On robust estimation of the location parameter

Central to the looming paradigm shift toward data-intensive science, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in many fields. Shall we embrace deep learning as the key to everything? Or should we resist a black-box solution? These are controversial issues within the remote-sensing community. In this article, we analyze the challenges of using deep learning for remote-sensing data analysis, review recent advances, and provide resources we hope will make deep learning in remote sensing seem ridiculously simple. More importantly, we encourage remote-sensing scientists to bring their expertise into deep learning and use it as an implicit general model to tackle unprecedented, large-scale, influential challenges, such as climate change and urbanization.

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Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.

/pdf/deep-learning-in-remote-sensing-a-review-3epn5qlsfr.pdf

Deep learning in remote sensing: a review

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Two Dimensional Phase Unwrapping Theory Algorithms And Software

In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, and natural language processing. 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 not only be aware of advancements such as DL, but also be leading researchers in this area. 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 they relate to (i) inadequate data sets, (ii) human-understandable solutions for modeling physical phenomena, (iii) big data, (iv) nontraditional 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.

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

This paper describes the C-band multi-polarization Synthetic Aperture Radar (SAR) sensor for the Gaofen-3 (GF-3) mission. Based on the requirement analysis, the design of working modes and SAR payload are given. An accurate antenna model is introduced for the pattern optimization and SAR performance calculation. The paper concludes with an overview of predicted performance which was verified by in-orbit tests.

The SAR Payload Design and Performance for the GF-3 Mission

The characteristic difference between targets and clutter is analyzed. Considering the ship target's gray intensity distribution and its shape difference compared to the clutter, in this letter, a new algorithm is presented based on correlation. The algorithm utilizes the strong gray intensity correlation in the ship target; also, the joint gray intensity distribution using 2-D joint log-normal distribution of a pixel with neighboring pixels in the clutter is modeled, which can be used for correlation-based joint constant false alarm rate detection. Using this algorithm, the false alarms caused by speckle and local background nonhomogeneity can be greatly reduced. The detection performance is much better.

A New CFAR Ship Detection Algorithm Based on 2-D Joint Log-Normal Distribution in SAR Images

Electromagnetic orbital angular momentum (OAM) has attracted a lot of interest recently in the field of communication systems. OAM-carrying radio beams can be generated and detected by several devices. In this paper, we use a circular phased antenna array to generate OAM-carrying beams that possess ring-shaped intensities and helical phase fronts. We also propose and validate a communication system that uses OAM-based multiplexing. A circular antenna array is used to generate the superimposed OAMmode by using appropriate excitation settings. The transmission characteristics of OAM modes with respect to different transmission distances are investigated. We find that the OAM-based multiplexing technology is only feasible within a certain distance range. The obtained experimental results are consistent with simulation and numerical calculation results.

Generation and Transmission of OAM-Carrying Vortex Beams Using Circular Antenna Array

This paper presents a new bistatic point target reference spectrum. It is derived by using the 2-D principle of stationary phase which is first applied in the synthetic aperture radar (SAR) community. The spectrum contains two hyperbolic range-azimuth coupling terms and thus is very similar to the monostatic spectrum. It shows the characteristic of the conventional monostatic SAR besides an additional azimuth scaling term. Therefore, it makes the common Doppler-based monostatic processing algorithms readily suitable to handle the Bistatic SAR (BiSAR) data in the moderate-squint azimuth-variant configurations with two moving platforms. Based on the spectrum, two Doppler-based monostatic imaging algorithms [i.e., range-Doppler algorithm (RDA) and chirp-scaling algorithm (CSA)] are readily implemented to deal with the moderate-squint azimuth-variant BiSAR data. Compared to the processing procedure for the monostatic SAR, the RDA and CSA for the BiSAR need only the adjustment of Doppler parameters. Finally, the potential and limitation of the spectrum are analyzed, and the real raw data in the spaceborne/airborne configurations are used to validate the proposed spectrum and processing methods.

Processing the Azimuth-Variant Bistatic SAR Data by Using Monostatic Imaging Algorithms Based on Two-Dimensional Principle of Stationary Phase

Among many synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms, convolutional neural network (CNN)-based algorithms are the commonly used methods. However, most previous SA...

Yunkai Deng

Papers

The SAR Payload Design and Performance for the GF-3 Mission

A New CFAR Ship Detection Algorithm Based on 2-D Joint Log-Normal Distribution in SAR Images

Generation and Transmission of OAM-Carrying Vortex Beams Using Circular Antenna Array

Processing the Azimuth-Variant Bistatic SAR Data by Using Monostatic Imaging Algorithms Based on Two-Dimensional Principle of Stationary Phase

SAR ATR based on displacement- and rotation-insensitive CNN