An ASIFT-Based Local Registration Method for Satellite Imagery

Abstract

Imagery registration is a fundamental step, which greatly affects later processes in image mosaic, multi-spectral image fusion, digital surface modelling, etc., where the final solution needs blending of pixel information from more than one images. It is highly desired to find a way to identify registration regions among input stereo image pairs with high accuracy, particularly in remote sensing applications in which ground control points (GCPs) are not always available, such as in selecting a landing zone on an outer space planet. In this paper, a framework for localization in image registration is developed. It strengthened the local registration accuracy from two aspects: less reprojection error and better feature point distribution. Affine scale-invariant feature transform (ASIFT) was used for acquiring feature points and correspondences on the input images. Then, a homography matrix was estimated as the transformation model by an improved random sample consensus (IM-RANSAC) algorithm. In order to identify a registration region with a better spatial distribution of feature points, the Euclidean distance between the feature points is applied (named the S criterion). Finally, the parameters of the homography matrix were optimized by the Levenberg-Marquardt (LM) algorithm with selective feature points from the chosen registration region. In the experiment section, the Chang'E-2 satellite remote sensing imagery was used for evaluating the performance of the proposed method. The experiment result demonstrates that the proposed method can automatically locate a specific region with high registration accuracy between input images by achieving lower root mean square error (RMSE) and better distribution of feature points.

Satellite Attitude Determination and Map Projection Based on Robust Image Matching

“…The proposed technique allows us to treat a 2-D image sensor as an accurate attitude sensor, and the technique is particularly useful for small satellites with limited payload because the erroneous onboard attitude determination often amplifies the registration errors. It should be noted that there are existing studies that achieve image registration without attitude information (e.g., [5,6], to name a few). However, our goal is satellite attitude determination in the first place, and map projection is one side of this study to utilize the geometric constraints in the observations.…”

Remote Sensing Image Registration Using Multiple Image Features

“…Various reviews on image registration methods can be found in [1][2][3][4][5]. Area-based methods use the pixel intensities of identical image windows to estimate similarity while feature-based methods extract image features (e.g., points, lines, and regions) and match them using similarity criteria [6].…”

Geometry-Based Global Alignment for GSMS Remote Sensing Images

“…However, the GSMS images are generally polluted by illumination, scale variation, cloud influence and other factors, and those algorithms do not work well. A feature-based matching algorithm is widely applied to remote sensing images [6][7][8][9] because of its robustness. For example, scale-invariant feature transform (SIFT) [10,11] has an excellent performance in most circumstances.…”