Jacqueline Le Moigne

Efficient algorithms for robust feature matching

Abstract One of the basic building blocks in any point-based registration scheme involves matching feature points that are extracted from a sensed image to their counterparts in a reference image. This leads to the fundamental problem of point matching: Given two sets of points, find the (affine) transformation that transforms one point set so that its distance from the other point set is minimized. Because of measurement errors and the presence of outlying data points, it is important that the distance measure between the two point sets be robust to these effects. We measure distances using the partial Hausdorff distance. Point matching can be a computationally intensive task, and a number of theoretical and applied approaches have been proposed for solving this problem. In this paper, we present two algorithmic approaches to the point matching problem, in an attempt to reduce its computational complexity, while still providing a guarantee of the quality of the final match. Our first method is an approximation algorithm, which is loosely based on a branch-and-bound approach due to Huttenlocher and Rucklidge, (Technical Report 1321, Dept. of Computer Science, Cornell University, Ithaca, 1992; Proc. IEEE Conf. on Computer vision and Pattern Recognition, New York, 1993, pp. 705–706). We show that by varying the approximation error bounds, it is possible to achieve a tradeoff between the quality of the match and the running time of the algorithm. Our second method involves a Monte Carlo method for accelerating the search process used in the first algorithm. This algorithm operates within the framework of a branch-and-bound procedure, but employs point-to-point alignments to accelerate the search. We show that this combination retains many of the strengths of branch-and-bound search, but provides significantly faster search times by exploiting alignments. With high probability, this method succeeds in finding an approximately optimal match. We demonstrate the algorithms’ performances on both synthetically generated data points and actual satellite images.

A FAST IMPLEMENTATION OF THE ISODATA CLUSTERING ALGORITHM

Clustering is central to many image processing and remote sensing applications. ISODATA is one of the most popular and widely used clustering methods in geoscience applications, but it can run slowly, particularly with large data sets. We present a more efficient approach to ISODATA clustering, which achieves better running times by storing the points in a kd-tree and through a modification of the way in which the algorithm estimates the dispersion of each cluster. We also present an approximate version of the algorithm which allows the user to further improve the running time, at the expense of lower fidelity in computing the nearest cluster center to each point. We provide both theoretical and empirical justification that our modified approach produces clusterings that are very similar to those produced by the standard ISODATA approach. We also provide empirical studies on both synthetic data and remotely sensed Landsat and MODIS images that show that our approach has significantly lower running times.

Image Registration for Remote Sensing

Foreword Jon A. Benediktsson Part I. The Importance of Image Registration for Remote Sensing: 1. Introduction Jacqueline Le Moigne, Nathan S. Netanyahu and Roger D. Eastman 2. Influence of image registration on validation efforts Bin Tan and Curtis E. Woodcock 3. Survey of image registration methods Roger D. Eastman, Nathan S. Netanyahu and Jacqueline Le Moigne Part II. Similarity Metrics for Image Registration: 4. Fast correlation and phase correlation Harold S. Stone 5. Matched filtering techniques Qin-Sheng Chen 6. Image registration using mutual information Arlene A. Cole-Rhodes and Pramod K. Varshney Part III. Feature Matching and Strategies for Image Registration: 7. Registration of multiview images A. Ardeshir Goshtasby 8. New approaches to robust, point-based image registration David M. Mount, Nathan S. Netanyahu and San Ratanasanya 9. Condition theory for image registration and post-registration error estimation Charles S. Kenney, B. S. Manjunath, Marco Zuliani and Kaushal Solanki 10. Feature-based image to image registration Venu M. Govindu and Rama Chellappa 11. On the use of wavelets for image registration Jacqueline Le Moigne, Ilya Zavorin and Harold S. Stone 12. Gradient descent approaches to image registration Arlene A. Cole-Rhodes and Roger D. Eastman 13. Bounding the performance of image registration Min Xu and Parmod K. Varshney Part IV. Applications and Operational Systems: 14. Multi-temporal and multi-sensor image registration Jacqueline Le Moigne, Arlene A. Cole-Rhodes, Roger D. Eastman, Nathan S. Netanyahu, Harold S. Stone, Ilya Zavorin and Jeffrey T. Morisette 15. Georegistration of meteorological images James L. Carr 16. Challenges, solutions, and applications of accurate multi-angle image registration: lessons learned from MISR Veljko M. Jovanovic, David J. Diner and Roger Davies 17. Automated AVHRR image navigation William J. Emery, R. Ian Crocker and Daniel G. Baldwin 18. Landsat image geocorrection and registration James C. Storey 19. Automatic and precise orthorectification of SPOT images Simon Baillarin, Aurelie Bouillon and Marc Bernard 20. Geometry of the VEGETATION sensor Sylvia Sylvander 21. Accurate MODIS global geolocation through automated ground control image matching Robert E. Wolfe and Masahiro Nishihama 22. SeaWIFS operational geolocation assessment system Frederick S. Patt Part V. Conclusion: 23. Concluding remarks Jacqueline Le Moigne, Nathan S. Netanyahu and Roger D. Eastman Glossary Index.

Parallel registration of multisensor remotely sensed imagery using wavelet coefficients

Due to the increasing amount and diversity of remotely sensed data, image registration is becoming one of the most important issues in remote sensing. In the near future, remote sensing systems will provide large amounts of data representing multiple- time or simultaneous observations of the same features by different sensors. The combination of data from coarse-resolution satellite sensors designed for large-area survey and from finer- resolution sensors for more detailed studies will allow better analysis of each type of data as well as validation of global low-resolution data analysis by the use of local high-resolution data analysis. This integration of information from multiple sources starts with the registration of the data. The most common approach to image registration is to choose, in both input image and reference image, some well-defined ground control points (GCPs), and then to compute the parameters of a deformation model. The main difficulty lies in the choice of the GCPs. In our work, a parallel implementation of decomposition and reconstruction by wavelet transforms has been developed on a single-instruction multiple-data (SIMD) massively parallel computer, the MasPar MP-1. Utilizing this framework, we show how maxima of wavelet coefficients, which can be used for finding ground control points of similar resolution remotely sensed data, can also form the basis of the registration of very different resolution data, such as data from the NOAA Advanced Very High Resolution Radiometer (AVHRR) and from the Landsat/Thematic Mapper (TM).Due to the increasing amount and diversity of remotely sensed data, image registration is becoming one of the most important issues in remote sensing. In the near-future, remote sensing systems will provide large amounts of data representing multiple-time or simultaneous observations of the same features by different sensors. The combination of data from coarse-resolution viewing satellite sensors designed for large area survey and from finer resolution sensors for more detailed studies will allow better analysis of each type of data as well as validation of global low-resolution data analysis by the use of local highresolution data analysis. This integration of information from multiple sources starts with the registration of the data. The most cormnon approach to image registration is to choose, in both input image and reference image, some well defined ground control points (GCPs), and then to compute the parameters of a deformation model. The main difficulty lies in the choice of the GCPs. In our work, a parallel implementation of decomposition and reconstruction by wavelet transforms has been developed on a Single Instruction Multiple Data (SIMD) massively parallel computer, the MasPar MP1 . Utilizing this framework,we show how maxima of wavelet coefficents, which can be used for finding ground control points of similar resolution remotely sensed data8, can also form the basis of the registration of very different resolution data, such as data from the NOAA Advanced Very High Resolution Radiometer (AVHRR) and from the Landsat/Thematic Mapper (TM).

Improved algorithms for robust point pattern matching and applications to image registration

Given two images of roughly the same scene, image registration is the process of determining the transformation that most nearly maps one image to another. This problem is of particular interest in remote sensing applications, where it is known that two images correspond to roughly the same gecgraphic region, but the exact alignment between the images io not known. There are many approaches to image registration. We will consider an approach based on extracting a Ret of point features from each of the two images, and thus reducing the problem to a point pattern matching problem. Because of measurement errors and the presence of outlying data points in either of the images, it is important that the diotance measure between two point sets be robust to theeo cffecto. We will measure distances using the partial Hauodorff distance, An important element of image registration applications is that the search begins with a priori information on the bounds of transformation, and a good algorithm should be able to take advantage of this information. Point matching can be a computationally intensive task, and there have been a number of algorithms and approaches proposed for solving this problem, both from theoretical and applied standpoints. One common approach is based on a *Dopartmont of Computer Science and Institute for Advanced Computer Studies, University of Maryland, College Park, MaryInnd. Email: mountQce.umd.edu. The support of the Nationnl Science Foundation under grant CCR-9712379 is gratefully acknowledged. tCentcr for Automation Research, University of Maryland, Collego Park, and Center of Excellence in Space Data and Information Sciences (CESDIS), Code 930.5, Space Data and Computing Division, NASA Goddard Space Flight Center, Greenbolt, Mnr.vlnnd. EmaiL nathanocf ar .umd. edu. The SUP port of tha Applied Information Sciences Branch (AISB), Code 935, NASAIGSFC, under contract NAS 5555-37 is greatfully acknowledged. SUniversities Space Research Association/CESDIS, Code 030,5, Space Data and Computing Division, NASA/GSFC, Grconbolt, Maryland. Email: lemoigneQcesdis.gsfc.nasa.gov. The nupport of the AISB, Code 935, NASA/GSFC is greatfully acknowledged, jkr&sjon tomnko dj&l orhardcopies ofnjl orpartoftiworkfm pcmond or clormom we in grated without fee qrovided that copies rue not mndc or &ributcd for profit or comn~ercld advantage and that copjm beor Ihin notiw and the full citation on the f~page. Tf copy odwwi~rice, to rcpubliQ to post on servers or to redlstnbue to W require3 prior rpeoitio permission Mdlor a fffi. SCCJ 98 Minneapolis M~IUV.XO~~ USA Copyri&t /KM 1998 O-89791-9734/98/ 6...

Wavelet-Based Image Registration on Parallel Computers

5.00 geometric branch-and-bound search of transformation space and another is based on using point alignments to derive the matching transformation. The former has the advantage that it can provide guarantees on the accuracy of the final match, and that it naturally uses any a priori information to bound the search. The latter has the advantage of simplicity and speed. We introduce a new practical approximation algorithm, which we call bounded alignment, for robust point pattern matching. This algorithm is a novel combination of these two approaches. Our algorithm operates within the framework of the branch-and-bound, but employs point-to-point alignments to accelerate the search. We show that this combination retains many of the strengths of branch-and-bound search, but provides significantly faster search times by esplaiting alignments. We have implemented the algorithm and have demonstrated its performance on both synthetically generated data points and actual satellite images.

Automatic Image Registration of Multimodal Remotely Sensed Data With Global Shearlet Features

Digital image registration is very important in many applications, such as medical imagery, robotics, visual inspection, and remotely sensed data processing. NASA s Mission To Planet Earth (MTPE) program will be producing enormous Earth global change data, reaching hundreds of Gigabytes per day, that are collected form different spacecrafts and different perspectives using many sensors with diverse resolutions and characteristics. The analysis of such data requires integration, therefore, accurate registration of these data. Image registration is defined as the process which determines the most accurate relative orientation between two or more images, acquired at the same or different times by different or identical sensors. Registration can also provide the absolute orientation between an image and a map.Digital image registration is very important in many applications, such as medical imagery, robotics, visual inspection, and remotely sensed data processing. NASA s Mission To Planet Earth (MTPE) program will be producing enormous Earth global change data, reaching hundreds of Gigabytes per day, that are collected form different spacecrafts and different perspectives using many sensors with diverse resolutions and characteristics. The analysis of such data requires integration, therefore, accurate registration of these data. Image registration is defined as the process which determines the most accurate relative orientation between two or more images, acquired at the same or different times by different or identical sensors. Registration can also provide the absolute orientation between an image and a map.

Mutual information as a similarity measure for remote sensing image registration

Automatic image registration is the process of aligning two or more images of approximately the same scene with minimal human assistance. Wavelet-based automatic registration methods are standard but are sometimes not robust to the choice of initial conditions. That is, if the images to be registered are too far apart relative to the initial guess of the algorithm, the registration algorithm does not converge or has poor accuracy and is thus not robust. These problems occur because wavelet techniques primarily identify isotropic textural features and are less effective at identifying linear and curvilinear edge features. We integrate the recently developed mathematical construction of shearlets, which is more effective at identifying sparse anisotropic edges, with an existing automatic wavelet-based registration algorithm. Our shearlet features algorithm produces more distinct features than wavelet features algorithms; the separation of edges from textures is even stronger than with wavelets. Our algorithm computes shearlet and wavelet features for the images to be registered and then performs least-squares minimization on these features to compute a registration transformation. Our algorithm is two-staged and multiresolution in nature. First, a cascade of shearlet features is used to provide a robust, although approximate, registration. This is then refined by registering with a cascade of wavelet features. Experiments across a variety of image classes show an improved robustness to initial conditions, when compared with wavelet features alone.

Use of wavelets for image registration

Feature-based matching is essential for attaining sub-pixel registration of remotely sensed imagery. In this work, we focus on two different similarity metrics which are used to match extracted features, correlation and mutual information. Although mutual information has been successfully applied to medical image registration, these metrics have not been systematically studied for remote sensing applications. This paper presents some first results in the comparison of correlation and mutual information, relative to their respective accuracy and response to noise. The study is performed using Landsat-TM data.

Image registration using wavelet techniques

Wavelet-based image registration has previously been proposed by the authors. In previous work, maxima obtained from orthogonal Daybooks filters as well as from Simoncelli steerable filters were utilized and compared to register images in a multi-resolution fashion. The first comparative results between both types of filters showed that despite the lack of translation-invariance of the orthogonal filters, both types of filters gave very encouraging results for non-noisy data and small transformations. But the accuracy obtained with orthogonal filters seemed to degrade very quickly for large rotations and large amounts of noise, while results obtained with steerable filters appeared much more stable under these conditions. In this work, we are performing a systematic study of the robustness of such methods as a function of translation, rotation and noise parameters, for both types of filters and using data form the Landsat/Thematic Mapper.Wavelet-based image registration has previously been proposed by the authors. In previous work, maxima obtained from orthogonal Daubechies filters as well as from Simoncelli steerable filters were utilized and compared to register images in a multi-resolution fashion. The first comparative results between both types of filters showed that despite the lack of translation-invariance of the orthogonal filters, both types of filters gave very encouraging results for non-noisy data and small transformations. But the accuracy obtained with orthogonal filters seemed to degrade very quickly for large rotations and large amounts of noise, while results obtained with steerable filters appeared much more stable under these conditions. In this work, we are performing a systematic study of the robustness of such methods as a function of translation, rotation and noise parameters, for both types of filters and using data from the Landsat/ Thematic Mapper (TM).