Resolution Synthetic Aperture Radar Imagery Research Articles

Supervised wetland classification using high spatial resolution optical, SAR, and LiDAR imagery

Wetlands are among the most valuable natural resources, being highly beneficial to both the environment and humans. Therefore, it is very important to map and monitor wetlands. Although various remote sensing datasets, including optical, synthetic aperture radar (SAR), light detection and ranging (LiDAR) imagery, have been widely applied to classify wetlands, it is still required to discuss the advantages/limitations of each of these datasets and suggest the best remote sensing methodology for wetland mapping. Thus, the Terra Nova National Park, located in Newfoundland, Canada, was initially selected as the study area to develop a supervised classification method along with object-based image analysis. To this end, different remote sensing-based scenarios were investigated using individual optical, SAR, and LiDAR datasets, as well as their various combinations. In addition, for achieving the highest accuracy, the effects of segmentation scales and the tuning parameters of the random forest (RF) classifier were examined. The results showed that a combination of optical, SAR, and LiDAR images with the segmentation scale of 150, the RF depth of 20, and the RF minimum sample number of 5 provided the highest classification accuracy with the overall accuracy of 87.2%. Moreover, based on the results, approximately 21% and 79% of the study area are covered by wetlands and nonwetlands, respectively. The proposed methodology shows an optimum scenario for future wetland classification tasks and can assist stakeholders in the effective management of wetlands and establishment of necessary policies.

Mixture Statistical Distribution Based Multiple Component Model for Target Detection in High Resolution SAR Imagery

This paper proposes an innovative Mixture Statistical Distribution Based Multiple Component (MSDMC) model for target detection in high spatial resolution Synthetic Aperture Radar (SAR) images. Traditional detection algorithms usually ignore the spatial relationship among the target’s components. In the presented method, however, both the structural information and the statistical distribution are considered to better recognize the target. Firstly, the method based on compressed sensing reconstruction is used to recover the SAR image. Then, the multiple component model composed of a root filter and some corresponding part filters is applied to describe the structural information of the target. In the following step, mixture statistical distributions are utilised to discriminate the target from the background, and the Method of Logarithmic Cumulants (MoLC) based Expectation Maximization (EM) approach is adopted to estimate the parameters of the mixture statistical distribution model, which will be finally merged into the proposed MSDMC framework together with the multiple component model. In the experiment, the aeroplanes and the electrical power towers in TerraSAR-X SAR images are detected at three spatial resolutions. The results indicate that the presented MSDMC Model has potential for improving the detection performance compared with the state-of-the-art SAR target detection methods.

Landslide Mapping in Vegetated Areas Using Change Detection Based on Optical and Polarimetric SAR Data

Mapping of landslides, quickly providing information about the extent of the affected area and type and grade of damage, is crucial to enable fast crisis response, i.e., to support rescue and humanitarian operations. Most synthetic aperture radar (SAR) data-based landslide detection approaches reported in the literature use change detection techniques, requiring very high resolution (VHR) SAR imagery acquired shortly before the landslide event, which is commonly not available. Modern VHR SAR missions, e.g., Radarsat-2, TerraSAR-X, or COSMO-SkyMed, do not systematically cover the entire world, due to limitations in onboard disk space and downlink transmission rates. Here, we present a fast and transferable procedure for mapping of landslides, based on change detection between pre-event optical imagery and the polarimetric entropy derived from post-event VHR polarimetric SAR data. Pre-event information is derived from high resolution optical imagery of Landsat-8 or Sentinel-2, which are freely available and systematically acquired over the entire Earth’s landmass. The landslide mapping is refined by slope information from a digital elevation model generated from bi-static TanDEM-X imagery. The methodology was successfully applied to two landslide events of different characteristics: A rotational slide near Charleston, West Virginia, USA and a mining waste earthflow near Bolshaya Talda, Russia.

A Method for Automatic Road Extraction of High Resolution SAR Imagery

Nowadays automatic road extraction from satellite imageries is considered as one of the most important research trends in the field of remote sensing. This paper presents a method for automatic extraction of road centerlines from syn- thetic aperture radar (SAR) imagery. During the first step, three features, namely the direction of the least total radiance, the corresponding radiance, and the contrast are extracted to definethe road characteristics bythe backscatter coefficient of each pixel and its neighboring pixels from the SAR imagery. The fusion of the extracted features is carried out in the next step for detection of the road areas by using a fuzzy inference system. Afterwards, the morphology skeletonization is ap- plied on the roadareas to extract the roadskeleton. Then some interested seed points are extracted so that they could be used in a snake model, which is employed to connect the seed points in order to form up the road centerlines. The proposed algorithm is tested on different parts of TerraSAR-X images. The experimental results reveal that the proposed method is effective in terms of correctness, completeness, and quality.

HIGH RESOLUTION AIRBORNE SAR IMAGE CHANGE DETECTION IN URBAN AREAS WITH SLIGHTLY DIFFERENT ACQUISITION GEOMETRIES

Abstract. Before applying change detection, high resolution SAR (Synthetic Aperture Radar) imagery benefits from advanced denoising mechanisms to preserve details and minimize speckle. We propose a change detector based on a MCA (Morphological Components Analysis) of a difference image (DI). With MCA, the data is decomposed into image features utilizing sparse representations of the image content. By introducing a priori knowledge of the content of the scenes, and exploiting shape information corresponding to the changes provided by MCA, we can significantly improve performance under adverse conditions, such as inconsistent acquisition geometries.

Centimeter range measurement using amplitude data of TerraSAR-X imagery

Abstract. The SAR (Synthetic Aperture Radar) imagery are largely used for the environmental, structures and infrastructures monitoring. In particular, Differential SAR Interferometry (DInSAR) is a well known technique that allows producing spatially dense displacement maps with centimetre to millimetre accuracy. The SAR signal is characterized by phase and amplitude value and the DInSAR remote sensing technique allows to analyse deformation phenomena affecting both extended natural areas and localized man-made structures, by exploiting the phase difference of SAR image pairs. New SAR satellite sensors such as COSMO-SkyMed, TerraSAR-X and PAZ offer the capability to achieve positioning in a global reference frame accuracies in the meter range and even better, thanks to the higher image resolution (up to 0.20 m pixel resolution in the Staring SpotLight mode for TerraSAR-X and PAZ) and to the use of on board dual frequency GPS receivers, which allows to determine the satellite orbit with an accuracy at few centimetres level. The goal of this work is to exploit the slant-range measurements reaching centimetre accuracies using only the amplitude information of SAR images acquired by TerraSAR-X satellite sensor. The leading idea is to evaluate the positioning accuracy of well identifiable and stable natural and man-made Persistent Scatterers (PS’s) along the SAR line of sight. The preliminary results, obtained on the Berlin area (Germany), shown that it is possible achieve a slant-range positioning accuracy with a bias well below 10 cm and a standard deviation of about 3 cm; the results are encouraging for applications of high resolution SAR imagery amplitude data in land and infrastructures monitoring.

Hydraulic modelling calibration in small rivers by using coarse resolution synthetic aperture radar imagery

AbstractThis study investigates the potential of the low‐resolution images (150 m) derived by the Advanced Synthetic Aperture Radar onboard the ENVISAT satellite to calibrate a hydraulic model coupled with a semidistributed hydrologic model applied to a subcatchment of the upper‐middle Tiber River, Genna basin (<100 km2), in central Italy, wherein a flood event occurred on 28 November 2010 causing high damages. Specifically for this flood event, an Advanced Synthetic Aperture Radar image is available for the peak region of the discharge hydrograph. Different image processing techniques are used for the assessment of the flooded areas by using both a single image and a change detection image based on an additional (dry) reference image. The satellite‐derived flooded areas are adopted for the calibration of the channel and floodplain Manning's roughness coefficients of a ‘quasi‐two‐dimensional’ hydraulic model applied to the study basin, which allows to distinguish the main channel from the floodplain. The inflows for the hydraulic model corresponding to each tributary of the main channel are determined by a semidistributed hydrologic model calibrated by using a set of historical flood events.The hydraulic model calibration is also carried out by using the same set of historical flood events. For the event of November 2010, the modelled discharge and water level hydrographs obtained with the two sets of Manning roughness (via satellite and historical calibration) are compared with in situ observations at the outlet of the basin.This application on a small catchment contributes to strengthen the value of remote sensing data confirming the potential of synthetic aperture radar imagery to reduce uncertainties in hydraulic modelling calibration. Copyright © 2012 John Wiley & Sons, Ltd.

Timely Low Resolution SAR Imagery To Support Floodplain Modelling: a Case Study Review

It is widely recognised that remote sensing can support flood monitoring, modelling and management. In particular, satellites carrying Synthetic Aperture Radar (SAR) sensors are valuable as radar wavelengths can penetrate cloud cover and are insensitive to daylight. However, given the strong inverse relationship between spatial resolution and revisit time, monitoring floods from space in near real time is currently only possible through low resolution (about 100 m pixel size) SAR imagery. For instance, ENVISAT-ASAR (Advanced Synthetic Aperture Radar) in WSM (wide swath mode) revisit times are of the order of 3 days and the data can be obtained within 24 h at no (or low) cost. Hence, this type of space-borne data can be used for monitoring major floods on medium-to-large rivers. This paper aims to discuss the potential for, and uncertainties of, coarse resolution SAR imagery to monitor floods and support hydraulic modelling. The paper first describes the potential of globally and freely available space-borne data to support flood inundation modelling in near real time. Then, the uncertainty of SAR-derived flood extent maps is discussed and the need to move from deterministic binary maps (wet/dry) of flood extent to uncertain flood inundation maps is highlighted.

Modified difference change detector for small targets in SAR imagery

One of the most straightforward techniques for detecting changes in an image involves forming the difference between a test image and a reference image. Unfortunately, such a technique can give rise to a large number of false alarms due to the statistical variability of the underlying pixel values, as has been well established within the radar community over the years. One method for dealing with this large number of false alarms involves forming the ratio, rather than the difference, of two synthetic aperture radar (SAR) images. We introduce a modified version of the standard differencing technique to overcome problems associated with pixel value variability. The new (modified) differencing approach utilizes assumptions about the statistics of the image background and the object being sought (target) to reduce the number of false alarms due to highly variable background (clutter) regions, and it includes the standard ratio test as a special case. In fact, we find that the modified difference approach can also be viewed as a modified version of the ratio test with a threshold that varies as a function of the background clutter radar cross section (RCS). We also present an abridged, albeit suboptimal, version of this approach that eliminates assumptions regarding the target's probability distribution, and we analyze both of the approaches. We then compare these results with those obtained with a standard ratio test, and illustrate how the modified difference test reduces to the ratio test under certain operating conditions. The abridged version of the modified approach is applied to high resolution synthetic aperture radar imagery and compared with results obtained with the classical differencing technique, and following this, the modified difference technique is compared with the standard ratio test. Results suggest that under appropriate conditions the abridged, modified technique can successfully detect changes without the need for any image segmentation.

Comparison of Selected Features for Target Detection in Synthetic Aperture Radar Imagery

Cooke, Tristrom, Redding, Nicholas J., Schroeder, Jim, and Zhang, Jingxin, Comparison of Selected Features for Target Detection in Synthetic Aperture Radar Imagery, Digital Signal Processing10 (2000), 286–296.Several methods are available that capture the statistics of radar imagery. The best features, in the sense of man-made target discrimination, are expected to be different for different types of natural background and for different objects of interest such as vehicles. We demonstrate that discrimination of natural background and man-made objects using low resolution synthetic aperture radar imagery is possible using singular value decomposition; several other simple features are also used to augment the feature vector. We use a subset of eigenvectors as features for target discrimination. The optimal set of features used to classify a region as “background clutter only” or “target region” is automatically chosen by a standard suboptimal feature selection algorithm.