Automatic Image Registration Through Image Segmentation and SIFT

Abstract

Automatic image registration (AIR) is still a present challenge for the remote sensing community. Although a wide variety of AIR methods have been proposed in the last few years, there are several drawbacks which avoid their common use in practice. The recently proposed scale invariant feature transform (SIFT) approach has already revealed to be a powerful tool for the obtention of tie points in general image processing tasks, but it has a limited performance when directly applied to remote sensing images. In this paper, a new AIR method is proposed, based on the combination of image segmentation and SIFT, complemented by a robust procedure of outlier removal. This combination allows for an accurate obtention of tie points for a pair of remote sensing images, being a powerful scheme for AIR. Both synthetic and real data have been considered in this work for the evaluation of the proposed methodology, comprising medium and high spatial resolution images, and single-band, multispectral, and hyperspectral images. A set of measures which allow for an objective evaluation of the geometric correction process quality has been used. The proposed methodology allows for a fully automatic registration of pairs of remote sensing images, leading to a subpixel accuracy for the whole considered data set. Furthermore, it is able to account for differences in spectral content, rotation, scale, translation, different viewpoint, and change in illumination. Index Terms-Automatic image registration (AIR), image segmentation, optical images, scale invariant feature transform (SIFT).

Feature Extraction Based Retrieval of Geographic Images

“…The most powerful method for obtaining the local descriptors is the SIFT(Scale Invariant Feature Transform) [4].It transforms image data into scale-invariant coordinates which is relative to local features. It is based on 4 major stages: scale space extrema detection, localization of key point, assignment of orientation, and key point descriptor, [3].…”

“…In computer vision SIFT is an algorithm to detect and describe the local features in images. The interesting points on the object can be extracted to provide a "feature description" of the object of any image [4].Moment invariants are the functions of image moments and the moments are nothing but the projection of the image function into a polynomial basis [3]. Then we retrieve the relevant geographic images from the dataset by using Euclidean distances, [6].…”

An ASIFT-Based Local Registration Method for Satellite Imagery

“…SIFT locates key point by finding scale-space extreme in the difference-of-Gaussian (DoG) function; once a key point is detected, it will be described and matched based on its orientation, scale and coordination. SIFT and its variations are the most popular and successful feature detection algorithms in many remote sensing registration applications [2,9,[13][14][15][16]. However, if the image pairs are obtained under a large difference of camera viewpoints, the correspondences generated by SIFT would be too few to perform a reliable registration.…”

Modified Ransac for Sift-Based Insar Image Registration

“…When applied to image registration, Random Sample Consensus (RANSAC) [14] is often used with SIFT to remove outliers (mismatched pairs of points). Although a lot of image registration results by SIFT and modified versions with RANSAC are reported [15][16][17][18][19][20], few works have been done on InSAR image registration and little attention has been paid to RANSAC. Furthermore, the "maximization of inliers" criterion of the original RANSAC is not optimal for InSAR image registration for the number of residues (NOR) [21] of the interferogram obtained through this criterion is not the fewest.…”