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

Synthetic Aperture Radar Tool and Libraries: A Framework for Geo-Referenced Data Processing and Algorithm Prototyping

Creating a system for Synthetic Aperture Radar (SAR) image formation can be a huge undertaking as it requires knowledge of several disparate domains. Researchers may be prevented from applying interesting techniques in a particular domain due to hurdles in working with those areas outside their area of interest. This paper presents the SyntheTic Aperture Radar Tool and Libraries (STARTAL) framework for SAR processing that simplifies adding new data formats and prototyping algorithms. STARTAL provides a user interface for viewing the full data region on ground geometry, selecting sub-regions to process, and viewing processed results. Many common, difficult tasks are provided as libraries for general use. To validate the STARTAL framework, this paper also shows imagery which has been processed with algorithms developed at Utah State University (USU) which are derived from a model-based expression of the relationship between collected SAR data and ground geometry

An Efficient Solution to the Factorized Geometrical Autofocus Problem

This paper describes a new search strategy within the scope of factorized geometrical autofocus (FGA) and synthetic-aperture-radar processing. The FGA algorithm is a fast factorized back-projection formulation with six adjustable geometry parameters. By tuning the flight track step by step and maximizing focus quality by means of an object function, a sharp image is formed. We propose an efficient two-stage approach for the geometrical variation. The first stage is a low-order (few parameters) parallel search procedure involving small image areas. The second stage then combines the local hypotheses into one global autofocus solution, without the use of images. This method has been applied successfully on ultrawideband CARABAS II data. Errors due to a constant acceleration are superposed on the measured track prior to processing, giving a 6-D autofocus problem. Image results, including resolution, peak-to-sidelobe ratio and magnitude values for point-like targets, finally confirm the validity of the strategy. The results also verify the prediction that there are several satisfying autofocus solutions for the same radar data

Interferometric synthetic aperture sonar system supported by satellite

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Landslide mapping and monitoring by using radar and optical remote sensing: examples from the EC-FP7 project SAFER

This paper focuses on the Landslide Thematic services of the EU-funded FP7-SPACE project SAFER (Services and Applications For Emergency Response) for inventory mapping, monitoring and rapid mapping by using Earth Observation (EO). We exploited satellite Interferometric Synthetic Aperture Radar (InSAR) and Object-Based Image Analysis (OBIA), and discuss example applications in South Tyrol and Abruzzo (Italy), Lower Austria (Austria), Lubietova (Slovakia) and the Kaohsiung County (Taiwan). These case studies showcase the significance of radar and optical EO data, InSAR and OBIA methods for landslide mapping and monitoring in different geological environments and during all phases of emergency management: mitigation, preparedness, crisis and recovery

SAR Images Refocusing and Scattering Center Detection for Infrastructure Monitoring

International audienceInfrastructure monitoring applications can require the tracking of slowly moving points of a certain structure. Given a certain point from a structure to be monitored, in the context of available SAR products where the image is already focused in a slant range - azimuth grid, it is not obvious if this point is the scattering center, if it is in layover or if it is visible from the respective orbit. This paper proposes a refocusing procedure of SAR images on a set of measured points among with a 4D tomography based scattering center detection. The refocusing procedure consists of an azimuth de-focusing followed by a modified back-projection on the given set of points. The presence of a scattering center at the given positions is detected by computing the local elevation-velocity plane for each point and testing if the main response is at zero elevation. The refocusing and scattering center detection algorithm is validated on real data acquired with the TerraSAR-X satellite during March-June 2012. The mean displacement velocities of the detected scatterers show good agreement with the in-situ measurements

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

Land Cover Classification using Sentinel-1 Radar Mission Interferometry

Synthetic Aperture Radar (SAR) has been widely used for many years in the field of remote sensing. SAR has valuable contribution due to its ability to provide complementary information to optical systems, penetration of radar waves through volumetric targets and high-resolution. SAR has the ability to operate during day and night. It provides operational services under all weather conditions. SAR imagery has many applications including land cover changes, environmental monitoring, climate change and military surveillance. This work focuses on land cover classification with SAR interferometry (InSAR) technique using Sentinel-1 space radar image pair. Sentinel-1 data were collected over the southern part of Estonia. Two SLC SAR images were acquired from both Sentinel-1A and Sentinel-1B with six days temporal difference. In this study, interferometric coherence and backscattering intensity processing chains have been set up and applied to Sentinel-1 SAR image pair. The Sentinel Application Platform (SNAP) has been used for processing of single pair for Sentinel-1 mission. The SNAP is an European Space Agency (ESA) software. The Sentinel-1 image pair processing has been done using Sentinel-1 Toolbox (S1TBX) which is a part of SNAP. Corine Land Cover (CLC) 2012 database has been used as a reference data with 20 m resolution. The CLC2012 contains land use/cover information for most of the European countries. A single optical image from Sentinel-2A was additionally used for feature extraction. An overall accuracy of 68% to 73% was achieved when performing classification into five classes (Urban, Field, Forest, Peat-land, Water) using supervised classification with k-nearest neighbour (kNN) algorithm. The accuracy assessment was done by using confusion matrices