Synthetic aperture radar/LANDSAT MSS image registration

Algorithms and procedures necessary to merge aircraft synthetic aperture radar (SAR) and LANDSAT multispectral scanner (MSS) imagery were determined. The design of a SAR/LANDSAT data merging system was developed. Aircraft SAR images were registered to the corresponding LANDSAT MSS scenes and were the subject of experimental investigations. Results indicate that the registration of SAR imagery with LANDSAT MSS imagery is feasible from a technical viewpoint, and useful from an information-content viewpoint

Upgrade of foss date plug-in: Implementation of a new radargrammetric DSM generation capability

Synthetic Aperture Radar (SAR) satellite systems may give important contribution in terms of Digital Surface Models (DSMs) generation considering their complete independence from logistic constraints on the ground and weather conditions. In recent years, the new availability of very high resolution SAR data (up to 20 cm Ground Sample Distance) gave a new impulse to radargrammetry and allowed new applications and developments. Besides, to date, among the software aimed to radargrammetric applications only few show as free and open source. It is in this context that it has been decided to widen DATE (Digital Automatic Terrain Extractor) plug-in capabilities and additionally include the possibility to use SAR imagery for DSM stereo reconstruction (i.e. radargrammetry), besides to the optical workflow already developed. DATE is a Free and Open Source Software (FOSS) developed at the Geodesy and Geomatics Division, University of Rome "La Sapienza", and conceived as an OSSIM (Open Source Software Image Map) plug-in. It has been developed starting from May 2014 in the framework of 2014 Google Summer of Code, having as early purpose a fully automatic DSMs generation from high resolution optical satellite imagery acquired by the most common sensors. Here, the results achieved through this new capability applied to two stacks (one ascending and one descending) of three TerraSAR-X images each, acquired over Trento (Northern Italy) testfield, are presented. Global accuracies achieved are around 6 metres. These first results are promising and further analysis are expected for a more complete assessment of DATE application to SAR imagery

PALSAR wide-area mapping and annual monitoring methodology for Borneo

This paper describes the operational radar mapping processing chain developed and steps taken to produce a provisional wide-area PALSAR forest and land cover map of Borneo for the year 2007, compliant with emerging international standards (CEOS guidelines, FAO LCCS). The methodology is based on the classification of FBS and FBD image pairs. To cover Borneo the equivalent of 554 standard images is required. The final overall accuracy assessment result shows this demonstration map product is in 85.5% full agreement with the independent reference dataset and in 7.8% ‘partial agreement’. Monitoring land cover change on an annual basis requires consistent year-to-year mapping. This implies that the localised and temporal effects of environmental factors on the backscatter level (such as inundation or El Niño drought) and variation due to differing observation dates/cycles (related to change of season) have to be accounted for strip by strip. New concepts for (a) automated intercalibration of radar data, (b) time-consistency and (c) automated adaptation of radar signatures to changing environmental conditions have been evaluated for its usefulness to improve the classification and the consistency of annual monitoring

Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision

A high precision geometric method for automated shoreline detection from Landsat TM and ETM+ imagery is presented. The methodology is based on the application of an algorithm that ensures accurate image geometric registration and the use of a new algorithm for sub-pixel shoreline extraction, both at the sub-pixel level. The analysis of the initial errors shows the influence that differences in reflectance of land cover types have over shoreline detection, allowing us to create a model to substantially reduce these errors. Three correction models were defined according to the type of gain used in the acquisition of the original Landsat images. Error assessment tests were applied on three artificially stabilised coastal segments that have a constant and well-defined land-water boundary. A testing set of 45 images (28 TM, 10 ETM high-gain and 7 ETM low-gain) was used. The mean error obtained in shoreline location ranges from 1.22 to 1.63. m, and the RMSE from 4.69 to 5.47. m. Since the errors follow a normal distribution, then the maximum error at a given probability can be estimated. The results confirm that the use of Landsat imagery for detection of instantaneous coastlines yields accuracy comparable to high-resolution techniques, showing the potential of Landsat TM and ETM images in those applications where the instantaneous lines are a good geomorphological descriptor. © 2012 Elsevier Inc.The authors appreciate the financial support provided by the Spanish Ministerio de Ciencia e Innovacion and the Spanish Plan E in the framework of the Projects CGL2009-14220-C02-01 and CGL2010-19591.Pardo Pascual, JE.; Almonacid Caballer, J.; Ruiz Fernández, LÁ.; Palomar-Vázquez, J. (2012). Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision. Remote Sensing of Environment. 123:1-11. doi:10.1016/j.rse.2012.02.024S11112

Extraction of spatial information from sterioscopic SAR images

Synthetic Aperture Radar (SAR) is now widely used for generating Digital Elevation Models (DEMs) and has advantages over optical data in terms of availability as it allows all-day and all-weather operations. The stereoscopic SAR method, which allows direct extraction of spatial information in three-dimensional space, has been established for decades. However, the traditional stereoscopic methods developed for SAR data depend on many human operations and need ground control points (GCPs), to set up geometric models. The aims of the thesis are not only to propose a refined rigorous stereoscopic SAR method and a new error model to predict theoretic errors, but also to achieve a higher level of automation and accuracy. By using a weighting matrix, which is derived by considering different observations in the space intersection algorithm, the minimal number of the GCPs required for the refined algorithm is only two. To achieve a high degree of automation, an optimized strategy of parameter selection for the pyramidal image correlation scheme employing a region-growing technique has been proposed. This avoids a trial-and-error approach to produce digital parallax data from the same-side SAR image pairs. A new method to derive GCPs automatically has been developed using a SAR image simulation technique, under the condition that a known DEM chip is available, to minimize human interventions and operator error. The proposed method for providing GCPs and the DEMs generated from space intersection have been incorporated into the procedures for geocoding SAR images to validate the proposed algorithms. The results derived show that the stereoscopic SAR data can be applied to geometric rectification in flat-to-moderate areas, and other applications of extraction of spatial information are promising

First Analyses of Sentinel-1 Images for Maritime Surveillance

Sentinel-1 is the European Synthetic Aperture Radar (SAR) satellite operational since 3 October 2014. The SAR’s characteristics should make it suitable for maritime surveillance (ship detection), and it will routinely collect a large amount of maritime imagery over European and global seas. After its launch in April 2014, preliminary data have been made available to limited users in the satellite’s commissioning phase, and since the start of the operational phase data are available to the general public. These early data have been used to assess the quality of Sentinel-1 images and their suitability for ship detection. This was partly done by using the JRC’s ship detection software SUMO, after adaptation to ingest and process Sentinel-1 data. It is found that the sensor lives up to its specifications, thereby making it very useful for maritime surveillance thanks to its combination of wide swath and low noise at the medium resolution with which it will mostly be operated (“IW” and “EW” modes).JRC.G.3-Maritime affair

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