Automatic Image Registration of Satellite Images using SIFT

In this paper, we present an automated image registration algorithm, which combines the multi-scale wavelet transform (WT) with Scale Invariant Feature Transform (SIFT). The control points are selected using three levels WT. First, the image is decomposed into multi-scale levels using WT, then low frequency (approximation) images are input to SIFT algorithm for automatically obtaining control points. Experimental results show the efficiency and the accuracy of the proposed technique for selecting enough and correct control points.

Journal of Civil Engineering and Architecture, 2020

The scale-invariant feature transform (SIFT) ability to automatic control points (CPs) extraction is very well known on remote sensing images, however, its result inaccurate and sometimes has incorrect matching from generating a small number of false CPs pairs, their matching has high false alarm. This paper presents a method containing a modification to improve the performance of the SIFT CPs matching by applying sum of absolute difference (SAD) in different manner for the new optical satellite generation called near-equatorial orbit satellite (NEqO) and multi-sensor images. The proposed method leads to improving CPs matching with a significantly higher rate of correct matches. The data in this study were obtained from the RazakSAT satellite covering the Kuala Lumpur-Pekan area. The proposed method consists of three parts: (1) applying the SIFT to extract CPs automatically, (2) refining CPs matching by SAD algorithm with empirical threshold, and (3) evaluating the refined CPs scenario by comparing the result of the original SIFT with that of the proposed method. The result indicates an accurate and precise performance of the model, which showed the effectiveness and robustness of the proposed approach.

Photogrammetric Engineering & Remote Sensing, 2012

We propose an automatic image-to-image registration of highresolution satellite images using local properties and geometric locations of control points to improve the registration accuracy. First, coefficients of global affine transformation between images are extracted using a scale-invariant feature transform (SIFT)-based method, and features of the sensed image are transformed to the reference coordinate system using these coefficients. Then, a spatial distance and orientation difference between features of the reference and sensed images are additionally used to extract a large number of evenly distributed control points. The specified spatial distance is calculated between features of the sensed image that have been transformed to the reference coordinates and features of the reference image. Finally, the spatial distance integrated with Euclidean distances of invariant vectors is employed for local matching. The average orientation differences between control points of the two images are used for outlier elimination. A mapping function model consisting of an affine transformation and piecewise linear functions is applied to the control points for automatic registration of high-resolution images. The proposed method can extract a larger number of spatially well-distributed control points than SIFT-based methods. The registration accuracy for all sites calculated from manually selected checkpoints has acceptable geometric accuracy at the pixel level.

IEEE Transactions on Geoscience and Remote Sensing, 2011

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.

IJSRD, 2014

This paper aims to survey of Image Registration techniques for Satellite Images. Image Registration is a process of arranging two images (references and sense images) of the same scene taken at different times, from different sensors, and/or different viewpoints into a common coordinate system. They can be classified according to their nature (area based and feature based) and four basic steps of image registration procedures: feature detection, feature matching, and transform model estimation and image transformation and Re-sampling. There are different errors like localization errors, alignment errors, matching errors, are main challenges of image registration. To minimize error methods like Correlation like methods, Fourier transfer methods etc. are used for image registration.

The Photogrammetric Record, 2007

GIS databases need to undergo frequent updating due to rapid changes in the physical environment. Satellite imagery, with its recently enhanced spatial, spectral and temporal resolution, allows for accurate and reliable detection and characterization of patterns of change. A key factor that dictates the validity and reliability of change detection results is the quality of image registration. There is a need to develop robust registration procedures for multi-temporal, multi-spectral and/or multi-resolution imagery captured from different sensors to facilitate the change detection process. This paper discusses an approach for automated image registration based on a hierarchical image matching strategy. Following an initial feature point extraction phase, the method uses the similarity of grey levels to find homologous point candidates across the images. A hierarchical image pyramid is used to enhance both the success rate and reliability of matching while reducing the computational complexity. The adopted approach also uses contextual information to achieve locally consistent matches. Implementation and testing of the proposed method has involved imagery from different sensors. Here, results are reported for IKONOS and QuickBird data over test sites in Hobart, Australia and Thimphu, Bhutan. The potential of operational automated image registration in the process of change detection is illustrated via these test results.

Satellite image matching and co-registration are two key stages in image registration, fusion and super-resolution imaging processes where images are taken from different sensors, viewpoints or at different times. This paper presents: (1) An evaluation for the co-registration process using local features, (2) A registration scheme for registering optical images taken from different viewpoints in addition to radar images taken at different times. The selected feature detectors have been tested during the key point extraction, descriptor construction and matching processes. The framework suggests a sub-sampling process which controls the number of extracted key points for a real time processing and for minimizing the hardware requirements. After getting the pairwise matches between the two images, a registered image is composited by applying bundle adjustment and image warping enhancements. The results showed a good performance level for SURF over both SIFT and ORB detectors in terms of higher number of inliers and repeatability ratios. The Experiments were done on different optical and radar images from Rapid-Eye, TerraSAR-X, and ASTER satellite data for some areas in Germany and Egypt.

Identifying targets (sensed image) on large satellite images (reference image) is a difficult task and consumes time. In this paper an image registration method for satellite image sensing using feature based techniques is proposed for detecting the targets. Image registration is the process of sheathing images of the same scene with respect to a particular reference image. Classical registration methods are used for registering the images but they have certain limitations in their accuracy. Method proposed and implemented here has good response target matches and depicts 76 percent accuracy with the best combination of feature extractor and detector.

International Journal of Engineering Sciences & Research Technology, 2013

Image registration is one of the challenging tasks in image processing problems. The problem of image registration is to find a geometrical transformation that aligns points in one view of an image with corresponding points in another image or similar image. As image registration is widely used in remote sensing, this paper discusses the methods, process and descriptors used for image registration.

2010

Satellites generally have arrays of sensors having different resolution and wavelength parameters. For some applications, images acquired from different viewpoints and positions are required to be aligned. This alignment process could be achieved by matching the image features followed by image registration. In this paper registration of multispectral satellite images using Speeded Up Robust Features (SURF) method is examined. The performance of SURF for registration of high resolution satellite images captured at different bands is evaluated. Scale restriction (SR) method, which has recently been proposed for SIFT, is adapted to SURF to improve multispectral image registration performance. Matching performance between different bands using SURF, U-SURF, SURF with SR and U-SURF with SR is tested and robustness of these with respect to orientation and scale is evaluated.