Survey of Image Registration techniques for Satellite Images

Procedia Computer Science, 2020

Image registration is the process of overlaying two or more multi sensor or multi temporal or multi resolution images of the same scene. Image registration is very crucial preprocessing step in any remote sensing image processing application. This paper presents a brief review of evolution of different image registration methodologies with challenges involved in it. Image registration basically aligns the two images (reference and sensed image) geometrically. In the first part of this paper, classical methods will be discussed which are mainly based on area based (Intensity based) approach and feature based approach. Ample amount of work has already been done in both methods in manual and automatic manner. In the second part all recent approaches will be discussed and compared with results, on the basis of registration accuracy, number of correct correspondence. In recent studies Convolutional Neural Network with deep learning algorithms are used in combination with traditional methods to boost the overall performance.

World Pumps, 2010

Image registration determines the relative orientation between two images. As there are different techniques for image registration, it is important to compare these techniques to identify the advantages and disadvantages of each one. In this paper, a comparison between a fast Fourier transform (FFT)-based technique, a contour-based technique, a wavelet-based technique, a Harris-Pulse Coupled Neural Network (PCNN)-based technique and Harris-Moment-based technique is presented. The algorithms were tested on Landsat Thematic Mapper (TM) and SPOT remote sensing images and its performance were compared using the Root Mean Square Error (RMSE).

2009 First International Conference on Advances in Satellite and Space Communications, 2009

Image registration is one of the basic image processing operations in remote sensing. With the increase in the number of images collected every day from different sensors, automated registration of multi-sensor/multi-spectral images has become an important issue. A wide range of registration techniques has been developed for many different types of applications and data. Given the diversity of the data, it is unlikely that a single registration scheme will work satisfactorily for all different applications. A possible solution is to integrate multiple registration algorithms into a rule-based artificial intelligence system, so that appropriate methods for any given set of multisensor data can be automatically selected. The objective of this paper is to present an automatic registration algorithm which has been developed at INPE. It uses a multiresolution analysis procedure based upon the wavelet transform. The procedure is completely automatic and relies on the grey level information content of the images and their local wavelet transform modulus maxima. The algorithm was tested on SPOT and TM images from forest, urban and agricultural areas. In all cases we obtained very encouraging results.

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.

Image registration is a fundamental task in remote sensing image processing that is used to match two or more images taken, for example, at different times, from different sensors or from different viewpoints. A lot of automation has been achieved in this field but ever sprouting data quality and characteristics compel innovators to design new and/or improve existing registration techniques. In this paper, image registration methodologies are broadly classified into intensity and feature based approaches and analyzed critically.

2002

Image registration is the process of matching two images so that corresponding coordinate points in the two images correspond to the same physical region of the scene being imaged. It's an important operation needed in remote sensing that basically involves the identification of many control points. As the manual identification of control points may be time-consuming and tedious, automated techniques have been developed. This paper describes a system for automatic registration of satellite images under development at the Division of Image Processing (National Institute for Space Research -INPE) and the Vision Lab (Electrical & Computer Engineering department, UCSB). The system provides tools that allow automatic registration and mosaic of remote sensing images. It is designed to accept different types of data (TM, SPOT, JERS, etc.) and information provided by the user that are used to speed up the processing or avoid mismatched control points. A statistical procedure is used to characterize good and bad registrations. Based on this "good fit-bad fit" statistical testing the user can stop or modify the parameters and continue the processing. Extensive algorithm tests have been performed by registering optical, radar, multi-sensor, high-resolution images and video sequences. We have included very difficult image registration examples in order to show the strengths and limits of our approach. We developed a registration system online demo (http://nayana.ece.ucsb.edu/registration) that contains several examples that can be executed using web browser.

Image and Vision Computing, 2003

This paper aims to present a review of recent as well as classic image registration methods. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints, and/or by different sensors. The registration geometrically align two images (the reference and sensed images). The reviewed approaches are classified according to their nature (areabased and feature-based) and according to four basic steps of image registration procedure: feature detection, feature matching, mapping function design, and image transformation and resampling. Main contributions, advantages, and drawbacks of the methods are mentioned in the paper. Problematic issues of image registration and outlook for the future research are discussed too. The major goal of the paper is to provide a comprehensive reference source for the researchers involved in image registration, regardless of particular application areas. q

The purpose of this paper is to provide a review of classic as well as recent image registration methods. Image registration method aims to align two or more images of the same scene taken at different times, with different instruments, from different viewpoints. In this process two images (the reference and sensed images) are geometrically aligned. The reviewed approaches are classified according to four basic steps of the image registration procedure: feature detection, control points matching, a design of the mapping function, and image transformation and according to their nature (area based and feature-based). The paper also has an objective to provide a comprehensive study of different image registration methods, regardless of particular application areas.

Lecture Notes in Computer Science, 2010

Image Registration is the first step towards using remote sensed images for any purpose. Despite numerous techniques being developed for image registration, only a handful has proved to be useful for registration of remote sensing images due to their characteristic of being computationally heavy. Recent flux in technology has prompted a legion of approaches that may suit divergent remote sensing applications. This paper presents a comprehensive survey of such literatures including recently developed techniques.

2006

This paper, introduces a novel multiresolution method for automatic image registration with respect to image-to-object spaces based on key features consideration. The present approach is designed to be completely independent from the sensor type and any prior information on the exterior orientation. Moreover, in the proposed procedure, Genetic algorithm (GA) is used to match the corresponding features and fit the satellite image on the vector map by optimizing the transformation accuracy on checked and control points. The potential of the proposed method is evaluated using IKONOS imagery and corresponding digital vector map. During the time lapse between the generated map and image acquisition, considerable changes have also occurred in the city. This method has proved to be very efficient and reliable for automatic registration of satellite imageries based on digital maps.