Least Squares Image Matching Research Articles

Monocular Photogrammetric System for 3D Reconstruction of Welds in Turbid Water

This article presents a methodology for the 3D reconstruction of welds under water. A monocular measuring system, equipped with a hemispherical (dome) port, and its methodology are introduced. Furthermore, a simple but effective approach for the alignment of a dome port and the entrance pupil of a lens is presented. The alignment is mechanically supported by the presented lens alignment shell. Turbidity experiments in the laboratory quantify the accuracy of the 3D reconstruction of an underwater welded hollow weld in comparison with a high-quality reference measurement. The analyses show that the system is capable to reconstruct the surface of the weld in high quality using least-squares image matching. The quality level ranges from 0.06 mm in clear water to 0.11 mm in turbid water featuring a visibility of ~ 140 mm.

Optimal Weight Design Approach for the Geometrically-Constrained Matching of Satellite Stereo Images

This study presents an optimal weighting approach for combined image matching of high-resolution satellite stereo images (HRSI). When the rational polynomial coefficients (RPCs) for a pair of stereo images are available, some geometric constraints can be combined in image matching equations. Combining least squares image matching (LSM) equations with geometric constraints equations necessitates determining the appropriate weights for different types of observations. The common terms between the two sets of equations are the image coordinates of the corresponding points in the search image. Considering the fact that the RPCs of a stereo pair are produced in compliance with the coplanarity condition, geometric constraints are expected to play an important role in the image matching process. In this study, in order to control the impacts of the imposed constraint, optimal weights for observations were assigned through equalizing their average redundancy numbers. For a detailed assessment of the proposed approach, a pair of CARTOSAT-1 sub-images, along with their precise RPCs, were used. On top of obtaining different matching results, the dimension of the error ellipses of the intersection points in the object space were compared. It was shown through analysis that the geometric mean of the semi-minor and semi-major axis by our method was reduced 0.17 times relative to the unit weighting approach.

A Linear Feature-Based Approach for the Registration of Unmanned Aerial Vehicle Remotely-Sensed Images and Airborne LiDAR Data

Compared with traditional manned airborne photogrammetry, unmanned aerial vehicle remote sensing (UAVRS) has the advantages of lower cost and higher flexibility in data acquisition. It has, therefore, found various applications in fields such as three-dimensional (3D) mapping, emergency management, and so on. However, due to the instability of the UAVRS platforms and the low accuracy of the onboard exterior orientation (EO) observations, the use of direct georeferencing image data leads to large location errors. Light detection and ranging (LiDAR) data, which is highly accurate 3D information, is treated as a complementary data source to the optical images. This paper presents a semi-automatic approach for the registration of UAVRS images and airborne LiDAR data based on linear control features. The presented approach consists of three main components, as follows. (1) Buildings are first separated from the point cloud by the integrated use of height and size filtering and RANdom SAmple Consensus (RANSAC) plane fitting, and the 3D line segments of the building ridges and boundaries are semi-automatically extracted through plane intersection and boundary regularization with manual selections; (2) the 3D line segments are projected to the image space using the initial EO parameters to obtain the approximate locations, and all the corresponding 2D line segments are semi-automatically extracted from the UAVRS images. Meanwhile, the tie points of the UAVRS images are generated using a Förstner operator and least-squares image matching; and (3) by use of the equations derived from the coplanarity constraints of the linear control features and the colinear constraints of the tie points, block bundle adjustment is carried out to update the EO parameters of the UAVRS images in the coordinate framework of the LiDAR data, achieving the co-registration of the two datasets. Experiments were performed to demonstrate the validity and effectiveness of the presented method, and a comparison with the traditional registration method based on LiDAR intensity images showed that the presented method is more accurate, and a sub-pixel accuracy level can be achieved.

ACCURACY IMPROVEMENT BY THE LEAST SQUARES IMAGE MATCHING EVALUATED ON THE CARTOSAT-1

Abstract. Generating accurate elevation data from satellite images is a prerequisite step for applications that involve disaster forecasting and management using GIS platforms. In this respect, the high resolution satellite optical sensors may be regarded as one of the prime and valuable sources for generating accurate and updated elevation information. However, one of the main drawbacks of conventional approaches for automatic elevation generation from these satellite optical data using image matching techniques is the lack of flexibility in the image matching functional models to take dynamically into account the geometric and radiometric dissimilarities between the homologue stereo image points. The classical least squares image matching (LSM) method, on the other hand, is quite flexible in incorporating the geometric and radiometric variations of image pairs into its functional model. The main objective of this paper is to evaluate and compare the potential of the LSM technique for generating disparity maps from high resolution satellite images to achieve sub pixel precision. To evaluate the rate of success of the LSM, the size of the y-disparities between the homologous points is taken as the precision criteria. The evaluation is performed on the Cartosat-1 stereo along track images over a highly mountainous terrain. The precision improvement is judged based on the standard deviation and the scatter pattern of the y-disparity data. The analysis of the results indicate that, the LSM has achieved the matching precision of about 0.18 pixels which is clearly superior to the manual pointing that yielded the precision of 0.37 pixels.

Least squares image matching: A comparison of the performance of robust estimators

Abstract. Least squares image matching (LSM) has been extensively applied and researched for high matching accuracy. However, it still suffers from some problems. Firstly, it needs the appropriate estimate of initial value. However, in practical applications, initial values may contain some biases from the inaccurate positions of keypoints. Such biases, if high enough, may lead to a divergent solution. If all the matching biases have exactly the same magnitude and direction, then they can be regarded as systematic errors. Secondly, malfunction of an imaging sensor may happen, which generates dead or stuck pixels on the image. This can be referred as outliers statistically. Because least squares estimation is well known for its inability to resist outliers, all these mentioned deviations from the model determined by LSM cause a matching failure. To solve these problems, with simulation data and real data, a series of experiments considering systematic errors and outliers are designed, and a variety of robust estimation methods including RANSACbased method, M estimator, S estimator and MM estimator is applied and compared in LSM. In addition, an evaluation criterion directly related to the ground truth is proposed for performance comparison of these robust estimators. It is found that robust estimators show the robustness for these deviations compared with LSM. Among these the robust estimators, M and MM estimator have the best performances.

CO-REGISTRATION BETWEEN MULTISOURCE REMOTE-SENSING IMAGES

Abstract. Image registration is essential for geospatial information systems analysis, which usually involves integrating multitemporal and multispectral datasets from remote optical and radar sensors. An algorithm that deals with feature extraction, keypoint matching, outlier detection and image warping is experimented in this study. The methods currently available in the literature rely on techniques, such as the scale-invariant feature transform, between-edge cost minimization, normalized cross correlation, leasts-quares image matching, random sample consensus, iterated data snooping and thin-plate splines. Their basics are highlighted and encoded into a computer program. The test images are excerpts from digital files created by the multispectral SPOT-5 and Formosat-2 sensors, and by the panchromatic IKONOS and QuickBird sensors. Suburban areas, housing rooftops, the countryside and hilly plantations are studied. The co-registered images are displayed with block subimages in a criss-cross pattern. Besides the imagery, the registration accuracy is expressed by the root mean square error. Toward the end, this paper also includes a few opinions on issues that are believed to hinder a correct correspondence between diverse images.

Registration of Multisource Satellite Images by Thin-plate Splines with Highly Reliable Conjugate Points

Image registration is essential for geospatial information systems analysis, which usually involves integrating multi-temporal and multispectral datasets from remote optical and radar sensors. An algorithm that deals with feature extraction, keypoint matching, outlier detection, and image warping is experimented in this study. The methods currently available in the literature rely on techniques, such as the scale-invariant feature transform, between-edge cost minimization, normalized cross correlation, least-squares image matching, random sample consensus, iterated data snooping, and thin-plate splines. Their basics are highlighted and encoded into a computer program. The test images are excerpts from digital files created by the multispectral SPOT-5 and Formosat-2 sensors, side-looking ERS-2, and Envisat synthetic aperture radars, and by the panchromatic Ikonos and QuickBird sensors. Suburban areas, housing rooftops, the countryside and hilly plantations are studied. The co-registered images are displayed with block sub-images in a criss-cross pattern. Besides the imagery, the registration accuracy is expressed by the root mean square error. Toward the end, this paper also includes a few opinions on issues that are believed to hinder a correct correspondence between diverse images.

Optimum estimator for SPOT-pan and ALS-lidar intensity image registration via tie points

The characteristics of a Système Pour d’Observation de la Terre (SPOT) panchromatic image of a geographic area such as an airfield are different from those of an airborne laser scanner lidar intensity image of the same area. This study investigates the feasibility of locating tie points shared by the target (airborne lidar-intensity) and search (spaceborne SPOT-pan) images. At an approximate point position, image segmentation and enhancement are employed so that a pair of target and search window images tend to resemble each other. The similarity is then measured by utilizing a least-squares image matching algorithm which involves variance-component estimation. The proposed method has a 3–15% higher success rate than an equal-weight matching method. Coordinate differences of the conjugate image points serve as reference value to be used in the least-squares collocation method for pixel-by-pixel transformation. The image registration accuracy from a SPOT-pan to a lidar intensity image is estimated to be ±1.0 pixel.