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Reconstructing Point Sets From Distance Distributions IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-03-05 Shuai Huang, Ivan Dokmanić
We address the problem of reconstructing a set of points on a line or a loop from their unassigned noisy pairwise distances. When the points lie on a line, the problem is known as the turnpike; when they are on a loop, it is known as the beltway. We approximate the problem by discretizing the domain and representing the $N$ points via an $N$ -hot encoding, which is a density supported on the discretized
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The Interference Channel Revisited: Aligning Interference by Adjusting Antenna Separation IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-03-03 Amir Leshem, Uri Erez
It is shown that, in a line-of-sight setting, a receiver equipped with two antennas may approximately null an arbitrary large number of spatial directions to any desired degree, while maintaining the interference-free signal-to-noise ratio, by judiciously adjusting the distance between the antenna elements, provided that the spacing can be made sufficiently large. The main theoretical result builds
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Remote sensing of marine oil slicks with hyperspectral camera and an extended database J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-04-01 Françoise Viallefont-Robinet, Laure Roupioz, Karine Caillault, Pierre-Yves Foucher
In the field of offshore oil slicks at the sea surface, radar or optical imagery can provide much useful information. Regarding optical imagery, detection relies on the differences between the reflectance of water alone and oil-covered water. As soon as the thickness of the oil layer is large enough to induce a spectral difference between the reflectance of water and oil-covered water, spectral indices
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Specifying the relationship between land use/land cover change and dryness in central Vietnam from 2000 to 2019 using Google Earth Engine J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-04-01 Thi Mai Thy Pham, The-Duoc Nguyen, Han Thi Ngoc Tham, Thi Nhat Kieu Truong, Lam-Dao Nguyen, Thong Nguyen-Huy
Land use/land cover (LULC) change and climate change are thought to be closely related and mutually influential, especially in contexts where land is converted for urban expansion or agriculture. We represent a first attempt to specify the relationship between LULC change and dryness in a region of Vietnam that is profoundly affected by climate change. Using the temperature–vegetation dryness index
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Mixture of Inference Networks for VAE-Based Audio-Visual Speech Enhancement IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-03-17 Mostafa Sadeghi, Xavier Alameda-Pineda
We address unsupervised audio-visual speech enhancement based on variational autoencoders (VAEs), where the prior distribution of clean speech spectrogram is simulated using an encoder-decoder architecture. At enhancement (test) time, the trained generative model (decoder) is combined with a noise model whose parameters need to be estimated. The initialization of the latent variables describing the
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Random Matrix Based Extended Target Tracking With Orientation: A New Model and Inference IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-03-10 Barkın Tuncer, Emre Özkan
In this study, we propose a novel extended target tracking algorithm which is capable of representing the extent of dynamic objects as an ellipsoid with a time-varying orientation angle. A diagonal positive semi-definite matrix is defined to model objects’ extent within the random matrix framework where the diagonal elements have inverse-Gamma priors. The resulting measurement equation is non-linear
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Constrained Radar Waveform Design for Range Profiling IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-03-17 Bo Tang, Jun Liu, Hai Wang, Yihua Hu
Range profiling refers to the measurement of target response along the radar slant range. It plays an important role in automatic target recognition. In this paper, we consider the design of transmit waveform to improve the range profiling performance of radar systems. Two design metrics are adopted for the waveform optimization problem: one is to maximize the mutual information between the received
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A Stochastic Model for Characterizing Fluctuations in Chemical Sensing IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-03-09 Abhishek Grover, Brejesh Lall
The chemical sensing process can be understood as the physical process consisting of binding and unbinding reactions on the sensing surface. The random nature of the reactions lead to fluctuations in the response of a sensor. The response of the sensor is modeled in the form of a stochastic differential equation (SDE). The model is developed by formulating the binding and unbinding reactions in the
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Frequency Domain Analysis and Equalization for Molecular Communication IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-03-17 Yu Huang, Fei Ji, Zhuangkun Wei, Miaowen Wen, Xuan Chen, Yuankun Tang, Weisi Guo
Molecular Communication (MC) is a promising micro-scale technology that enables wireless connectivity in electromagnetically challenged conditions. The signal processing approaches in MC are different from conventional wireless communications as molecular signals suffer from severe inter-symbol interference (ISI) and signal-dependent counting noise due to the stochastic diffusion process of the information
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Theory and Design of Joint Time-Vertex Nonsubsampled Filter Banks IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-03-10 Junzheng Jiang, Hairong Feng, David B. Tay, Shuwen Xu
Graph signal processing (GSP) is a field that deals with data residing on irregular domains, i.e. graph signals. In this field, the graph filter bank is one of the most important developments, owing to its ability to provide multiresolution analysis of graph signals. However, most of the current research on graph filter bank focuses on static graph signals. The research does not exploit the temporal
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Evaluation of aqua MODIS thermal emissive bands stability through radiative transfer modeling J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-04-01 Tung-Chang Liu, Xiaoxiong Xiong, Xi Shao, Yong Chen, Aisheng Wu, Tiejun Chang, Ashish Shrestha
Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua has been in operation providing continuous global observations for science research and applications since 2002. The long-term stability of thermal emissive bands (TEBs) of Aqua MODIS was monitored through inter-comparisons with measurements by hyperspectral or multi-spectral infrared sensors or through vicarious monitoring over cold targets
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Discrimination of common New Zealand native seaweeds from the invasive Undaria pinnatifida using hyperspectral data J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-04-01 Sadhvi Selvaraj, Bradley Stuart Case, William Lindsey White
Undaria pinnatifida (Harvey) Suringar, native to north-western Asia, is a prolific invasive seaweed species that has established across much of New Zealand, competing and co-existing with native seaweed species. Remote sensing could be used to map both invasive and native seaweeds in New Zealand. The aim of this study is to evaluate the hyperspectral differences (and the wavelengths at which they differ)
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Graphon Filters: Graph Signal Processing in the Limit IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-24 Matthew W. Morency, Geert Leus
Graph signal processing is an emerging field which aims to model processes that exist on the nodes of a network and are explained through diffusion over this structure. Graph signal processing works have heretofore assumed knowledge of the graph shift operator. Our approach is to investigate the question of graph filtering on a graph about which we only know a model. To do this we leverage the theory
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Robust Non-Linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-03-02 Jicong Fan, Chengrun Yang, Madeleine Udell
Low dimensional nonlinear structure abounds in datasets across computer vision and machine learning. Kernelized matrix factorization techniques have recently been proposed to learn these nonlinear structures for denoising, classification, dictionary learning, and missing data imputation, by observing that the image of the matrix in a sufficiently large feature space is low-rank. However, these nonlinear
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Sparse-Group Lasso for Graph Learning From Multi-Attribute Data IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-08 Jitendra K. Tugnait
We consider the problem of inferring the conditional independence graph (CIG) of high-dimensional Gaussian vectors from multi-attribute data. Most existing methods for graph estimation are based on single-attribute models where one associates a scalar random variable with each node. In multi-attribute graphical models, each node represents a random vector. In this paper, we present a sparse-group lasso
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Quantizer Design to Exploit Common Information in Layered and Scalable Coding IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-03-08 Mehdi Salehifar, Tejaswi Nanjundaswamy, Kenneth Rose
This paper considers a layered coding framework with a relaxed hierarchical structure, tailored to serve content at multiple quality levels, where a key challenge is the conflict between coding optimality at each layer and efficient use of storage and networking resources. The prevalent approach of storing and transmitting independent copies for each quality level, is highly wasteful in resources.
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Band-Stop Smoothing Filter Design IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-19 Arman Kheirati Roonizi, Christian Jutten
Smoothness priors and quadratic variation (QV) regularization are widely used techniques in many applications ranging from signal and image processing, computer vision, pattern recognition, and many other fields of engineering and science. In this contribution, an extension of such algorithms to band-stop smoothing filters (BSSFs) is investigated. For designing a BSSF, the most important parameters
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Globally Convergent Gradient Projection Type Algorithms for a Class of Robust Hypothesis Testings IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-15 Ting Ma, Enbin Song, Qingjiang Shi
This paper considers the popular minimax robust hypothesis testing problem—seeking the optimal decision rule with a minimum error probability for the least favorable distributions (LFDs) lying within an uncertainty set, which is characterized by an upper bound on the distance between actual and nominal densities. First, we convert the minimax robust hypothesis testing problem to a convex minimization
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An Improved Convergence Analysis for Decentralized Online Stochastic Non-Convex Optimization IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-03-01 Ran Xin, Usman A. Khan, Soummya Kar
In this paper, we study decentralized online stochastic non-convex optimization over a network of nodes. Integrating a technique called gradient tracking in decentralized stochastic gradient descent, we show that the resulting algorithm, GT-DSGD , enjoys certain desirable characteristics towards minimizing a sum of smooth non-convex functions. In particular, for general smooth non-convex functions
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General Cauchy Conjugate Gradient Algorithms Based on Multiple Random Fourier Features IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-03-12 Haonan Zhang, Bo Yang, Lin Wang, Shiyuan Wang
The general Cauchy loss (GCL) criterion has been successfully proposed to improve the performance of the Cauchy loss (CL) criterion for linear adaptive filtering in the presence of complex non-Gaussian noise. However, the commonly used adaptive filtering algorithms based on the GCL criterion utilize the stochastic gradient descent (SGD) method to update their weights with slow convergence rate and
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Efficient Sensor Placement for Signal Reconstruction Based on Recursive Methods IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-03-03 Bangjun Li, Haoran Liu, Ruzhu Wang
Selection of sparse sensors to recover the global signal field is a crucial task in many areas. Most of the existing algorithms tackle this problem by optimizing the surrogates of reconstruction criterion which relies on structural assumptions or low-dimensional models. In this paper, we propose a novel sensor placement method using signal reconstruction error as the cost function, sequentially minimize
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Extraction method for single Zanthoxylum bungeanum in karst mountain area based on unmanned aerial vehicle visible-light images J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-04-01 Meng Zhu, Zhongfa Zhou, Denghong Huang, Ruiwen Peng, Yang Zhang, Yongliu Li, Wenhui Zhang
The efficient and rapid extraction of information about the growth period of the Zanthoxylum bungeanum maxim is one of the prerequisites for analyzing and mastering its growth trend. The 5-cm high-resolution visible-light red, green, blue (RGB) image of the Zanthoxylum bungeanum growing period is obtained based on the low-altitude aerial photography of a quad-rotor unmanned aerial vehicle (UAV) platform
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Synthetic ultrawideband orbital angular momentum radar J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-03-01 Daniel J. Orfeo, Dylan Burns, Dryver R. Huston, Tian Xia
Control of orbital angular momentum (OAM) offers the potential for increases in control and sensitivity for high-performance microwave systems. EM waves with properties dependent on spatial distribution are said to be “structured.” Control of OAM in microwave systems is an example of a wave structure that exploits EM degrees of freedom, which most conventional systems do not use. OAM is characterized
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Target Localization Geometry Gain in Distributed MIMO Radar IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-26 Mohammad Sadeghi, Fereidoon Behnia, Rouhollah Amiri, Alfonso Farina
In this paper, we analyze the accuracy of target localization in multiple-input multiple-output (MIMO) radars with widely-separated antennas. The relative target-antennas geometry plays an important role in target localization. We investigate the optimal placement of transmit and receive antennas for coherent and non-coherent processing, based on maximizing the determinant of the Fisher information
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Mathematical Theory of Atomic Norm Denoising in Blind Two-Dimensional Super-Resolution IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-26 Mohamed A. Suliman, Wei Dai
This paper develops a new mathematical framework for denoising in blind two-dimensional (2D) super-resolution upon using the atomic norm. The framework denoises a signal that consists of a weighted sum of an unknown number of time-delayed and frequency-shifted unknown waveforms from its noisy measurements. Moreover, the framework also provides an approach for estimating the unknown parameters in the
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Sparse Symmetric Linear Arrays With Low Redundancy and a Contiguous Sum Co-Array IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-10 Robin Rajamäki, Visa Koivunen
Sparse arrays can resolve significantly more scatterers or sources than sensor by utilizing the co-array — a virtual array structure consisting of pairwise differences or sums of sensor positions. Although several sparse array configurations have been developed for passive sensing applications, far fewer active array designs exist. In active sensing, the sum co-array is typically more relevant than
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Stochastic Gradient-Based Distributed Bayesian Estimation in Cooperative Sensor Networks IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-11 Jose Cadena, Priyadip Ray, Hao Chen, Braden Soper, Deepak Rajan, Anton Yen, Ryan Goldhahn
Distributed Bayesian inference provides a full quantification of uncertainty offering numerous advantages over point estimates that autonomous sensor networks are able to exploit. However, fully-decentralized Bayesian inference often requires large communication overheads and low network latency, resources that are not typically available in practical applications. In this paper, we propose a decentralized
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A Multi-Taper S-Transform Method for Spectral Estimation of Stationary Processes IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-05 Zifeng Huang, You-Lin Xu
The S-transform (ST) is a method of time-frequency analysis of time series. We develop here the Multi-taper S-transform (MTST) for spectral estimation of multi-variate stationary processes through replacing the scalable Gaussian window of the ST with scalable, adjustable orthogonal time-frequency Hermite functions. The MTST is shown to reduce bias and variance of power spectral density (PSD) estimates
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Front Cover IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2021-03-24
Presents the front cover for this issue of the publication.
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IEEE Transactions on Geoscience and Remote Sensing publication information IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2021-03-24
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Table of contents IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2021-03-24
Presents the table of contents for this issue of the publication.
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Hyperspectral Image Denoising via Clustering-Based Latent Variable in Variational Bayesian Framework IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2021-03-24 Peyman Azimpour, Tahereh Bahraini, Hadi Sadoghi Yazdi
The hyperspectral-image (HSI) noise-reduction step is a very significant preprocessing phase of data-quality enhancement. It has been attracting immense research attention in the remote sensing and image processing domains. Many methods have been developed for HSI restoration, the goal of which is to remove noise from the whole HSI cube simultaneously without considering the spectral–spatial similarity
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TechRxiv: Share Your Preprint Research with the World! IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2021-03-24
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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Introducing IEEE Collabratec IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2021-03-24
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IEEE Transactions on Geoscience and Remote Sensing information for authors IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2021-03-24
These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
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IEEE Transactions on Geoscience and Remote Sensing institutional listings IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2021-03-24
Presents a listing of institutions relevant for this issue of the publication.
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Predicting gross primary productivity and PsnNet over a mixed ecosystem under tropical seasonal variability: a comparative study between different machine learning models and correlation-based statistical approaches J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-03-01 Subhajit Bandopadhyay, Lopita Pal, Rahul Deb Das
The global interaction of CO2 flux was highly dynamic under seasonal and inter-annual variability. Thus, precise estimation of gross primary productivity (GPP) and net photosynthesis (PsnNet) under seasonal variations was important to understand the global carbon cycle and ecosystem response to climate variability. Considering this significance, in our paper, we have conducted a comparative investigation
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An Efficient Power Allocation Strategy for Maneuvering Target Tracking in Cognitive MIMO Radar IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-03-17 Haowei Zhang, Weijian Liu, Binfeng Zong, Junpeng Shi, Junwei Xie
In this paper, an efficient power allocation (PA) strategy is developed for maneuvering target tracking (MTT) in the collocated MIMO radar. The mechanism of our strategy is to implement the optimal PA based on the prior target maneuvering information in the tracking cycle. The predicted conditional Cramer–Rao lower bound (PC-CRLB) is derived, normalized and adopted as the optimization criterion, since
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Power Allocation for Coexisting Multicarrier Radar and Communication Systems in Cluttered Environments IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-19 Fangzhou Wang, Hongbin Li
In this paper, power allocation is examined for the coexistence of a radar and a communication system that employ multicarrier waveforms. We propose two designs for the considered spectrum sharing problem by maximizing the output signal-to-interference-plus-noise ratio (SINR) at the radar receiver while maintaining certain communication throughput and power constraints. The first is a joint design
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A Comparative Study of Gamma Markov Chains for Temporal Non-Negative Matrix Factorization IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-19 Louis Filstroff, Olivier Gouvert, Cédric Févotte, Olivier Cappé
Non-negative matrix factorization (NMF) has become a well-established class of methods for the analysis of non-negative data. In particular, a lot of effort has been devoted to probabilistic NMF, namely estimation or inference tasks in probabilistic models describing the data, based for example on Poisson or exponential likelihoods. When dealing with time series data, several works have proposed to
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Filtering in Pairwise Markov Model With Student's t Non-Stationary Noise With Application to Target Tracking IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-26 Guanghua Zhang, Jian Lan, Le Zhang, Fengshou He, Shaomin Li
Hidden Markov models are widely used for target tracking, where the process and measurement noises are usually modeled as independent Gaussian distributions for mathematical simplicity. However, the independence and Gaussian assumptions do not always hold in practice. For example, in a typical target tracking application, a radar is utilized to track a non-cooperative target. Measurement noise is correlated
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An Efficient Forecasting Approach to Reduce Boundary Effects in Real-Time Time-Frequency Analysis IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-26 Adrien Meynard, Hau-Tieng Wu
Time-frequency (TF) representations of time series are intrinsically subject to the boundary effects. As a result, the structures of signals that are highlighted by the representations are garbled when approaching the boundaries of the TF domain. In this paper, for the purpose of real-time TF information acquisition of nonstationary oscillatory time series, we propose a numerically efficient approach
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Regularized Local Basis Function Approach to Identification of Nonstationary Processes IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-25 Artur Gańcza, Maciej Niedźwiecki, Marcin Ciołek
The problem of identification of nonstationary stochastic processes (systems or signals) is considered and a new class of identification algorithms, combining the basis functions approach with local estimation technique, is described. Unlike the classical basis function estimation schemes, the proposed regularized local basis function estimators are not used to obtain interval approximations of the
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On-orbit calibration and performance assessments of Terra and Aqua MODIS thermal emissive bands J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-03-01 Xiaoxiong Xiong, Aisheng Wu, Tiejun Chang, Truman Wilson, Yonghong Li, Na Chen, Ashish Shrestha, Carlos Perez Diaz
Terra and Aqua MODIS have successfully operated for more than 20 and 18 years, respectively, and far exceeded their designed lifetimes of 6 years. MODIS has 36 spectral bands, among which 16 are the thermal emissive bands (TEB) covering a wavelength range from 3.75 to 14.24 μm. Observations from the MODIS TEB have been used to generate a number of data products, such as surface/cloud/atmospheric temperatures
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Sentinel-1 and Sentinel-2 data fusion system for surface water extraction J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-03-01 Mostafa Saghafi, Ahmad Ahmadi, Behnaz Bigdeli
Detecting and monitoring surface water has received much attention in recent decades. Surface water is one of the most critical water resources for both human and ecological systems. Remote sensing technology has made it possible to have accurate and frequent updates of surface water. We propose a remote sensing multisensor fusion system using optical data including Landsat-8 and Sentinel-2 and RADAR
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Identification of windthrow-endangered infrastructure combining LiDAR-based tree extraction methods using GIS J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-03-01 Michael Steffen, Mandy Schipek, Anne-Farina Lohrengel, Lennart Meine
Windthrows induced by strong winds pose a major threat to both transport infrastructure and road users. Therefore, an exposure analysis of trees along the federal trunk road network was carried out and exemplarily applied for the federal state of North Rhine-Westphalia, Germany. The aim of this project was the development of a GIS-based method on the basis of freely accessible high-resolution airborne
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Optimal Restricted Isometry Condition of Normalized Sampling Matrices for Exact Sparse Recovery With Orthogonal Least Squares IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-17 Junhan Kim, Jian Wang, Byonghyo Shim
In this paper, we analyze the performance guarantee of the orthogonal least squares (OLS) algorithm expressed in terms of the restricted isometry property (RIP). We show the optimality of the proposed guarantee by showing that there exists an example for which OLS fails to work when the proposed condition is violated. In addition, we show that if the columns of a sampling matrix are $\ell _{2}$ -normalized
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Orbita hyperspectral satellite image for land cover classification using random forest classifier J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-03-01 You Mo, Ruofei Zhong, Shisong Cao
The Orbita hyperspectral satellite (OHS) is the first commercial hyperspectral satellite in China that completed launching and networking. It can collect world-class hyperspectral data and obtain aerial hyperspectral imagery with 32 bands covering the spectrum range from 400 to 1000 nm at a 10-m resolution, which are of great significance for the quantitative analyses of remote sensing and refined
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Fully convolutional DenseNet with adversarial training for semantic segmentation of high-resolution remote sensing images J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-03-01 Xuejun Guo, Zehua Chen, Chengyi Wang
Semantic segmentation is an important and foundational task in the application of high-resolution remote sensing images (HRRSIs). However, HRRSIs feature large differences within categories and minor variances across categories, posing a significant challenge to the high-accuracy semantic segmentation of HRRSIs. To address this issue and obtain powerful feature expressiveness, a deep conditional generative
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Adaptive IQ Mismatch Compensation in Time-Domain Using Frequency-Domain Observations IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-01-05 Elina Nayebi, Pranav Dayal, Kee-Bong Song
The imbalance between in-phase (I) and quadrature (Q) branches of quadrature down-conversion receivers creates interference between the mirror frequencies in baseband. We introduce two new adaptive algorithms for compensating frequency-dependent IQ imbalance in orthogonal frequency-division multiplexing (OFDM) systems. The proposed algorithms utilize frequency-domain observations to update filter weights
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Majorization-Minimization on the Stiefel Manifold With Application to Robust Sparse PCA IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-12 Arnaud Breloy, Sandeep Kumar, Ying Sun, Daniel P. Palomar
This paper proposes a framework for optimizing cost functions of orthonormal basis learning problems, such as principal component analysis (PCA), subspace recovery, orthogonal dictionary learning, etc. The optimization algorithm is derived using the majorization-minimization framework in conjunction with orthogonal projection reformulations to deal with the orthonormality constraint in a systematic
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Detection of bark beetle infestation in drone imagery via thresholding cellular automata J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-03-01 S. Elisa Schaeffer, Manuel Jiménez-Lizárraga, Sara V. Rodriguez-Sanchez, Gerardo Cuellar-Rodríguez, Oscar A. Aguirre-Calderón, Angel M. Reyna-González, Alan Escobar
Bark beetle outbreaks are a significant cause of loss of vegetation cover, for which accurate monitoring of forest areas is required to detect and control bark beetle outbreaks as early as possible. A tool that processes aerial imagery from an unmanned aerial vehicle to automatically detect levels of damage caused by bark beetle outbreaks is proposed and evaluated. The true-color RGB flight imagery
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Adaptive Radar Detection in Low-Rank Heterogeneous Clutter via Invariance Theory IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-10 Yao Rong, Augusto Aubry, Antonio De Maio, Mengjiao Tang
This paper addresses adaptive detection of a range distributed target in the presence of dominant heterogeneous clutter, which is (possibly) low-rank and lies in a known subspace, plus Gaussian thermal noise. First, this problem is transformed into an equivalent binary hypothesis test with observations having block-diagonal covariance matrices. Then an invariance analysis is conducted on the resulting
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A Depth-First Iterative Algorithm for the Conjugate Pair Fast Fourier Transform IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-18 Alexandre Becoulet, Amandine Verguet
The Split-Radix Fast Fourier Transform has the same low arithmetic complexity as the related Conjugate Pair Fast Fourier Transform. Both transforms have an irregular datapath structure which is straightforwardly expressed only in recursive forms. Furthermore, the conjugate pair variant has a complicated input indexing pattern which requires existing iterative implementations to rely on precomputed
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Spectrally-Agile Waveform Design for Wideband MIMO Radar Transmit Beampattern Synthesis via Majorization-ADMM IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-01-19 Wen Fan, Junli Liang, Guangshan Lu, Xuhui Fan, Hing Cheung So
This paper investigates the problem of wideband multiple-input multiple-output radar transmit beampattern synthesis in a spectrally dense environment, and two optimization problems are formulated according to practical consideration. The first one is beampattern matching design under the scenario of known interference spectral band while the second addresses minimum peak sidelobe beampattern design
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Cyclostationary Processes With Evolving Periods and Amplitudes IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-04 Soumya Das, Marc G. Genton
Wide-sense cyclostationary processes are an important class of non-stationary processes that have a periodic structure in their first- and second-order moments. This article extends the notion of cyclostationarity (in the wide sense) to processes where the mean and covariance functions might depart from strict periodicities and constant amplitudes. Specifically, we propose a novel and flexible class
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Evaluating eco-environment in urban agglomeration from a vegetation-impervious surface-soil-air framework: an example in Ningxia, China J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-03-01 Hao Sun, Ling Wu, Jiaqi Hu, Liru Ma, Huan Li, Dan Wu
Urban agglomerations (UA) are the fastest growing regional types during recent years, especially in developing countries. Monitoring and evaluating the eco-environment quality of UA is significant for sustainability. The previous remote sensing models of urban eco-environment are generally based on the vegetation-impervious surface-soil framework, which neglects the air quality in urban areas. We constructed
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Stellar map centroid positioning based on dark channel denoising and feasibility of jitter detection on ZiYuan3 satellite platform J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-03-01 Hong Zhu, Junfeng Xie, Xinming Tang, Di Jia, Guangtong Sun
Stellar map denoising and centroid positioning, which directly determine the postpositioning accuracy of star trackers, are key technologies in stellar map processing. Due to the influence of a complex starry sky background, there is often a large amount of noise in stellar maps, which makes it difficult to accurately locate the stellar centroid. A stellar map processing method based on dark channel
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Randomized Stepped Frequency Radars Exploiting Block Sparsity of Extended Targets: A Theoretical Analysis IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-12 Lei Wang, Tianyao Huang, Yimin Liu
Randomized stepped frequency radar (RSFR) is very attractive for tasks under complex electromagnetic environment. Due to the synthetic high range resolution in RSFRs, a target usually occupies a series of range cells and is called an extended target . To reconstruct the range-Doppler information in a RSFR, previous studies based on sparse recovery mainly exploit the sparsity of the target scene but
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Phase Retrieval for Partially Coherent Observations IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2021-02-04 Jonas Kornprobst, Alexander Paulus, Josef Knapp, Thomas F. Eibert
Phase retrieval is in general a non-convex and non-linear task and the corresponding algorithms struggle with the issue of local minima. We consider the case where the measurement samples within typically very small and disconnected subsets are coherently linked to each other — which is a reasonable assumption for our objective of antenna measurements. Two classes of measurement setups are discussed
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