Subpixel SAR image registration through parabolic interpolation of the 2D cross-correlation

In this article, the problem of synthetic aperture radar (SAR) images coregistration is considered. In particular, a novel algorithm aimed at achieving a fine subpixel coregistration accuracy is developed. The procedure is based on the parabolic interpolation of the 2-D cross correlation computed between the two SAR images to be aligned. More precisely, from the 2-D cross correlation, a neighborhood of its peak value is extracted and the interpolation of both the 2-D paraboloid and the two alternative 1-D parabolas is computed to provide the finer misregistration estimation with subpixel accuracy. The main advantage of the proposed framework is that the overall computational burden is only due to the 2-D cross correlation estimation since the parabolic interpolation is calculated with a closed-form expression. The results obtained on real recorded unmanned aerial vehicle (UAV) SAR data highlight the effectiveness of the proposed approach as well as its capabilities to provide some benefits with respect to other available strategies

A joint coregistration of rotated multitemporal SAR images based on the cross-cross-correlation

Accurate synthetic aperture radar (SAR) images coregistration is on the base of several remote sensing applications, such as interferometry, change detection, etc. This paper proposes a new algorithm for jointly coregister a stack of multitemporal SAR images exploiting the cross-correlations computed for each couple of patches' cross-correlation. By doing so, the method is capable of exploit also the respective misregistration information between the slave during the estimation process. This methodology is applied to improve the performance of the constrained Least Squares (CLS) optimization method that does not account for the reciprocal information related to the slaves. Tests on real-recorded data shown the benefits of the proposed method in terms of root mean square error (RMSE) for images affected by respective rotations

SAR image registration in the presence of rotation and translation : a constrained least squares approach

This letter proposes a coregistration algorithm to compensate for possible inaccuracy of trajectory sensor during the SAR image acquisition process. Such a misalignment can be modeled as a pure displacement in range and azimuth directions and a rotation effect due to different angle of sight. The approach is formalized as a Constrained Least Squares (CLS) optimization problem enforcing a constraint of absence of a zooming effect between the two SAR images. Moreover, system equations can optionally be weighted according to local properties between the extracted patches within the quoted couple. Interestingly, the solution can be obtained in closed-form, therefore with a low computational cost. The results of the tests conducted on the 9.6GHz Gotcha SAR data demonstrate the capability of the strategy to proper register the imagery

Coregistration method for rotated/shifted FOPEN SAR images

This paper tests a SAR image coregistration method, developed to account for a joint rotation and range/azimuth shift effect in absence of zooming, on foliage penetrating (FOPEN) data. In particular, the method is referred as a constrained Least Squares (CLS) optimization method and, in its basic form, it sharply extracts all patches composing the entire image. Differently, in next developments it applies a detection stage to identify extended areas in the images where patches are then selected. Moreover, it also performs a refinement of the equations in the CLS problem through an iterative cancellation procedure. The performance of this enhanced version of the CLS are made on the challenging Carabas-II VHF-band FOPEN SAR data to demonstrate its effectiveness also in high-resolution SAR images

Deriving glacier surface velocities from repeat optical images

The velocity of glaciers is important for many aspects in glaciology. Mass accumulated in the accumulation area is transported down to the ablation area by deformation and sliding due to the gravitational force, and hence gla­cier velocity is connected to the mass balance of glaciers. It also contributes directly to the mass balance of calving glaciers because it is an important control of the ice discharge rate for such glaciers. Changing glacier velocities is an indicator of instable glaciers, and monitoring velocity over time can make people aware of possible hazards that may arise from instable glaciers. The movement of glaciers is also important for transporting material and for eroding the landscape. The focus of this thesis is to further develop image matching within glaciology. In image matching, images from two di.erent times are compared us­ing correlation techniques to derive glacier displacement over the time period. Most studies have concentrated on using image matching to derive glacier velocities instead of developing this method further. To be able to derive the densest possible velocity grids for all glaciers in the world, image matching methods over glacier surfaces have to be explored further. So far all images that have been used to derive velocity in glaciology have been high or medium spatial resolution images. Low resolution images cover large sections in one image, and this makes them suited for investigating the velocity of large areas such as Antarctic ice shelves. We derive velocities for Antarctic ice shelves using MODIS images with a spatial resolution of 250 m to test whether these images are suited for deriving ice shelf velocity. Because the accuracy is about one fourth of a pixel, and it is possible to use images acquired several years apart due to the low surface transformation, MODIS images are well suited for deriving velocity of Antarctic ice shelves and also to monitor their changes over time. We found when comparing di.erent image matching methods over dif­ferent glacier surfaces that the most commonly used method, normalized cross-correlation, generally performs worse compared to orientation correla­tion and the matching part of the program COSI-Corr. The only situation where normalized cross-correlation outperforms the two other methods are on narrow glaciers where small window sizes are needed. COSI-Corr per­forms best overall, but orientation correlation performs almost as well. In addition orientation correlation is the only method that manages to match striped Landsat images after the failure of the Scan Line Corrector. Both orientation correlation and COSI-Corr are considered to be methods well suited for global glacier velocity mapping. Normalized cross-correlation can supplement these two methods on narrow glaciers. The effort that has been put into developing image matching in glaciology since the start of this study, both in this study and in other studies, makes it possible to derive glacier velocities over large regions, and only computer processing time hinders automatic matching of glacier velocities worldwide. Global glacier velocities can give valuable insights. We show in this thesis that it can give information about how glaciers respond to climate change. Glacier velocity of .ve regions of the world with negative mass balance is derived, and in all regions the general glacier speed is decreasing over the last decades. In addition global glacier velocities can be used to understand glacier dynamics, and predict glacier hazards. It can be tested against gla­cier inventory parameters, and it can be used to estimate erosion rates and transport times

SAR coregistration by robust selection of extended targets and iterative outlier cancellation

This letter extends the constrained least-squares (CLS) optimization method developed to coregister multitemporal synthetic aperture radar (SAR) images affected by a joint rotation effect and range/azimuth shifts enforcing the absence of zooming effects. To take advantage of the structural information extracted from the scene, the method starts with a detection stage that identifies extended targets/areas in the images. The selected tie-points allow the CLS problem to be reformulated to find its (initial) solution based on a robust subset of image blocks. Then, the mean square error (MSE) of each equation evaluated from the initial solution allows to implement an iterative cancellation procedure to further skim the CLS equation set. The effectiveness of the proposed procedure is validated on real SAR data in comparison with the standard CLS

A cross-cross-correlation based method for joint coregistration of rotated multitemporal synthetic aperture radar images

Coregistration is among the most important and challenging tasks when dealing with multiple synthetic aperture radar (SAR) images, especially when they are acquired at different time instants and characterised by low signal to noise power ratio (SNR) that contributes to their coherence reduction. However, even if some technological expedients could be implemented to maintain the same trajectory and to compensate for these inaccuracies during the acquisition campaign, multitemporal SAR images always need additional registration refinements after compression. Usually, to coregister a series of multitemporal SAR images, one of them is selected as the master, and the remainders are separately registered to it. Differently, in this study, a new strategy is developed to jointly coregister a stack of multitemporal SAR images. It is based on the exploitation of the cross-correlations in turn computed from each couple of cross-correlations (a.k.a. cross-cross-correlations) of the extracted patches. By doing so, the method is capable of exploiting also the respective misregistration information between the slaves during the estimation process. In this respect, this methodology is applied to enhance the registration capabilities of the constrained Least Squares (CLS) optimisation method, which instead does not account for the reciprocal information related to the slaves. Several tests are performed on multitemporal airborne-measured SAR data. Obtained results show the effectiveness of the proposed algorithm in terms of achieved root mean square error for images affected by respective rotations also in comparison with the CLS counterpart

Matching of repeat remote sensing images for precise analysis of mass movements

Photogrammetry, together with radar interferometry, is the most popular of the remote sensing techniques used to monitor stability of high mountain slopes. By using two images of an area taken from different view angles, photogrammetry produces digital terrain models (DTM) and orthoprojected images. Repeat digital terrain models (DTM) are differenced to compute elevation changes. Repeat orthoimages are matched to compute the horizontal displacement and deformation of the masses. The success of the photogrammetric approach in the computation of horizontal displacement (and also the generation of DTM through parallax matching, although not covered in this work) greatly relies on the success of image matching techniques. The area-based image matching technique with the normalized cross-correlation (NCC) as its similarity measure is widely used in mass movement analysis. This method has some limitations that reduce its precision and reliability compared to its theoretical potential. The precision with which the matching position is located is limited to the pixel size unless some sub-pixel precision procedures are applied. The NCC is only reliable in cases where there is no significant deformation except shift in position. Identification of a matching entity that contains optimum signal-to-noise ratio (SNR) and minimum geometric distortion at each location has always been challenging. Deformation parameters such as strains can only be computed from the inter-template displacement gradient in a post-matching process. To find appropriate solutions for the mentioned limitations, the following investigations were made on three different types of mass movements; namely, glacier flow, rockglacier creep and land sliding. The effects of ground pixel size on the accuracy of the computed mass movement parameters such as displacement were investigated. Different sub-pixel precision algorithms were implemented and evaluated to identify the most precise and reliable algorithm. In one approach images are interpolated to higher spatial resolution prior to matching. In another approach the NCC correlation surface is interpolated to higher resolution so that the location of the correlation peak is more precise. In yet another approach the position of the NCC peak is computed by fitting 2D Gaussian and parabolic curves to the correlation peak turn by turn. The results show that the mean error in metric unit increases linearly with the ground pixel size being about half a pixel at each resolution. The proportion of undetected moving masse increases with ground pixel size depending on the displacement magnitudes. Proportion of mismatching templates increases with increasing ground pixel size depending on the noise content, i.e. temporal difference, of the image pairs. Of the sub-pixel precision algorithms, interpolating the image to higher resolution using bi-cubic convolution prior to matching performs best. For example, by increasing the spatial resolution (i.e. reducing the ground pixel size) of the matched images by 2 to 16 times using intensity interpolation, 40% to 80% of the performances of the same resolution original image can be achieved. A new spatially adaptive algorithm that defines the template sizes by optimizing the SNR, minimizing the geometric distortion and optimizing the similarity measure was also devised, implemented and evaluated on aerial and satellite images of mass movements. The algorithm can also exclude ambiguous and occluded entities from the matching. The evaluation of the algorithm was conducted on simulated deformation images and in relation to the image-wide fixed template sizes ranging from 11 to 101 pixels. The evaluation of the algorithm on the real mass movements is conducted by a novel technique of reconstructing the reference image from the deformed image and computing the global correlation coefficient and the corresponding SNR between the reference and the reconstructed image. The results show that the algorithm could reduce the error of displacement estimation by up to over 90% (in the simulated case) and improve the SNR of the matching by up to over 4 times compared to the globally fixed template sizes. The algorithm pushes terrain displacement measurement from repeat images one step forward towards full automation. The least squares image matching (LSM) matches images precisely by modeling both the geometric and radiometric deformation. The potential of the LSM is not fully utilized for mass movement analysis. Here, the procedures with which horizontal surface displacement, rotation and strain rates of glacier flow, rockglacier creep and land sliding are computed from the spatial transformation parameters of LSM automatically during the matching are implemented and evaluated. The results show that the approach computes longitudinal strain rates, transverse strain rates and shear strain rates reliably with mean absolute deviation in the order of 10-4 as evaluated on stable grounds. The LSM also improves the accuracy of displacement estimation of the NCC by about 90% in ideal (simulated) case and the SNR of the matching by about 25% in real multi-temporal images of mass movements. Additionally, advanced spatial transformation models such as projective and second degree polynomial are used for the first time for mass movement analysis in addition to the affine. They are also adapted spatially based on the minimization of the sum of square deviation between the matching templates. The spatially adaptive approach produces the best matching, closely followed by the second-order polynomial. Affine and projective models show similar results closely following the two approaches. In the case of the spatially adaptive approach, over 60% of the entities matched for the rockglacier and the landslide are best fit by the second-order polynomial model. In general, the NCC alone may be sufficient for low resolution images of moving masses with limited or no deformation. To gain better precision and reliability in such cases, the template sizes can be adapted spatially and the images can be interpolated to higher resolution (preferably not more detail than 1/16th of a pixel) prior to the matching. For highly deformed masses where higher resolution images are used, the LSM is recommended as it results in more accurate matching and deformation parameters. Improved accuracy and precision are obtained by selecting matchable areas using the spatially adaptive algorithm, identifying approximate matches using the NCC and optimizing the matches and measuring the deformation parameters using the LSM algorithm

GLAcier Feature Tracking testkit (GLAFT): a statistically and physically based framework for evaluating glacier velocity products derived from optical satellite image feature tracking

Glacier velocity measurements are essential to understand ice flow mechanics, monitor natural hazards, and make accurate projections of future sea-level rise. Despite these important applications, the method most commonly used to derive glacier velocity maps, feature tracking, relies on empirical parameter choices that rarely account for glacier physics or uncertainty. Here we test two statistics- and physics-based metrics to evaluate velocity maps derived from optical satellite images of Kaskawulsh Glacier, Yukon, Canada, using a range of existing feature-tracking workflows. Based on inter-comparisons with ground truth data, velocity maps with metrics falling within our recommended ranges contain fewer erroneous measurements and more spatially correlated noise than velocity maps with metrics that deviate from those ranges. Thus, these metric ranges are suitable for refining feature-tracking workflows and evaluating the resulting velocity products. We have released an open-source software package for computing and visualizing these metrics, the GLAcier Feature Tracking testkit (GLAFT).</p