Geodetic Stereo SAR With Small Multi-Directional Radar Reflectors

This paper evaluates the applicability and achievable SAR accuracy for octahedrons – a combination of eight corner reflectors with a common phase centre. In an experiment at the observatory in Wettzell from July to November 2015 these cost-efficient and mobile radar targets were measured with TerraSAR-X Staring Spotlight and High-Resolution Spotlight. Applying the geodetic stereo SAR concept, octahedrons are very robust for absolute 3D positioning through their backscattering in multiple directions. Using octahedrons with as size of 47 cm, we achieve 3σ standard deviations of about 3 cm for east, north and height components. For individual measurements in Staring Spotlight the standard deviation shows 1.4 cm in range and 3.2 cm in azimuth

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

Mitigation of atmospheric perturbations and solid Earth movements in a TerraSAR-X time-series

The TerraSAR-X (TSX) synthetic aperture radar (SAR) marks the recent emergence of a new generation of spaceborne radar sensors that can for the first time lay claim to localization accuracies in the sub-meter range. The TSX platform's extremely high orbital stability and the sensor's hardware timing accuracy combine to enable direct measurements of atmospheric refraction and solid Earth movements. By modeling these effects for individual TSX acquisitions, absolute pixel geolocation accuracy on the order of several centimeters can be achieved without need for even a single tiepoint. A 16-month time series of images was obtained over a fixed test site, making it possible to validate both an atmospheric refraction and a solid Earth tide model, while at the same time establishing the instrument's long-term stability. These related goals were achieved by placing trihedral corner reflectors (CRs) at the test site and estimating their phase centers with centimeter-level accuracy using differential GPS (DGPS). Oriented in pairs toward a given satellite track, the CRs could be seen as bright "points” in the images, providing a geometric reference set. SAR images from the high-resolution spotlight (HS) mode were obtained in alternating ascending and descending orbit configurations. The highest-resolution products were selected for their small sample dimensions, as positions can be more precisely determined. Based on the delivered product annotations, the CR image positions were predicted, and these predictions were compared with their measured image positions both before and after compensation for atmospheric refraction and systematic solid Earth deviations. It was possible to show that when the atmospheric distortion and Earth tides are taken into account, the TSX HS products have geolocation accuracies far exceeding the specified requirements. Furthermore, this accuracy was maintained for the duration of the 16-month test period. It could be demonstrated that with a correctly calibrated sensor, and after accounting for atmospheric and tidal effects, tiepoint-free geolocation is possible with TSX with an absolute product accuracy of about 5c

CENTIMETER COSMO-SKYMED RANGE MEASUREMENTS FOR MONITORING GROUND DISPLACEMENTS

The SAR (Synthetic Aperture Radar) imagery are widely used in order to monitor displacements impacting the Earth surface and infrastructures. The main remote sensing technique to extract sub-centimeter information from SAR imagery is the Differential SAR Interferometry (DInSAR), based on the phase information only. However, it is well known that DInSAR technique may suffer for lack of coherence among the considered stack of images. New Earth observation SAR satellite sensors, as COSMO-SkyMed, TerraSAR-X, and the coming PAZ, can acquire imagery with high amplitude resolutions too, up to few decimeters. Thanks to this feature, and to the on board dual frequency GPS receivers, allowing orbits determination with an accuracy at few centimetres level, the it was proven by different groups that TerraSAR-X imagery offer the capability to achieve, in a global reference frame, 3D positioning accuracies in the decimeter range and even better just exploiting the slant-range measurements coming from the amplitude information, provided proper corrections of all the involved geophysical phenomena are carefully applied. The core of this work is to test this methodology on COSMO-SkyMed data acquired over the Corvara area (Bolzano – Northern Italy), where, currently, a landslide with relevant yearly displacements, up to decimeters, is monitored, using GPS survey and DInSAR technique. The leading idea is to measure the distance between the satellite and a well identifiable natural or artificial Persistent Scatterer (PS), taking in account the signal propagation delays through the troposphere and ionosphere and filtering out the known geophysical effects that induce periodic and secular ground displacements. The preliminary results here presented and discussed indicate that COSMO-SkyMed Himage imagery appear able to guarantee a displacements monitoring with an accuracy of few centimetres using only the amplitude data, provided few (at least one) stable PS's are available around the monitored area, in order to correct residual biases, likely due to orbit errors

Exploring the Potential of Conditional Adversarial Networks for Optical and SAR Image Matching

Tasks such as the monitoring of natural disasters or the detection of change highly benefit from complementary information about an area or a specific object of interest. The required information is provided by fusing high accurate co-registered and geo-referenced datasets. Aligned high resolution optical and synthetic aperture radar (SAR) data additionally enables an absolute geo-location accuracy improvement of the optical images by extracting accurate and reliable ground control points (GCPs) from the SAR images. In this paper we investigate the applicability of a deep learning based matching concept for the generation of precise and accurate GCPs from SAR satellite images by matching optical and SAR images. To this end, conditional generative adversarial networks (cGANs) are trained to generate SAR-like image patches from optical images. For training and testing, optical and SAR image patches are extracted from TerraSAR-X and PRISM image pairs covering greater urban areas spread over Europe. The artificially generated patches are then used to improve the conditions for three known matching approaches based on normalized cross-correlation (NCC), SIFT and BRISK, which are normally not usable for the matching of optical and SAR images. The results validate that a NCC, SIFT and BRISK based matching greatly benefit, in terms of matching accuracy and precision, from the use of the artificial templates. The comparison with two state-of-the-art optical and SAR matching approaches shows the potential of the proposed method but also revealed some challenges and the necessity for further developments

Satellite based synthetic aperture radar and optical spatial-temporal information as aid for operational and environmental mine monitoring

A sustainable society is a society that satisfies its resource requirements without endangering the sustainability of these resources. The mineral endowment on the African continent is estimated to be the first or second largest of world reserves. Therefore, it is recognised that the African continent still heavily depends on mineral exports as a key contributor to the gross domestic product (GDP) of various countries. These mining activities, however, do introduce primary and secondary environmental degradation factors. They attract communities to these mining areas, light and heavy industrial establishments occur, giving rise to artisanal activities. This study focussed on satellite RS products as an aid to a mine’s operations and the monitoring of its environment. Effective operational mine management and control ensures a more sustainable and profitable lifecycle for mines. Satellite based RS holds the potential to observe the mine and its surrounding areas at high temporal intervals, different spectral wavelengths and spatial resolutions. The combination of SAR and optical information creates a spatial platform to observe and measure the mine’s operations and the behaviour of specific land cover and land use classes over time and contributes to a better understanding of the mining activities and their influence on the environment within a specific geographical area. This study will introduce an integrated methodology to collect, process and analyse spatial information over a specific targeted mine. This methodology utilises a medium resolution land cover base map, derived from Landsat 8, to understand the predominant land cover types of the surrounding area. Using very high resolution mono- and stereoscopic satellite imagery provides a finer scale analysis and identifies changes in features at a smaller scale. Combining these technologies with the synthetic aperture radar (SAR) applications for precise measurement of surface subsidence or upliftment becomes a spatial toolbox for mine management. This study examines a combination of satellite remote sensing products guided by a systematic workflow methodology to integrate spatial results as an aid for mining operations and environmental monitoring. Some of the results that can be highlighted is the successful land cover classification using the Landsat 8 satellite. The land cover that dominated the Kolomela mine area was the “SHRUBLAND/GRASS” class with a 94% coverage and “MINE” class of 2.6%. Sishen mine had a similar dominated land cover characteristic with a “SHRUBLAND/GRASS” class of 90% and “MINE” class of 4.8%. The Pléiades time-series classification analysis was done using three scenes each acquired at a different time interval. The Sishen and Kolomela mine showed especially changes from the bare soil class to the asphalt or mine class. The Pléiades stereoscopic analysis provided volumetric change detection over small, medium, large and recessed areas. Both the Sishen and Kolomela mines demonstrated height profile changes in each selected category. The last category of results focused on the SAR technology to measure within millimetre accuracy the subsidence and upliftment behaviour of surface areas over time. The Royal Bafokeng Platinum tailings pond area was measured using 74 TerraSAR-X scenes. The tailings wall area was confirmed as stable with natural subsidence that occurred in its surrounding area due to seasonal changes of the soil during rainy and dry periods. The Chuquicamata mine as a large open pit copper mine area was analysed using 52 TerraSAR-X scenes. The analysis demonstrated significant vertical surface movement over some of the dumping sites. It is the wish of the researcher that this dissertation and future research scholars will continue to contribute in this scientific field. These contributions can only assist the mining sector to continuously improve its mining operations as well as its monitoring of the primary as well as the secondary environmental impacts to ensure improved sustainability for the next generation.Environmental SciencesM. Sc. (Environmental Science

A Study of Types of Sensors used in Remote Sensing

Of late, the science of Remote Sensing has been gaining a lot of interest and attention due to its wide variety of applications. Remotely sensed data can be used in various fields such as medicine, agriculture, engineering, weather forecasting, military tactics, disaster management etc. only to name a few. This article presents a study of the two categories of sensors namely optical and microwave which are used for remotely sensing the occurrence of disasters such as earthquakes, floods, landslides, avalanches, tropical cyclones and suspicious movements. The remotely sensed data acquired either through satellites or through ground based- synthetic aperture radar systems could be used to avert or mitigate a disaster or to perform a post-disaster analysis

In-depth verification of Sentinel-1 and TerraSAR-X geolocation accuracy using the Australian Corner Reflector Array

This article shows how the array of corner reflectors (CRs) in Queensland, Australia, together with highly accurate geodetic synthetic aperture radar (SAR) techniques—also called imaging geodesy—can be used to measure the absolute and relative geometric fidelity of SAR missions. We describe, in detail, the end-to-end methodology and apply it to TerraSAR-X Stripmap (SM) and ScanSAR (SC) data and to Sentinel-1interferometric wide swath (IW) data. Geometric distortions within images that are caused by commonly used SAR processor approximations are explained, and we show how to correct them during postprocessing. Our results, supported by the analysis of 140 images across the different SAR modes and using the 40 reflectors of the array, confirm our methodology and achieve the limits predicted by theory for both Sentinel-1 and TerraSAR-X. After our corrections, the Sentinel-1 residual errors are 6 cm in range and 26 cm in azimuth, including all error sources. The findings are confirmed by the mutual independent processing carried out at University of Zurich (UZH) and German Aerospace Center (DLR). This represents an improve�ment of the geolocation accuracy by approximately a factor of four in range and a factor of two in azimuth compared with the standard Sentinel-1 products. The TerraSAR-X results are even better. The achieved geolocation accuracy now approaches that of the global navigation satellite system (GNSS)-based survey of the CRs positions, which highlights the potential of the end-to-end SAR methodology for imaging geodesy

Mitigation of atmospheric perturbations and solid Earth movements in a TerraSAR-X time-series

The TerraSAR-X (TSX) synthetic aperture radar (SAR) marks the recent emergence of a new generation of spaceborne radar sensors that can for the first time lay claim to localization accuracies in the sub-meter range. The TSX platform’s extremely high orbital stability and the sensor’s hardware timing accuracy combine to enable direct measurements of atmospheric refraction and solid Earth movements. By modeling these effects for individual TSX acquisitions, absolute pixel geolocation accuracy on the order of several centimeters can be achieved without need for even a single tiepoint. A 16-month time series of images was obtained over a fixed test site, making it possible to validate both an atmospheric refraction and a solid Earth tide model, while at the same time establishing the instrument’s long-term stability. These related goals were achieved by placing trihedral corner reflectors (CRs) at the test site and estimating their phase centers with centimeter-level accuracy using differential GPS (DGPS). Oriented in pairs toward a given satellite track, the CRs could be seen as bright “points” in the images, providing a geometric reference set. SAR images from the high-resolution spotlight (HS) mode were obtained in alternating ascending and descending orbit configurations. The highest-resolution products were selected for their small sample dimensions, as positions can be more precisely determined. Based on the delivered product annotations, the CR image positions were predicted, and these predictions were compared with their measured image positions both before and after compensation for atmospheric refraction and systematic solid Earth deviations. It was possible to show that when the atmospheric distortion and Earth tides are taken into account, the TSX HS products have geolocation accuracies far exceeding the specified requirements. Furthermore, this accuracy was maintained for the duration of the 16-month test period. It could be demonstrated that with a correctly calibrated sensor, and after accounting for atmospheric and tidal effects, tiepoint-free geolocation is possible with TSX with an absolute product accuracy of about 5 cm