Christopher R. Jackson

Polarimetry and its use in automatic target detection with examples from Search and Rescue

The problem of automatically locating targets in synthetic aperture radar (SAR) imagery has traditionally been done in the image or intensity domain. A fully polarimetric SAR provides additional information that can be used in connection with SAR processing for speckle reduction, without any degradation of SAR resolution, and in automatic target detection for classifying the source of a particular scattering signature in an image. The polarimetric information can also aid in locating targets which lack a strong intensity return. This paper presents enhanced imagery and automatic target detection results using data collected under the NASA Search and Rescue Mission Office at Goddard Space Flight Center (GSFC) by the NASA/JPL AirSAR radar.

Synthetic aperture radar processing system for search and rescue

Synthetic aperture radar (SAR) is uniquely suited to help solve the search and rescue problem since it can be utilized either day or night and through both dense fog or thick cloud cover. This paper describes the search and rescue data processing system (SARDPS) developed at Goddard Space Flight Center. SARDPS was developed for the Search and Rescue Mission Office in order to conduct research, development, and technology demonstration of SAR to quickly locate small aircraft which have crashed in remote areas. In order to effectively apply SAR to the detection of crashed aircraft several technical challenges needed to be overcome. These include full resolution SAR image formation using low frequency radar appropriate for foliage penetration, the application of autofocusing for SAR motion compensation in the processing system, and the development of sophisticated candidate crash site detection algorithms. In addition, the need to dispatch rescue teams to specific locations requires precise SAR image georectification and map registration techniques. The final end-to-end processing system allows for raw SAR phase history data to be quickly converted to georeferenced map/image products with candidate crash site locations identified.

Real-time atmospheric imaging and processing with hybrid adaptive optics and hardware accelerated lucky-region fusion (LRF) algorithm

Atmospheric turbulences can significantly deteriorate the performance of long-range conventional imaging systems and create difficulties for target identification and recognition. Our in-house developed adaptive optics (AO) system, which contains high-performance deformable mirrors (DMs) and the fast stochastic parallel gradient decent (SPGD) control mechanism, allows effective compensation of such turbulence-induced wavefront aberrations and result in significant improvement on the image quality. In addition, we developed advanced digital synthetic imaging and processing technique, “lucky-region” fusion (LRF), to mitigate the image degradation over large field-of-view (FOV). The LRF algorithm extracts sharp regions from each image obtained from a series of short exposure frames and fuses them into a final improved image. We further implemented such algorithm into a VIRTEX-7 field programmable gate array (FPGA) and achieved real-time video processing. Experiments were performed by combining both AO and hardware implemented LRF processing technique over a near-horizontal 2.3km atmospheric propagation path. Our approach can also generate a universal real-time imaging and processing system with a general camera link input, a user controller interface, and a DVI video output.

Detection of aircraft crash sites from space using fully polarimetric SIR-C SAR imagery for search and rescue applications

The Beaconless Search & Rescue Program at NASA Goddard Space Flight Center (GSFC) has been working to solve the technological challenges associated with detecting small aircraft crash sites using synthetic aperture radar (SAR) imagery. One area of work has focused on the use of fully polarimetric imagery to both improve image quality and distinguish the crash sites from the natural background. Data from aircraft based SARs have been used for development but since a SAR satellite deployment is one possible option for a practical Search and Rescue system, the work is being extended to satellite SAR imagery. This paper presents the results of processing Shuttle Imaging Radar-C (SIR-C) data collected over an aircraft crash site near Wadesboro, North Carolina through the target detection software developed at GSFC. The results demonstrate the ability to achieve crash site detection using SAR data collected from Earth orbit.

Hardware acceleration of lucky-region fusion (LRF) algorithm for imaging

“Lucky-region” fusion (LRF) is a synthetic imaging technique that has proven successful in enhancing the quality of images distorted by atmospheric turbulence. The LRF algorithm extracts sharp regions of an image obtained from a series of short exposure frames, and fuses the sharp regions into a final, improved image. In our previous research, the LRF algorithm had been implemented on a PC using the C programming language. However, the PC did not have sufficient processing power to handle real-time extraction, processing and reduction required when the LRF algorithm was applied to real-time video from fast, high-resolution image sensors rather than single picture images. This document describes a hardware implementation of the LRF algorithm on a VIRTEX-7 field programmable gate array (FPGA) to achieve real-time image processing. The novelty in our approach is the creation of a “black box” LRF video processing system with a general camera link input, a user controller interface, and a camera link or DVI video output. We also describe a custom hardware simulation environment we have built to test our LRF implementation.

Hunt for forgotten warplanes: a unique application for the Goddard Space Flight Center Search and Rescue Synthetic Aperture Radar (SAR2) program

The principal purpose of the Beaconless Search and Rescue program at Goddard Space Flight Center (GSFC) is to utilize synthetic aperture radar (SAR) for the efficient and rapid location of recent small aircraft crashes. An additional side benefit might prove to be the detection and discovery of long lost or forgotten historic aircraft that have now become of immense value for museum display or among wealthy collectors. As the GSFC SAR2 program matures and its achievements in SAR target detection become more widely available, they will be of use to amateur and professional airplane hunters. We recommend that such ancillary benefits be kept in mind during the continued development and testing of such equipment, which would be of benefit to all future generations concerning the history of aviation. We welcome and encourage all participants to notify organizations such as ours of the discovery of any historic aircraft wreckage or intact abandoned old aircraft throughout the world.

SAR processing using SHARC signal processing systems

Synthetic aperture radar (SAR) is uniquely suited to help solve the Search and Rescue problem since it can be utilized either day or night and through both dense fog or thick cloud cover. Other papers in this session, and in this session in 1997, describe the various SAR image processing algorithms that are being developed and evaluated within the Search and Rescue Program. All of these approaches to using SAR data require substantial amounts of digital signal processing: for the SAR image formation, and possibly for the subsequent image processing. In recognition of the demanding processing that will be required for an operational Search and Rescue Data Processing System (SARDPS), NASA/Goddard Space Flight Center and NASA/Stennis Space Center are conducting a technology demonstration utilizing SHARC multi-chip modules from Boeing to perform SAR image formation processing.

Low-cost airborne synthetic aperture radar

This paper describes the rudiments of a design and implementation approach that will produce low-cost and quick turnaround airborne synthetic aperture radar (SAR) systems including designs for remotely piloted vehicles (RPVs). The concept is based on strict adherence to a discipline of simplicity in application boundary definition, the corresponding design that follows, extension of this core of simplicity through the build and test cycle and continuation of this theme when system modification and upgrades are considered. As this paper points out, the tenets for low-cost development of SAR systems are not new. Indeed, several such developments validate the guidelines advocated in this paper. The crux of this end-to-end development simplicity is to minimize the functions assigned to the on-board radar systems, transferring them to less expensive ground-based information processing assets that will perform motion compensation, image signal processing and target identification/classification. This cause limitations in the applications sheath of the airborne system, but in many cases this is an acceptable compromise.