Efficient Raw Signal Generation Based on Equivalent Scatterer and Subaperture Processing for SAR with Arbitrary Motion

An efficient SAR raw signal generation method based on equivalent scatterer and subaperture processing is proposed in this paper. It considers the radar’s motion track, which can obtain the precise raw signal for the real SAR. First, the imaging geometry with arbitrary motion is established, and then the scene is divided into several equidistant rings. Based on the equivalent scatterer model, the approximate expression of the SAR system transfer function is derived, thus each pulse’s raw signal can be generated by the convolution of the transmitted signal and system transfer function, performed by the fast Fourier transform (FFT). To further improve the simulation efficiency, the subaperture and polar subscene processing is used. The system transfer function of pluses for the same subaperture is calculated simultaneously by the weighted sum of all subscenes’ equivalent backscattering coefficient in the same equidistant ring, performed by the nonuniform FFT (NUFFT). The method only involves the FFT, NUFFT and complex multiplication operations, which means the easier implementation and higher efficiency. Simulation results are given to prove the validity of this method

Efficient SAR Raw Data Compression in Frequency Domain

SAR raw data compression is necessary to reduce huge amounts of SAR data for a memory on board a satellite, space shuttle or aircraft and for later downlink to a ground station. In view of interferometric and polarimetric applications for SAR data, it becomes more and more important to pay attention to phase errors caused by data compression. Herein, a detailed comparison of block adaptive quantization in time domain (BAQ) and in frequency domain (FFT-BAQ) is given. Inclusion of raw data compression in the processing chain allows an efficient use of the FFT-BAQ and makes implementation for on-board data compression feasible. The FFT-BAQ outperforms the BAQ in terms of signal-to-quantization noise ratio and phase error and allows a direct decimation of the oversampled data equivalent to FIR-filtering in time domain. Impacts on interferometric phase and coherency are also given

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

An introduction to the interim digital SAR processor and the characteristics of the associated Seasat SAR imagery

Basic engineering data regarding the Interim Digital SAR Processor (IDP) and the digitally correlated Seasat synthetic aperature radar (SAR) imagery are presented. The correlation function and IDP hardware/software configuration are described, and a preliminary performance assessment presented. The geometric and radiometric characteristics, with special emphasis on those peculiar to the IDP produced imagery, are described

Communication channel analysis and real time compressed sensing for high density neural recording devices

Next generation neural recording and Brain- Machine Interface (BMI) devices call for high density or distributed systems with more than 1000 recording sites. As the recording site density grows, the device generates data on the scale of several hundred megabits per second (Mbps). Transmitting such large amounts of data induces significant power consumption and heat dissipation for the implanted electronics. Facing these constraints, efficient on-chip compression techniques become essential to the reduction of implanted systems power consumption. This paper analyzes the communication channel constraints for high density neural recording devices. This paper then quantifies the improvement on communication channel using efficient on-chip compression methods. Finally, This paper describes a Compressed Sensing (CS) based system that can reduce the data rate by > 10x times while using power on the order of a few hundred nW per recording channel