Sentinel-1 Imaging Performance Verification with TerraSAR-X

This paper presents dedicated analyses of TerraSAR-X data with respect to the Sentinel-1 TOPS imaging mode. First, the analysis of Doppler centroid behaviour for high azimuth steering angles, as occurs in TOPS imaging, is investigated followed by the analysis and compensation of residual scalloping. Finally, the Flexible-Dynamic BAQ (FD-BAQ) raw data compression algorithm is investigated for the first time with real TerraSAR-X data and its performance is compared to state-of-the-art BAQ algorithms. The presented analyses demonstrate the improvements of the new TOPS imaging mode as well as the new FD-BAQ data compression algorithm for SAR image quality in general and in particular for Sentinel-1

OFDM Synthetic Aperture Radar Imaging with Sufficient Cyclic Prefix

The existing linear frequency modulated (LFM) (or step frequency) and random noise synthetic aperture radar (SAR) systems may correspond to the frequency hopping (FH) and direct sequence (DS) spread spectrum systems in the past second and third generation wireless communications. Similar to the current and future wireless communications generations, in this paper, we propose OFDM SAR imaging, where a sufficient cyclic prefix (CP) is added to each OFDM pulse. The sufficient CP insertion converts an inter-symbol interference (ISI) channel from multipaths into multiple ISI-free subchannels as the key in a wireless communications system, and analogously, it provides an inter-range-cell interference (IRCI) free (high range resolution) SAR image in a SAR system. The sufficient CP insertion along with our newly proposed SAR imaging algorithm particularly for the OFDM signals also differentiates this paper from all the existing studies in the literature on OFDM radar signal processing. Simulation results are presented to illustrate the high range resolution performance of our proposed CP based OFDM SAR imaging algorithm.Comment: This version has been accepted by IEEE Transactions on Geoscience and Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing 201

IRCI Free Range Reconstruction for SAR Imaging with Arbitrary Length OFDM Pulse

Our previously proposed OFDM with sufficient cyclic prefix (CP) synthetic aperture radar (SAR) imaging algorithm is inter-range-cell interference (IRCI) free and achieves ideally zero range sidelobes for range reconstruction. In this OFDM SAR imaging algorithm, the minimum required CP length is almost equal to the number of range cells in a swath, while the number of subcarriers of an OFDM signal needs to be more than the CP length. This makes the length of a transmitted OFDM sequence at least almost twice of the number of range cells in a swath and for a wide swath imaging, the transmitted OFDM pulse length becomes long, which may cause problems in some radar applications. In this paper, we propose a CP based OFDM SAR imaging with arbitrary pulse length, which has IRCI free range reconstruction and its pulse length is independent of a swath width. We then present a novel design method for our proposed arbitrary length OFDM pulses. Simulation results are presented to illustrate the performances of the OFDM pulse design and the arbitrary pulse length CP based OFDM SAR imaging.Comment: 29 pages, 10 figures, regular pape

Information extraction and transmission techniques for spaceborne synthetic aperture radar images

Information extraction and transmission techniques for synthetic aperture radar (SAR) imagery were investigated. Four interrelated problems were addressed. An optimal tonal SAR image classification algorithm was developed and evaluated. A data compression technique was developed for SAR imagery which is simple and provides a 5:1 compression with acceptable image quality. An optimal textural edge detector was developed. Several SAR image enhancement algorithms have been proposed. The effectiveness of each algorithm was compared quantitatively

Sparse representation-based synthetic aperture radar imaging

There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying scene. Based on the observation that typical underlying scenes usually exhibit sparsity in terms of such features, we develop an image formation method which formulates the SAR imaging problem as a sparse signal representation problem. Sparse signal representation, which has mostly been exploited in real-valued problems, has many capabilities such as superresolution and feature enhancement for various reconstruction and recognition tasks. However, for problems of complex-valued nature, such as SAR, a key challenge is how to choose the dictionary and the representation scheme for effective sparse representation. Since we are usually interested in features of the magnitude of the SAR reflectivity field, our new approach is designed to sparsely represent the magnitude of the complex-valued scattered field. This turns the image reconstruction problem into a joint optimization problem over the representation of magnitude and phase of the underlying field reflectivities. We develop the mathematical framework for this method and propose an iterative solution for the corresponding joint optimization problem. Our experimental results demonstrate the superiority of this method over previous approaches in terms of both producing high quality SAR images as well as exhibiting robustness to uncertain or limited data

Sparse representation-based SAR imaging

There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying scene. Based on the observation that typical underlying scenes usually exhibit sparsity in terms of such features, we develop an image formation method which formulates the SAR imaging problem as a sparse signal representation problem. Sparse signal representation, which has mostly been exploited in real-valued problems, has many capabilities such as superresolution and feature enhancement for various reconstruction and recognition tasks. However, for problems of complex-valued nature, such as SAR, a key challenge is how to choose the dictionary and the representation scheme for effective sparse representation. Since we are usually interested in features of the magnitude of the SAR reflectivity field, our new approach is designed to sparsely represent the magnitude of the complex-valued scattered field. This turns the image reconstruction problem into a joint optimization problem over the representation of magnitude and phase of the underlying field reflectivities. We develop the mathematical framework for this method and propose an iterative solution for the corresponding joint optimization problem. Our experimental results demonstrate the superiority of this method over previous approaches in terms of both producing high quality SAR images as well as exhibiting robustness to uncertain or limited data

SAR-Based Vibration Estimation Using the Discrete Fractional Fourier Transform

A vibration estimation method for synthetic aperture radar (SAR) is presented based on a novel application of the discrete fractional Fourier transform (DFRFT). Small vibrations of ground targets introduce phase modulation in the SAR returned signals. With standard preprocessing of the returned signals, followed by the application of the DFRFT, the time-varying accelerations, frequencies, and displacements associated with vibrating objects can be extracted by successively estimating the quasi-instantaneous chirp rate in the phase-modulated signal in each subaperture. The performance of the proposed method is investigated quantitatively, and the measurable vibration frequencies and displacements are determined. Simulation results show that the proposed method can successfully estimate a two-component vibration at practical signal-to-noise levels. Two airborne experiments were also conducted using the Lynx SAR system in conjunction with vibrating ground test targets. The experiments demonstrated the correct estimation of a 1-Hz vibration with an amplitude of 1.5 cm and a 5-Hz vibration with an amplitude of 1.5 mm

Data compression in remote sensing applications

A survey of current data compression techniques which are being used to reduce the amount of data in remote sensing applications is provided. The survey aspect is far from complete, reflecting the substantial activity in this area. The purpose of the survey is more to exemplify the different approaches being taken rather than to provide an exhaustive list of the various proposed approaches

A Self-Organizing Neural System for Learning to Recognize Textured Scenes

A self-organizing ARTEX model is developed to categorize and classify textured image regions. ARTEX specializes the FACADE model of how the visual cortex sees, and the ART model of how temporal and prefrontal cortices interact with the hippocampal system to learn visual recognition categories and their names. FACADE processing generates a vector of boundary and surface properties, notably texture and brightness properties, by utilizing multi-scale filtering, competition, and diffusive filling-in. Its context-sensitive local measures of textured scenes can be used to recognize scenic properties that gradually change across space, as well a.s abrupt texture boundaries. ART incrementally learns recognition categories that classify FACADE output vectors, class names of these categories, and their probabilities. Top-down expectations within ART encode learned prototypes that pay attention to expected visual features. When novel visual information creates a poor match with the best existing category prototype, a memory search selects a new category with which classify the novel data. ARTEX is compared with psychophysical data, and is benchmarked on classification of natural textures and synthetic aperture radar images. It outperforms state-of-the-art systems that use rule-based, backpropagation, and K-nearest neighbor classifiers.Defense Advanced Research Projects Agency; Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657