Towards Automatic SAR-Optical Stereogrammetry over Urban Areas using Very High Resolution Imagery

In this paper we discuss the potential and challenges regarding SAR-optical stereogrammetry for urban areas, using very-high-resolution (VHR) remote sensing imagery. Since we do this mainly from a geometrical point of view, we first analyze the height reconstruction accuracy to be expected for different stereogrammetric configurations. Then, we propose a strategy for simultaneous tie point matching and 3D reconstruction, which exploits an epipolar-like search window constraint. To drive the matching and ensure some robustness, we combine different established handcrafted similarity measures. For the experiments, we use real test data acquired by the Worldview-2, TerraSAR-X and MEMPHIS sensors. Our results show that SAR-optical stereogrammetry using VHR imagery is generally feasible with 3D positioning accuracies in the meter-domain, although the matching of these strongly hetereogeneous multi-sensor data remains very challenging. Keywords: Synthetic Aperture Radar (SAR), optical images, remote sensing, data fusion, stereogrammetr

A Framework for SAR-Optical Stereogrammetry over Urban Areas

Currently, numerous remote sensing satellites provide a huge volume of diverse earth observation data. As these data show different features regarding resolution, accuracy, coverage, and spectral imaging ability, fusion techniques are required to integrate the different properties of each sensor and produce useful information. For example, synthetic aperture radar (SAR) data can be fused with optical imagery to produce 3D information using stereogrammetric methods. The main focus of this study is to investigate the possibility of applying a stereogrammetry pipeline to very-high-resolution (VHR) SAR-optical image pairs. For this purpose, the applicability of semi-global matching is investigated in this unconventional multi-sensor setting. To support the image matching by reducing the search space and accelerating the identification of correct, reliable matches, the possibility of establishing an epipolarity constraint for VHR SAR-optical image pairs is investigated as well. In addition, it is shown that the absolute geolocation accuracy of VHR optical imagery with respect to VHR SAR imagery such as provided by TerraSAR-X can be improved by a multi-sensor block adjustment formulation based on rational polynomial coefficients. Finally, the feasibility of generating point clouds with a median accuracy of about 2m is demonstrated and confirms the potential of 3D reconstruction from SAR-optical image pairs over urban areas.Comment: This is the pre-acceptance version, to read the final version, please go to ISPRS Journal of Photogrammetry and Remote Sensing on ScienceDirec

Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images

Improving the geo-localization of optical satellite images is an important pre-processing step for many remote sensing tasks like scene monitoring over time or the scene analysis after sudden events. These tasks often require the fusion of geo-referenced and precisely co-registered multi-sensor data. Images captured by high resolution synthetic aperture radar (SAR) satellites have an absolute geo-location accuracy within few decimeters. This renders SAR images interesting as a source for the geo-location improvement of optical images, whose geo-location accuracy is in the range of some meters. In this paper, we are investigating a deep learning based approach for the geo-localization accuracy improvement of optical satellite images through SAR reference data. Image registration between SAR and optical satellite images requires few but accurate and reliable matching points. To derive such matching points a neural network based on a Siamese network architecture was trained to learn the two dimensional spatial shift between optical and SAR image patches. The neural network was trained over TerraSAR-X and PRISM image pairs covering greater urban areas spread over Europe. The results of the proposed method confirm that accurate and reliable matching points are generated with a higher matching accuracy and precision than state-of-the-art approaches

A first comparison of Cosmo-Skymed and TerraSAR-X data over Chamonix Mont-Blanc test-site

International audienceThis paper presents the first results obtained with satellite im- age time series (SITS) acquired by Cosmo-SkyMed (CSK) over the Chamonix Mont-Blanc test-site. A CSK SITS made of 39 images is merged with a TerraSAR-X SITS made of 26 images by using the orbital information and co-registration tools developed in the EFIDIR project. The results are illus- trated by the computation of speckle-free images by temporal averaging, by the generation and comparison of topographic interferograms and by the measure of glacier displacement fields by amplitude correlation

Deep learning for inverse problems in remote sensing: super-resolution and SAR despeckling

L'abstract è presente nell'allegato / the abstract is in the attachmen

Guided patch-wise nonlocal SAR despeckling

We propose a new method for SAR image despeckling which leverages information drawn from co-registered optical imagery. Filtering is performed by plain patch-wise nonlocal means, operating exclusively on SAR data. However, the filtering weights are computed by taking into account also the optical guide, which is much cleaner than the SAR data, and hence more discriminative. To avoid injecting optical-domain information into the filtered image, a SAR-domain statistical test is preliminarily performed to reject right away any risky predictor. Experiments on two SAR-optical datasets prove the proposed method to suppress very effectively the speckle, preserving structural details, and without introducing visible filtering artifacts. Overall, the proposed method compares favourably with all state-of-the-art despeckling filters, and also with our own previous optical-guided filter

An Efficient Polyphase Filter Based Resampling Method for Unifying the PRFs in SAR Data

Variable and higher pulse repetition frequencies (PRFs) are increasingly being used to meet the stricter requirements and complexities of current airborne and spaceborne synthetic aperture radar (SAR) systems associated with higher resolution and wider area products. POLYPHASE, the proposed resampling scheme, downsamples and unifies variable PRFs within a single look complex (SLC) SAR acquisition and across a repeat pass sequence of acquisitions down to an effective lower PRF. A sparsity condition of the received SAR data ensures that the uniformly resampled data approximates the spectral properties of a decimated densely sampled version of the received SAR data. While experiments conducted with both synthetically generated and real airborne SAR data show that POLYPHASE retains comparable performance to the state-of-the-art BLUI scheme in image quality, a polyphase filter-based implementation of POLYPHASE offers significant computational savings for arbitrary (not necessarily periodic) input PRF variations, thus allowing fully on-board, in-place, and real-time implementation

PSI deformation map retrieval by means of temporal sublook coherence on reduced sets of SAR images

Prior to the application of any persistent scatterer interferometry (PSI) technique for the monitoring of terrain displacement phenomena, an adequate pixel selection must be carried out in order to prevent the inclusion of noisy pixels in the processing. The rationale is to detect the so-called persistent scatterers, which are characterized by preserving their phase quality along the multi-temporal set of synthetic aperture radar (SAR) images available. Two criteria are mainly available for the estimation of pixels' phase quality, i.e., the coherence stability and the amplitude dispersion or permanent scatterers (PS) approach. The coherence stability method allows an accurate estimation of the phase statistics, even when a reduced number of SAR acquisitions is available. Unfortunately, it requires the multi-looking of data during the coherence estimation, leading to a spatial resolution loss in the final results. In contrast, the PS approach works at full-resolution, but it demands a larger number of SAR images to be reliable, typically more than 20. There is hence a clear limitation when a full-resolution PSI processing is to be carried out and the number of acquisitions available is small. In this context, a novel pixel selection method based on exploiting the spectral properties of point-like scatterers, referred to as temporal sublook coherence (TSC), has been recently proposed. This paper seeks to demonstrate the advantages of employing PSI techniques by means of TSC on both orbital and ground-based SAR (GB-SAR) data when the number of images available is small (10 images in the work presented). The displacement maps retrieved through the proposed technique are compared, in terms of pixel density and phase quality, with traditional criteria. Two X-band datasets composed of 10 sliding spotlight TerraSAR-X images and 10 GB-SAR images, respectively, over the landslide of El Forn de Canillo (Andorran Pyrenees), are employed for this study. For both datasets, the TSC technique has showed an excellent performance compared with traditional techniques, achieving up to a four-fold increase in the number of persistent scatters detected, compared with the coherence stability approach, and a similar density compared with the PS approach, but free of outliers.Peer ReviewedPostprint (published version

A study of decadal scale glacier changes of the Lunana glacier system in Bhutan, Himalaya, with considerations to glacial lake outburst floods (GLOFs)

This study assesses changes in glacier area, velocity, and geodetic mass balance for a selection of glaciers in the Lunana glacier system of Bhutan, Himalaya. It takes considerations to Glacial Lake Outburst Floods (GLOFs) by creating a glacial lake inventory of two important potential dangerous glacial lakes, Raphstreng Tsho and Luggye Tsho. Bhutan is located in the eastern parts of the HKH region and is known for its earlier GLOF events. The precipitation in Bhutan is driven by the Indian monsoon resulting in 60% annual precipitation, the high amount of rainfall results in rockfalls that covers large valley glacier tongues with debris. I studied the glacier area changes between 1976, 1996 and 2018 using freely available Landsat satellite imagery, SAR Sentinel 1&2, the SRTM Digital Elevation Model (DEM) and HMA DEM. The geodetic mass balance was calculated between 1976, 2000 and 2018/9 (for selected glaciers) using DEM constructed from high-resolution stereo images, Pléiades and SPOT, granted from the European Space Agency, as well as using the already accessed SRTM DEM and a Hexagon DEM courtesy of King, et al. (2019). The glacier velocity was generated using SAR TerraSAR-X data from 2016 and shows an average yearly displacement over the Lunana glacier system. The glacial lake time series for Raphstreng Tsho and Luggye Tsho where studied between 1993 and 2018 using a stack of freely available Landsat imagery. The results of this study, show a variety of decadal glacial changes over Lunana glacier system, with glaciers lowering on an average by 0.48± 0.08 m a-1 between 1976 and 2018/9 which calculates to a geodetic mass balance of -0.41 ± 0.068 m w.e. a-1. The system had a total average of 12.73% area of reduction for all glaciers, between the same time period. The Lunana glacier system consists of both debris-covered glaciers in the south and debris-free glaciers in the north, and as a result, the glacier changes vary between the two regions. Between 1976 – 2018/9 the southern region had an average surface melt of 0.76 ± 0.07 m a-1 which calculates to a geodetic mass balance of -0.65 ± 0.06 m w.e. a-1 and a 12.65% area of reduction. For the Northern region, the average surface melt was measured to be 1.26 ± 0.07 m a-1 which calculates to a geodetic mass balance of 1.07 ± 0.06 m w.e. a-1 and a 12.80% area of reduction. The glacier velocity was calculated to be at average of 3.05 ± 0.73 m a-1 over the south region and 3.78 ± 0.73 m a-1 over the north region. The Luggye glacier 1, located in the southern parts of Lunana glacier system, is the main input source for glacier meltwater to Luggye Tsho an ice-moraine dam proglacial lake which outburst in 1994 due to hydrostatic pressure. Between 1976 and 2018/9, the Luggye glacier 1 has had a considerable loss in surface elevation by 1.19 ± 0.07 m a-1 which calculates to a geodetic mass balance of 1.01 ± 0.069 m w.e. a-1. The 1994 GLOF event discharged over 18 million m3 of water, destroying infrastructure, flooding villages and houses which killed 21 humans. Today, Luggye Tsho is classified to yield over 1.41 km2 of water, an increase from its former state of 1.12 km2 in 1993, just before the event. This study cannot affirm if PDGLs such as Luggye Tsho is to outburst in the future, but it does affirm its growth in lake area and its input source from glacier melt over Luggye glacier, and that it should be monitored in case of potential outbreak. This can be done by doing repeated analysis of glacier velocity and calculation of glacier mass balance, as this would calculate the input source amount of meltwater to Luggye Tsho.Masteroppgave i geografiGEO350MASV-PHYGMASV-GEOGMPGEOGRMASV-MEH

A robust nonlinear scale space change detection approach for SAR images

In this paper, we propose a change detection approach based on nonlinear scale space analysis of change images for robust detection of various changes incurred by natural phenomena and/or human activities in Synthetic Aperture Radar (SAR) images using Maximally Stable Extremal Regions (MSERs). To achieve this, a variant of the log-ratio image of multitemporal images is calculated which is followed by Feature Preserving Despeckling (FPD) to generate nonlinear scale space images exhibiting different trade-offs in terms of speckle reduction and shape detail preservation. MSERs of each scale space image are found and then combined through a decision level fusion strategy, namely "selective scale fusion" (SSF), where contrast and boundary curvature of each MSER are considered. The performance of the proposed method is evaluated using real multitemporal high resolution TerraSAR-X images and synthetically generated multitemporal images composed of shapes with several orientations, sizes, and backscatter amplitude levels representing a variety of possible signatures of change. One of the main outcomes of this approach is that different objects having different sizes and levels of contrast with their surroundings appear as stable regions at different scale space images thus the fusion of results from scale space images yields a good overall performance