steps of edge detection, straight-line extraction, coarse location for bridge, accurate location for bridge. Meanwhile, realize the fast accurate location for bridge area by modified 8-neighborhood connectivity processing. The experiment result shows the reliability and efficiency of the method proposed in this article.

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