Range-Point Migration-Based Image Expansion Method Exploiting Fully Polarimetric Data for UWB Short-Range Radar

Ultrawideband radar with high-range resolution is a promising technology for use in short-range 3-D imaging applications, in which optical cameras are not applicable. One of the most efficient 3-D imaging methods is the range-point migration (RPM) method, which has a definite advantage for the synthetic aperture radar approach in terms of computational burden, high accuracy, and high spatial resolution. However, if an insufficient aperture size or angle is provided, these kinds of methods cannot reconstruct the whole target structure due to the absence of reflection signals from large part of target surface. To expand the 3-D image obtained by RPM, this paper proposes an image expansion method by incorporating the RPM feature and fully polarimetric data-based machine learning approach. Following ellipsoid-based scattering analysis and learning with a neural network, this method expresses the target image as an aggregation of parts of ellipsoids, which significantly expands the original image by the RPM method without sacrificing the reconstruction accuracy. The results of numerical simulation based on 3-D finite-difference time-domain analysis verify the effectiveness of our proposed method, in terms of image-expansion criteria

Synthetic aperture radar imagery of airports and surrounding areas: Study of clutter at grazing angles and their polarimetric properties

The statistical description of ground clutter at an airport and in the surrounding area is addressed. These data are being utilized in a program to detect microbursts. Synthetic aperture radar data were collected at the Denver Stapleton Airport. Mountain terrain data were examined to determine if they may potentially contribute to range ambiguity problems and degrade microburst detection. Results suggest that mountain clutter may not present a special problem source. The examination of clutter at small grazing angles was continued by examining data collected at especially low altitudes. Cultural objects such as buildings produce strong sources of backscatter at angles of about 85 deg, with responses of 30 dB to 60 dB above the background. Otherwise there are a few sources which produce significant scatter. The polarization properties of hydrospheres and clutter were examined with the intent of determining the optimum polarization. This polarization was determined to be dependent upon the ratio of VV and HH polarizations of both rain and ground clutter

Polarimetric Synthetic Aperture Radar (SAR) Application for Geological Mapping and Resource Exploration in the Canadian Arctic

The role of remote sensing in geological mapping has been rapidly growing by providing predictive maps in advance of field surveys. Remote predictive maps with broad spatial coverage have been produced for northern Canada and the Canadian Arctic which are typically very difficult to access. Multi and hyperspectral airborne and spaceborne sensors are widely used for geological mapping as spectral characteristics are able to constrain the minerals and rocks that are present in a target region. Rock surfaces in the Canadian Arctic are altered by extensive glacial activity and freeze-thaw weathering, and form different surface roughnesses depending on rock type. Different physical surface properties, such as surface roughness and soil moisture, can be revealed by distinct radar backscattering signatures at different polarizations. This thesis aims to provide a multidisciplinary approach for remote predictive mapping that integrates the lithological and physical surface properties of target rocks. This work investigates the physical surface properties of geological units in the Tunnunik and Haughton impact structures in the Canadian Arctic characterized by polarimetric synthetic aperture radar (SAR). It relates the radar scattering mechanisms of target surfaces to their lithological compositions from multispectral analysis for remote predictive geological mapping in the Canadian Arctic. This work quantitatively estimates the surface roughness relative to the transmitted radar wavelength and volumetric soil moisture by radar scattering model inversion. The SAR polarization signatures of different geological units were also characterized, which showed a significant correlation with their surface roughness. This work presents a modified radar scattering model for weathered rock surfaces. More broadly, it presents an integrative remote predictive mapping algorithm by combining multispectral and polarimetric SAR parameters

Polarization Optimization for the Detection of Multiple Persistent Scatterers Using SAR Tomography

The detection of multiple interfering persistent scatterers (PSs) using Synthetic Aperture Radar (SAR) tomography is an efficient tool for generating point clouds of urban areas. In this context, detection methods based upon the polarization information of SAR data are effective at increasing the number of PSs and producing high-density point clouds. This paper presents a comparative study on the effects of the polarization design of a radar antenna on further improving the probability of detecting persistent scatterers. For this purpose, we introduce an extension of the existing scattering property-based generalized likelihood ratio test (GLRT) with realistic dependence on the transmitted/received polarizations. The test is based upon polarization basis optimization by synthesizing all possible polarimetric responses of a given scatterer from its measurements on a linear orthonormal basis. Experiments on both simulated and real data show, by means of objective metrics (probability of detection, false alarm rate, and signal-to-noise ratio), that polarization waveform optimization can provide a significant performance gain in the detection of multiple scatterers compared to the existing full-polarization-based detection method. In particular, the increased density of detected PSs at the studied test sites demonstrates the main contribution of the proposed method

Polarization techniques for mitigation of low grazing angle sea clutter

Maritime surveillance radars are critical in commerce, transportation, navigation, and defense. However, the sea environment is perhaps the most challenging of natural radar backdrops because maritime radars must contend with electromagnetic backscatter from the sea surface, or sea clutter. Sea clutter poses unique challenges in very low grazing angle geometries, where typical statistical assumptions regarding sea clutter backscatter do not hold. As a result, traditional constant false alarm rate (CFAR) detection schemes may yield a large number of false alarms while objects of interest may be challenging to detect. Solutions posed in the literature to date have been either computationally impractical or lacked robustness. This dissertation explores whether fully polarimetric radar offers a means of enhancing detection performance in low grazing angle sea clutter. To this end, MIT Lincoln Laboratory funded an experimental data collection using a fully polarimetric X-band radar assembled largely from commercial off-the-shelf components. The Point de Chene Dataset, collected on the Atlantic coast of Massachusetts’ Cape Ann in October 2015, comprises multiple sea states, bandwidths, and various objects of opportunity. The dataset also comprises three different polarimetric transmit schemes. In addition to discussing the radar, the dataset, and associated post-processing, this dissertation presents a derivation showing that an established multiple input, multiple output radar technique provides a novel means of simultaneous polarimetric scattering matrix measurement. A novel scheme for polarimetric radar calibration using a single active calibration target is also presented. Subsequent research leveraged this dataset to develop Polarimetric Co-location Layering (PCL), a practical algorithm for mitigation of low grazing angle sea clutter, which is the most significant contribution of this dissertation. PCL routinely achieves a significant reduction in the standard CFAR false alarm rate while maintaining detections on objects of interest. Moreover, PCL is elegant: It exploits fundamental characteristics of both sea clutter and object returns to determine which CFAR detections are due to sea clutter. We demonstrate that PCL is robust across a range of bandwidths, pulse repetition frequencies, and object types. Finally, we show that PCL integrates in parallel into the standard radar signal processing chain without incurring a computational time penalty

Automatic vessel monitoring with single and multidimensional SAR images in the wavelet domain

Spaceborne Synthetic Aperture Radar (SAR) techniques constitute an extremely promising alternative compared to traditional surveillance methods thanks to the all-weather and day-and-night capabilities of Radar linked with the large coverage of SAR images. Nowadays, the capabilities of satellite based SAR systems are confirmed by a wide amount of applications and experiments all over the world. Nevertheless, specific data exploitation methods are still to be developed to provide an efficient automatic interpretation of SAR data. The aim of this paper is to present an approach based on multiscale time–frequency analysis for the automatic detection of spots in a noisy background which is a critical matter in a number of SAR applications. The technique has been applied to automatic ship detection in single and multidimensional SAR imagery and it has proven to be a rapid, robust and reliable tool, able to manage complicated heterogeneous scenes where classical approaches may fail.Peer Reviewe

Synthetic aperture radar (SAR)-based mapping of volcanic flows: Manam Island, Papua New Guinea

We present new radar-based techniques for efficient identification of surface changes generated by lava and pyroclastic flows, and apply these to the 1996 eruption of Manam Volcano, Papua New Guinea. Polarimetric L- and P-band airborne synthetic aperture radar (SAR) data, along with a C-band DEM, were acquired over the volcano on 17 November 1996 during a major eruption sequence. The L-band data are analyzed for dominant scattering mechanisms on a per pixel basis using radar target decomposition techniques. A classification method is presented, and when applied to the L-band polarimetry, it readily distinguishes bare surfaces from forest cover over Manam volcano. In particular, the classification scheme identifies a post-1992 lava flow in NE Valley of Manam Island as a mainly bare surface and the underlying 1992 flow units as mainly vegetated surfaces. The Smithsonian's Global Volcanism Network reports allow us to speculate whether the bare surface is a flow dating from October or November in the early part of the late-1996 eruption sequence. This work shows that fully polarimetric SAR is sensitive to scattering mechanism changes caused by volcanic resurfacing processes such as lava and pyroclastic flows. By extension, this technique should also prove useful in mapping debris flows, ash deposits and volcanic landslides associated with major eruptions