Abstract
Remote sensing image registration is the method of aligning two images from the same scene taken under different imaging circumstances containing different times, angles, or sensors. Scale-invariant feature transform (SIFT) is one of the most common matching methods previously used in the remote sensing image registration. The defects of SIFT are the large number of mismatches and high execution time due to the high dimensions of classical SIFT descriptor. These drawbacks reduce the efficiency of the SIFT algorithm. To enhance the performance of the remote sensing image registration, this paper proposes an approach consisting of three different steps. At first, the keypoints of both reference and second images are extracted using SIFT algorithm. Then, to increase the speed of the algorithm and accuracy of the matching, the SIFT descriptor with the vector length of 64 is used for keypoints description. Finally, a new method has been proposed for the image matching. The proposed matching method is based on calculating the distances of keypoints and their transformed points. Simulation results of applying the proposed method to some standard databases demonstrated the superiority of this approach compared with some other existing methods, according to the root mean square error (RMSE), precision and running time criteria.
Highlights
Image registration is a keystone in programmed remote sensing image analysis such as change detection, image synthesis and image mosaic [1, 2]
In [21], scale-invariant feature transform (SIFT) was adapted for multi-modal remote sensing image registration
To perform a fair evaluation, a comparison is made with the classical SIFT-nearest neighbor distance ratio (NNDR) matching [16], speed-up robust feature (SURF)-Delaunay triangulation matching(SURF-DTM) [30], SURF-NNDR matching [17], SIFT-Sparse Coding(SIFT-SC) [31] and improved SUSAN [32]
Summary
Image registration is a keystone in programmed remote sensing image analysis such as change detection, image synthesis and image mosaic [1, 2]. Registration methods of remote sensing images are categorized in intensity-based and feature-based approaches [3, 5, 6]. The former techniques use the intensity distribution in the masks of the same sizes. The SIFT algorithm is robust against scale and rotation changes and intensity variations, affine distortion and noise [18] These advantages made this algorithm significant in the registration process, the complex nature of remote sensing images has resulted in many mismatches [13, 19, 20]. This method considered a threshold value of 0.08 to remove keypoints with low contrast, and an 8×8
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