Subpixel Registration Accuracy Research Articles

Satellite position and attitude estimation using an infrared earth sensor

Micro/nano-satellites are constrained by payload, size, and cost, making enhancements in design flexibility crucial, particularly for orbit and attitude determination systems. An effective strategy is the innovative utilization of existing sensors. This study investigates a novel application of Earth sensors for autonomous position and attitude determination enabling them as a viable option for autonomous navigation in low-cost micro/nano-satellites through computer vision technology. To address large resolution differences and distortions between sensed infrared images and reference maps, this paper presents a coarse-to-fine matching framework that integrates deep learning features and area-based registrations. First, CNN-based registration and optimization techniques are utilized for coarse registration. A modified VGG16 (M-VGG) and a multiscale pyramid information fusion (MPIF) module are employed to extract and describe deep abstract features, enhancing robustness. Subsequently, edge-based extended phase correlation is employed for fine registration. Finally, the ultimate position and attitude parameters are determined by integrating the results from coarse and fine registration. The effectiveness of the proposed scheme is validated using synthetic images. An analysis of 342 images demonstrates sub-pixel registration accuracy, with a root mean square error (RMSE) of 1.9322 km for position and 0.1185∘ for attitude.

A Sub-Second Method for SAR Image Registration Based on Hierarchical Episodic Control

For Synthetic Aperture Radar (SAR) image registration, successive processes following feature extraction are required by both the traditional feature-based method and the deep learning method. Among these processes, the feature matching process—whose time and space complexity are related to the number of feature points extracted from sensed and reference images, as well as the dimension of feature descriptors—proves to be particularly time consuming. Additionally, the successive processes introduce data sharing and memory occupancy issues, requiring an elaborate design to prevent memory leaks. To address these challenges, this paper introduces the OptionEM-based reinforcement learning framework to achieve end-to-end SAR image registration. This framework outputs registered images directly without requiring feature matching and the calculation of the transformation matrix, leading to significant processing time savings. The Transformer architecture is employed to learn image features, while a correlation network is introduced to learn the correlation and transformation matrix between image pairs. Reinforcement learning, as a decision process, can dynamically correct errors, making it more-efficient and -robust compared to supervised learning mechanisms such as deep learning. We present a hierarchical reinforcement learning framework combined with Episodic Memory to mitigate the inherent problem of invalid exploration in generalized reinforcement learning algorithms. This approach effectively combines coarse and fine registration, further enhancing training efficiency. Experiments conducted on three sets of SAR images, acquired by TerraSAR-X and Sentinel-1A, demonstrated that the proposed method’s average runtime is sub-second, achieving subpixel registration accuracy.

Integration of ground-based and space-borne radar observations for three-dimensional deformations reconstruction: application to Luanchuan mining area, China

Luanchuan mining area, which is located in Henan Province, China and characterized by undulating topography and large precipitation, is vulnerable to the landslide disasters. The observations from the space-borne radar (ascending and descending TerraSAR-X) images and the ground-based radar (GPRI-II) images are integrated to monitor the surface deformations in Luanchuan mining area. Before the integration of the observations from multi-source SAR images, the coordinate systems of multi-source images are required to be unified by geographical projection. However, the accuracy of the commonly used geographical coding will be degraded by external DEM errors and inaccurate satellite orbit, which makes it difficult to achieve the high-precision registration of space-borne and ground-based radar scenes. Therefore, in this paper the iterative closest point (ICP) method is introduced to register the space-borne and ground-based images, yielding sub-pixel registration accuracy. Reasonable weights are then assigned to multi-source observations through Strain Model and Variance Component Estimation (SM-VCE) method, from which the high-precision three-dimensional deformations can be reconstructed. The results show that the maximum deformation rates in the east-west, north-south and vertical directions of Luanchuan mining area from July 20, 2019, to August 1, 2019, are about 0.2, 0.4 and 0.7 mm/day, respectively. The deformations are mainly on the loose slags in the Luanchuan pit rather than the bedrock, as a result of the accumulation of waste slags. Some suggestions on the installation location of ground-based radar are also provided to better combine the space-borne and ground-based observations.

An automatic method for subpixel registration of KMSS-M imagery based on coarse-resolution actualized reference

The paper describes a method for automatic subpixel-accurate geographic referencing of imagery acquired by KMSS-M with 60 m spatial resolution, based on leveraging a coarse, reconstructed, cloud-free and daily updated MODIS surface reflectance reference image. The method is based on maximizing Pearson's correlation value when determining an optimal local displacement of the distorted image fragment by comparing with the reference image. To assess the effectiveness of the method when used over continental-scale and heterogeneous areas, three experiments were carried out providing quantitative estimates of imagery registration errors: an experiment with model datasets, an experiment to estimate the absolute registration error of MODIS reference imagery, and an experiment to estimate the registration error of geocorrected KMSS-M data. Experimental evaluation of the method based on model datasets of decameter-resolution Sentinel-2 (MSI) imagery demonstrated its robustness when used over a variety of environmental conditions over a one year-long observation period. The average georeferencing error of MODIS coarse-resolution reference was shown to be less than 20 meters in Red and Near-infrared bands. Corrected KMSS-M imagery evaluation over the Russian Grain Belt within 2020 has shown, on average, the subpixel referencing accuracy both in Red and Near-infrared bands, while the average absolute georeferencing error of the original uncorrected KMSS-M imagery was shown to be about 3 kilometers. Subpixel registration accuracy of KMSS-M imagery, corrected with MODIS-based coarse-resolution reference, opens new prospects for using multi-temporal analysis of this multispectral surface reflectance data in a variety of scientific and practical applications associated with vegetation cover satellite monitoring. The technological flexibility of the method ensures its applicability to data from other satellite systems for Earth optical remote sensing.

Automatic Sub-Pixel Co-Registration of Remote Sensing Images Using Phase Correlation and Harris Detector

In this paper, we propose a new approach for sub-pixel co-registration based on Fourier phase correlation combined with the Harris detector. Due to the limitation of the standard phase correlation method to achieve only pixel-level accuracy, another approach is required to reach sub-pixel matching precision. We first applied the Harris corner detector to extract corners from both references and sensed images. Then, we identified their corresponding points using phase correlation between the image pairs. To achieve sub-pixel registration accuracy, two optimization algorithms were used. The effectiveness of the proposed method was tested with very high-resolution (VHR) remote sensing images, including Pleiades satellite images and aerial imagery. Compared with the speeded-up robust features (SURF)-based method, phase correlation with the Blackman window function produced 91% more matches with high reliability. Moreover, the results of the optimization analysis have revealed that Nelder–Mead algorithm performs better than the two-point step size gradient algorithm regarding localization accuracy and computation time. The proposed approach achieves better accuracy than 0.5 pixels and outperforms the speeded-up robust features (SURF)-based method. It can achieve sub-pixel accuracy in the presence of noise and produces large numbers of correct matching points.

Sub-pixel registration of multi-resolution imagery by correlation matching of the bathymetry-related features.

Multispectral imaging plays a significant role in coastal mapping and monitoring applications. For tasks involving the integration of multiple overlapped images, precise co-registration of the multisource satellite images is a crucial preliminary step. However, due to the limited terrestrial area and insufficient landscape features, the traditional methods become less efficient or even invalid in offshore island environments. This study addresses the problem by exploring the feasibility of using bathymetry information for geometric registration of satellite imagery. Instead of using the ground control points (GCPs) or extracting the tie points from the landscape features, the band ratio values are extracted from the multispectral images and are subsequently matched between different images through a correlation-based similarity measure. By searching the optimum correlation within the positioning uncertainty radius, the translation between two satellite images is estimated. Thus, the geometric inconsistency between the multispectral images of different sources and resolutions is effectively reduced. This result is obtained by using the ample bathymetry features without the aid of the GCPs and the in-situ bathymetry data. The experimental results using GeoEye-1, Sentinel-2, and Landsat-8 images at Ganquan Island show that for an island setting with a limited terrestrial area, the developed method achieves sub-pixel registration accuracy (less than 2 m) in planimetry. The effect of the nonlinearity and outliers are accounted for using the Spearman correlation measure. The improvement in image alignment enables the integration of multispectral images of different sources and resolutions for producing an accurate and consistent interpretation for coastal comparative and synergistic applications.

Improving Landsat Multispectral Scanner (MSS) geolocation by least-squares-adjustment based time-series co-registration

The Landsat Multispectral Scanner (MSS) data sensed by the Landsat 1–5 satellites make up a significant portion of the early Landsat data record. However, accurate MSS image geolocation has been difficult to achieve systematically due to a number of factors associated primarily with the older sensor and satellite technology. As of August 2019, only 49% of the Landsat MSS archive could be processed at the highest-level L1TP (precision and terrain corrected) level, and the remainder were processed as L1GS (systematically corrected) with inaccurate geolocation and no terrain correction. This paper presents a methodology to improve the geolocation of MSS time series. The methodology uses an area- and feature-based least-squares matching scale-space algorithm, with a time series registration implementation, that we developed previously using Landsat-8 and Sentinel-2 imagery. The methodology requires that at least three L1TP images in the time series acquired over a given path/row are available. A linear combination of a polynomial transformation and multiple radially symmetric radial-basis-functions (RBFs) to model local uncorrected terrain relief effects present in the L1GS images are used. The processing is automated and applied in two passes. The first pass screens L1TP images to select the well-aligned ones that are used as references. The second pass registers the target images, including the L1GS images and any misaligned L1TP images, to all the reference L1TP images. The transformation coefficients for each registered target image are derived by least-squares adjustment using densely-matched tie-points between the target and the reference images. The methodology is demonstrated using 12 months of Landsat-4 MSS images at four Landsat path/row locations that contain agricultural, mountainous, and coastal regions, including a total of 43 L1TP and 31 L1GS images. There were sufficient tie-points to characterize the degree of misregistration of 14 L1GS images that had significant mean misregistration shifts ranging from 7.33 to 17.42 60 m pixels. In addition, at one site, two L1TP images were found to be misaligned and have mean misregistration shifts of 1.27 and 2.20 60 m pixels. The methodology provides sub-pixel registration accuracy - after registration, the mean misregistration shifts for the 14 L1GS and two misaligned L1TP images varied from only 0.10 to 0.41 60 m pixels. The methodology does not use a digital elevation model, and examples illustrate that although the RBF transformations can compensate terrain relief distortion effects, larger (~0.5 to 1.0 pixel) misregistration errors can remain in areas with highly variable terrain relief. Results are also provided for Landsat-1 MSS imagery to demonstrate the applicability of the methodology to even the earliest part of the Landsat record. Detailed qualitative and quantitative results are presented and indicate the potential of the methodology to improve the geolocation of the Landsat MSS data record that is discussed with recommendations for future research.

High-throughput measurement of coefficient of thermal expansion using a high-resolution digital single-lens reflex camera and digital image correlation.

High-throughput measurement of thermal deformation and determination of coefficient of thermal expansion (CTE) using a high-resolution digital single lens reflex (DSLR) camera and digital image correlation (DIC) is described. To mitigate the mosaic effect caused by the Bayer filter of DSLR cameras, a color image pre-processing method, which adjusts the brightness and equalizes the color channels of the raw image, is carried out. In addition, a Gaussian pre-filtering step is adopted for denoising the images captured with DSLR cameras to enhance the subpixel registration accuracy. Then, by processing the recorded images using the state-of-the-art DIC algorithm, full-field displacements and strains can be determined. Compared with conventional industrial cameras, a DSLR camera offers not only portability, compactness, and economy but also much higher resolution of recorded images, allowing CTE characterization with higher throughput. Real experiments, including a verification experiment of the color image pre-processing technique, a benchmark CTE determination of Al alloy, and a high-throughput CTE determination of 15 samples of three different metals, validated the feasibility and accuracy of the proposed technique. The proposed method is cost-effective and time-saving, showing great potential in the high-throughput CTE measurement and other high-throughput strain measurement scenarios.

Crater Detection and Registration of Planetary Images Through Marked Point Processes, Multiscale Decomposition, and Region-Based Analysis

Because of the large variety of planetary sensors and spacecraft already collecting data and with many new and improved sensors being planned for future missions, planetary science needs to integrate numerous multimodal image sources, and, as a consequence, accurate and robust registration algorithms are required. In this article, we develop a new framework for crater detection based on marked point processes (MPPs) that can be used for planetary image registration. MPPs were found to be effective for various object detection tasks in Earth observation, and a new MPP model is proposed here for detecting craters in planetary data. The resulting spatial features are exploited for registration, together with fitness functions based on the MPP energy, on the mean directed Hausdorff distance, and on the mutual information. Two different methods—one based on birth–death processes and region-of-interest analysis and the other based on graph cuts and decimated wavelets—are developed within the proposed framework. Experiments with a large set of images, including 13 thermal infrared and visible images of the Mars surface, 20 semisimulated multitemporal pairs of images of the Mars surface, and a real multitemporal image pair of the Lunar surface, demonstrate the effectiveness of the proposed framework in terms of crater detection performance as well as for subpixel registration accuracy.

A Multitemporal Remote Sensing Image Registration Method Based on Water Bodies for the Lake-Rich Region

Accurate multitemporal remotely sensed image registration is essential for water body change detection and analysis of lake dynamics. However, lake-rich regions, such as Siberia, are mostly dominated by rivers and lakes, with few stable geometry features such as those typically used as control points for image registration. Lakes in Arctic regions are generally not static; their shorelines tend to expand and shrink seasonally, and may change substantially between different years, making it difficult to find consistent features for image registration. Consequently, traditional image-to-image registration methods, and even many sophisticated registration algorithms, rarely achieve accurate geometric correction in Arctic regions due to a lack of sufficient control points. In this article, we proposed a summary lake spatial attribute, the inferred deepest point of the lake (DPL), as a feature that is relatively insensitive to lake area changes, and therefore useful for registration of multitemporal images. The central focus of the DPL estimation algorithm is the identification of the largest inner circle (LIC) of the lake polygon. First, the internal Voronoi diagram of a lake polygon is calculated by the ``divide-and-conquer” method. The medial axis (MA) is then calculated by Voronoi diagram simplification, and finally, the LIC center is obtained by computing the distance from the MA intersection to all polygon edges. The approach was used to register HJ-1 and Landsat multispectral scanner (MSS) images in Siberia, where water bodies dominate the landscape and change significantly over time. The proposed method found a large number of control points from the extracted water bodies. Subpixel registration accuracy of 0.62 pixels (18.5 m) and 0.33 pixels (19.6 m) root mean square error (RMSE) was obtained for the HJ-1 and MSS images, respectively. In comparison, the alternative method of using lake centroids, only achieved 0.75 pixels (22.4 m) and 0.49 pixels (29.3 m) RMSE. This registration accuracy improvement is potentially important for large-scale regional cartography and change detection applications.

Registration Algorithm Based on Line-Intersection-Line for Satellite Remote Sensing Images of Urban Areas

Image registration is an important step in remote sensing image processing, especially for images of urban areas, which are often used for urban planning, environmental assessment, and change detection. Urban areas have many artificial objects whose contours and edges provide abundant line features. However, the locations of line endpoints are greatly affected by large background variations. Considering that line intersections remain relatively stable and have high positioning accuracy even with large background variations, this paper proposes a high-accuracy remote sensing image registration algorithm that is based on the line-intersection-line (LIL) structure, with two line segments and their intersection. A double-rectangular local descriptor and a spatial relationship-based outlier removal strategy are designed on the basis of the LIL structure. First, the LILs are extracted based on multi-scale line segments. Second, LIL local descriptors are built with pixel gradients in the LIL neighborhood to realize initial matching. Third, the spatial relations between initial matches are described with the LIL structure and simple affine properties. Finally, the graph-based LIL outlier removal strategy is conducted and incorrect matches are eliminated step by step. The proposed algorithm is tested on simulated and real images and compared with state-of-the-art methods. The experiments prove that the proposed algorithm can achieve sub-pixel registration accuracy, high precision, and robust performance even with significant background variations.

A Coarse-to-Fine Registration Strategy for Multi-Sensor Images with Large Resolution Differences

Automatic image registration for multi-sensors has always been an important task for remote sensing applications. However, registration for images with large resolution differences has not been fully considered. A coarse-to-fine registration strategy for images with large differences in resolution is presented. The strategy consists of three phases. First, the feature-base registration method is applied on the resampled sensed image and the reference image. Edge point features acquired from the edge strength map (ESM) of the images are used to pre-register two images quickly and robustly. Second, normalized mutual information-based registration is applied on the two images for more accurate transformation parameters. Third, the final transform parameters are acquired through direct registration between the original high- and low-resolution images. Ant colony optimization (ACO) for continuous domain is adopted to optimize the similarity metrics throughout the three phases. The proposed method has been tested on image pairs with different resolution ratios from different sensors, including satellite and aerial sensors. Control points (CPs) extracted from the images are used to calculate the registration accuracy of the proposed method and other state-of-the-art methods. The feature-based preregistration validation experiment shows that the proposed method effectively narrows the value range of registration parameters. The registration results indicate that the proposed method performs the best and achieves sub-pixel registration accuracy of images with resolution differences from 1 to 50 times.

Robust and Accurate Algorithm for Wearable Stereoscopic Augmented Reality with Three Indistinguishable Markers

In the context of surgical navigation systems based on augmented reality (AR), the key challenge is to ensure the highest degree of realism in merging computer-generated elements with live views of the surgical scene. This paper presents an algorithm suited for wearable stereoscopic augmented reality video see-through systems for use in a clinical scenario. A video-based tracking solution is proposed that relies on stereo localization of three monochromatic markers rigidly constrained to the scene. A PnP-based optimization step is introduced to refine separately the pose of the two cameras. Video-based tracking methods using monochromatic markers are robust to non-controllable and/or inconsistent lighting conditions. The two-stage camera pose estimation algorithm provides sub-pixel registration accuracy. From a technological and an ergonomic standpoint, the proposed approach represents an effective solution to the implementation of wearable AR-based surgical navigation systems wherever rigid anatomies are involved.

Digital Image Correlation with Enhanced Accuracy and Efficiency: A Comparison of Two Subpixel Registration Algorithms

The two major subpixel registration algorithms, currently being used in subset-based digital image correlation, are the classic Newton-Raphson (FA-NR) algorithm with forward additive mapping strategy and the recently introduced inverse compositional Gauss-Newton (IC-GN) algorithm. Although the equivalence of these two algorithms has been proved in existing studies, practical implementations of the two subpixel registration algorithms do involve differences, and therefore lead to different performance. In the present work, detailed theoretical error analyses of the two algorithms are performed. Based on the simple sum of squared difference criterion and the practical first-order shape function, analytic formulae that can quantify both the bias error (systematic error) and the variability (random error) in the displacements measured by IC-GN and FA-NR algorithms with various interpolation methods (i.e., cubic convolution interpolation, cubic polynomial interpolation, cubic B-spline interpolation and quintic B-spline interpolation) are derived. It is shown that, compared with FA-NR algorithm, IC-GN algorithm leads to reduced bias error in displacement estimation by eliminating noise-induced bias error, and gives rise on the average to smaller random errors in displacement estimation in the cases of high noise levels or using small subsets. Numerical tests with precisely controlled subpixel displacements confirm the correctness of the theoretical derivations. The results reveal that IC-GN algorithm outperforms the classic FA-NR algorithm not only in terms of computational efficiency, but also in respect of subpixel registration accuracy and noise-proof performance, and is strongly recommended as a standard subpixel registration algorithm for practical DIC applications instead of FA-NR algorithm.

A Precise Lower Bound on Image Subpixel Registration Accuracy

A new performance bound is proposed for analyzing parametric image registration methods objectively. This original bound is derived from the Cramer-Rao lower bound on the estimation error of parameters involved in a geometric transformation assumed between reference and template images (pure translation in this work) and parameters describing the texture of these images. For describing local fragments of both the reference and the template images, the parametric fractional Brownian motion (fBm) model has been chosen. Experimental results, obtained first on pure fBm data with full matching of the data to the texture model assumption, give evidence that the proposed bound describes more adequately the performance of conventional estimators than two other bounds previously proposed in the literature. This holds with respect to the signal-to-noise ratio value of both images, the roughness of their texture, their correlation, and the actual value of translation parameters between their grids. Then, one real Hyperion hyperspectral data set is considered to test the proposed bound behavior on real data. The proposed bound is demonstrated to describe more adequately the estimation accuracy of the translation parameters between different bands of this data set.

Real-time whole slide mosaicing for non-automated microscopes in histopathology analysis

Context: Mosaics of Whole Slides (WS) are a valuable resource for pathologists to have the whole sample available at high resolution. The WS mosaic provides pathologists with an overview of the whole sample at a glance, helping them to make a reliable diagnosis. Despite recent solutions exist for creating WS mosaics based, for instance, on automated microscopes with motorized stages or WS scanner, most of the histopathology analysis are still performed in laboratories endowed with standard manual stage microscopes. Nowadays, there are lots of dedicated devices and hardware to achieve WS automatically and in batch, but only few of them are conceived to work tightly connected with a microscope and none of them is capable of working in real-time with common light microscopes. However, there is a need of having low-cost yet effective mosaicing applications even in small laboratories to improve routine histopathological analyses or to perform remote diagnoses. Aims: The purpose of this work is to study and develop a real-time mosaicing algorithm working even using non-automated microscopes, to enable pathologists to achieve WS while moving the holder manually, without exploiting any dedicated device. This choice enables pathologists to build WS in real-time, while browsing the sample as they are accustomed to, helping them to identify, locate, and digitally annotate lesions fast. Materials and Methods: Our method exploits fast feature tracker and frame to frame registration that we implemented on common graphics processing unit cards. The system work with common light microscopes endowed with a digital camera and connected to a commodity personal computer. Result and Conclusion: The system has been tested on several histological samples to test the effectiveness of the algorithm to work with mosaicing having different appearances as far as brightness, contrast, texture, and detail levels are concerned, attaining sub-pixel registration accuracy at real-time interactive rates.

Fast, Robust and Accurate Digital Image Correlation Calculation Without Redundant Computations

High-efficiency and high-accuracy deformation analysis using digital image correlation (DIC) has become increasingly important in recent years, considering the ongoing trend of using higher resolution digital cameras and common requirement of processing a large sequence of images recorded in a dynamic testing. In this work, to eliminate the redundant computations involved in conventional DIC method using forward additive matching strategy and classic Newton–Raphson (FA-NR) algorithm without sacrificing its sub-pixel registration accuracy, we proposed an equivalent but more efficient DIC method by combining inverse compositional matching strategy and Gauss-Newton (IC-GN) algorithm for fast, robust and accurate full-field displacement measurement. To this purpose, first, an efficient IC-GN algorithm, without the need of re-evaluating and inverting Hessian matrix in each iteration, is introduced to optimize the robust zero-mean normalized sum of squared difference (ZNSSD) criterion to determine the desired deformation parameters of each interrogated subset. Then, an improved reliability-guided displacement tracking strategy is employed to achieve further speed advantage by automatically providing accurate and complete initial guess of deformation for the IC-GN algorithm implemented on each calculation point. Finally, an easy-to-implement interpolation coefficient look-up table approach is employed to avoid the repeated calculation of bicubic interpolation at sub-pixel locations. With the above improvements, redundant calculations involved in various procedures (i.e. initial guess of deformation, sub-pixel displacement registration and sub-pixel intensity interpolation) of conventional DIC method are entirely eliminated. The registration accuracy and computational efficiency of the proposed DIC method are carefully tested using numerical experiments and real experimental images. Experimental results verify that the proposed DIC method using IC-GN algorithm and the existing DIC method using classic FA-NR algorithm generate similar results, but the former is about three to five times faster. The proposed reliability-guided IC-GN algorithm is expected to be a new standard full-field displacement tracking algorithm in DIC.

The northern hemisphere of asteroid (21) Lutetia—topography and orthoimages from Rosetta OSIRIS NAC image data

During the flyby at asteroid (21) Lutetia by the Rosetta spacecraft in July 2010, the OSIRIS imaging system onboard acquired several hundred images of Lutetia's surface. Images of the OSIRIS NAC (Narrow Angle Camera) comprise up to 60m/pixel ground resolution. We analyzed 84 multi-spectral NAC images with stereo-photogrammetric methods. A 3D point control network within a photogrammetric block adjustment was used to derive improved orientation data (pointing and position) for the Rosetta spacecraft as well as corrections of the pre-flight estimates of Lutetia's position in space. For the generation of a digital terrain model (DTM), we selected a subset of 10 OSIRIS NAC images within a multi-image matching process. We combined the matching results with the adjusted orientation data and derived more than 12 million of surface points with a 3D point accuracy of ∼40m (2/3pixel) and a vertical component of that of ∼15m (1/4pixel). The 3D surface points were converted to a final 60m raster DTM, which covers about 78% of the entire northern hemisphere. All 84 NAC images were then ortho-rectified on the basis of the improved orientation and the DTM. Thus, orthoimage products with sub-pixel registration accuracy are available for multi-spectral mapping of the surface of Lutetia. Finally, the DTM was textured with an orthoimage mosaic to form a VRML dataset for full-resolution interactive 3D investigations.

A fast digital image correlation method for deformation measurement

Fast and high-accuracy deformation analysis using digital image correlation (DIC) has been increasingly important and highly demanded in recent years. In literature, the DIC method using the Newton–Rapshon (NR) algorithm has been considered as a gold standard for accurate sub-pixel displacement tracking, as it is insensitive to the relative deformation and rotation of the target subset and thus provides highest sub-pixel registration accuracy and widest applicability. A significant drawback of conventional NR-algorithm-based DIC method, however, is its extremely huge computational expense. In this paper, a fast DIC method is proposed deformation measurement by effectively eliminating the repeating redundant calculations involved in the conventional NR-algorithm-based DIC method. Specifically, a reliability-guided displacement scanning strategy is employed to avoid time-consuming integer–pixel displacement searching for each calculation point, and a pre-computed global interpolation coefficient look-up table is utilized to entirely eliminate repetitive interpolation calculation at sub-pixel locations. With these two approaches, the proposed fast DIC method substantially increases the calculation efficiency of the traditional NR-algorithm-based DIC method. The performance of proposed fast DIC method is carefully tested on real experimental images using various calculation parameters. Results reveal that the computational speed of the present fast DIC is about 120–200 times faster than that of the traditional method, without any loss of its measurement accuracy

Fully Automatic Subpixel Image Registration of Multiangle CHRIS/Proba Data

Subpixel image registration is the key to successful image fusion and superresolution enhancement of multiangle satellite data. Multiangle image registration poses two main challenges: 1) Images captured at large view angles are susceptible to resolution change and blurring, and 2) local geometric distortion caused by topographic effects and/or platform instability may be important. In this paper, we propose a two-step nonrigid automatic registration scheme for multiangle satellite images. In the first step, control points (CPs) are selected in a preregistration process based on the scale-invariant feature transform (SIFT). However, the number of CPs obtained in this first step may be too few and/or CPs may be unevenly distributed. To remediate these problems, in a second step, the preliminary registered image is subdivided into chips of 64 × 64 pixels, and each chip is matched with a corresponding chip in the reference image using normalized cross correlation (NCC). By doing so, more CPs with better spatial distribution are obtained. Two criteria are applied during the generation of CPs to identify outliers. Selected SIFT and NCC CPs are used for defining a nonrigid thin-plate-spline model. The proposed registration scheme has been tested using data from the Compact High Resolution Imaging Spectrometer (CHRIS) onboard the Project for On-Board Autonomy (Proba) satellite. Experimental results demonstrate that the proposed method works well in areas with little variation in topography. Application in areas with more pronounced relief would require the use of orthorectified image data in order to achieve subpixel registration accuracy.