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2024 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2024

作者： Haddad, Nabil Harkati, Lekhmissi Hadj-Rabah, Karima Bouaraba, Azzedine Mesloub, Ammar Ecole Militaire Polytechnique Antennas and Microwave Devices Laboratory Algeria Ecole Militaire Polytechnique Radar Laboratory Algeria University of Bab Ezzouar Image Processing and Radiation Laboratory Algeria

ISBN: (纸本)9798350358582

This paper proposes an iterative adaptation of the Multiple Signal Classification (MUSIC) algorithm for single-look tomographic Synthetic Aperture radar reconstruction. Our primary objective is to adapt MUSIC for single-look scenarios to deal with the spatial resolution degradation associated with multi-look processing. Compared to the spatial smoothing approach proposed in the literature, a key advantage of our method is its independence from requiring a uniform baseline. Consequently, there is no need for sector interpolation to make the baseline uniform. The proposed method begins with an initialization using the pseudo-spectrum derived from matched spatial filtering. The latter is enhanced through a series of iterative steps, resulting in substantial resolution improvements. Numerical simulations confirm the superior performances of our method compared to state-of-the-art iterative approaches. © 2024 IEEE.

关键词： Multiple signal classification

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A SCIL-based Transformer for PolSAR image Classification

A SCIL-based Transformer for PolSAR Image Classification

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IET International radar Conference 2023, IRC 2023

作者： Xin, Xinyue Li, Ming Wu, Yan Zhang, Peng Xu, Dazhi Zheng, Jia The National Laboratory of Radar Signal Processing School of Electronic Engineering Xidian University Xi'an China The Remote Sensing Image Processing and Fusion Group School of Electronic Engineering Xidian University Xi'an China

ISBN: (纸本)9781839539954

Transformer has been applied for polarimetric synthetic aperture radar (PolSAR) image processing due to its ability to construct long-range dependency. However, Transformer lacks the learning of local spatial information within samples and correlation information among samples, which limits its performance in practical tasks. To address these issues, this paper proposes a novel Transformer structure based on spatial information learning and correlation information learning (SCIL-based Transformer) for PolSAR image classification. Firstly, a global-local spatial information learning (GL-SIL) module is constructed to fully extract the global-local spatial information within samples. Secondly, a correlation information learning (CIL) module is designed to achieve the learning of correlation information among samples by modeling the dependency across samples. Compared to the self-attention network in Transformer, the above two modules not only add the learning of local spatial information within samples and the correlation information among samples, but also greatly reduce the network complexity. Experiments on two real PolSAR datasets have shown that the proposed SCIL-based Transformer has better performance than other state-of-the-art algorithms. © The Institution of Engineering & Technology 2023.

关键词： Contrastive Learning

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Iterative Music for Single-Look Sar Tomography

Iterative Music for Single-Look Sar Tomography

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Geoscience and Remote Sensing Symposium (M2GARSS), Mediterranean and Middle-East

作者： Nabil Haddad Lekhmissi Harkati Karima Hadj-Rabah Azzedine Bouaraba Ammar Mesloub Ecole Militaire Polytechnique Antennas and Microwave Devices Laboratory Algeria Ecole Militaire Polytechnique Radar Laboratory Algeria University of Bab Ezzouar Image Processing and Radiation Laboratory Algeria

ISBN: (数字)9798350358582

ISBN: (纸本)9798350358599

关键词： Interpolation Smoothing methods Pattern classification Tomography Numerical simulation Iterative algorithms Multiple signal classification

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Recognition of Active Deception Jamming for LFM radar Based on Hyperdimensional Computing 3

Recognition of Active Deception Jamming for LFM Radar Based ...

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2022 3rd International Conference on Electrical, Electronic Information and Communication Engineering, EEICE 2022

作者： Dong, Hao Wang, Yan Hu, Wen Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China

Active deception jamming is one of the common means to jam radar signals. How to effectively recognize active deception jamming is a challenge of modern radar technology. To address the accuracy and real-time of radar active jamming recognition in practical applications, we propose a fast recognition method of radar active deception jamming based on hyperdimensional computing. First, to address the high feature dimensions, we use the sparse representation of time-frequency diagrams as the input, which time-frequency diagrams are constructed by the jamming plus echo signals in multiple consecutive pulse periods. Second, to achieve a high accuracy and real time of recognition, we employ the hyperdimensional computing to map the input to hyperdimensional space for training and recognition. Last, in the recognition stage, the Hamming distance is adopted to measure the similarity of samples. We validate our method on nine types of radar active deception jamming. The experimental results show that compared with the existing mainstream machine learning-based methods, the proposed method greatly shortens the training time on the premise of maintaining a high recognition accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Hamming distance

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Fast Node Selection of Networked radar Based on Transfer Reinforcement Learning 5th

Fast Node Selection of Networked Radar Based on Transfer R...

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5th IFIP TC 12 International Conference on Intelligence Science, ICIS 2022

作者： Cao, Yanjun Wang, Yuan Guo, Jingjing Han, Li Zhang, Chao Zhu, Jin Zhang, Tianyang Zhang, Xiangrong The 54th Research Institute of China Electronics Technology Group Corporation Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Shaanxi Xian710071 China National Laboratory of Radar Signal Processing Xidian University Xian710071 China

ISBN: (纸本)9783031149023

The networked radar system can synthesize different echo signals received by various radars and realize the cooperative detection of multiple radars, becoming more and more critical for data fusion sharing and network collaboration. However, due to the large number and wide range of nodes in the networked radar system, there exists a redundancy problem in radar node assignment, which causes additional resource consumption and slows down the task execution speed of radar node selection. To solve the above problem, this paper proposes a fast radar node selection method based on transfer reinforcement learning to quickly select the optimal and minimum node resources. The proposed method devises a novel reward function for the Monte Carlo Tree and a different termination state of iteration to select the minimize the number of radar nodes. In order to further accelerate the selection of radar nodes, transfer reinforcement learning is presented to fully leverage the previous knowledge. Experimental results show that our proposed method can quickly select the optimal and minimum radar nodes in a brief period, significantly improving the speed of radar node selection in the networked radar. © 2022, IFIP International Federation for Information processing.

关键词： Reinforcement learning

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A SCIL-based transformer for PolSAR image classification

A SCIL-based transformer for PolSAR image classification

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IET International radar Conference (IRC 2023)

作者： Xinyue Xin Ming Li Yan Wu Peng Zhang Dazhi Xu Jia Zheng National Laboratory of Radar Signal Processing School of Electronic Engineering Xidian University Xi'an People's Republic of China Remote Sensing Image Processing and Fusion Group School of Electronic Engineering Xidian University Xi'an People's Republic of China

ISBN: (数字)9781837240982

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Microwave Remote Sensing Soil Moisture Opportunities with the Future CIMR Mission

Microwave Remote Sensing Soil Moisture Opportunities with th...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： M. Piles M. Link R. Fernandez-Moran M. Baur T. Jagdhuber D. Entekhabi Image Processing Laboratory (IPL) Universitat de València Burjassot-Paterna Spain Dep. of Geography Cambridge University Cambridge UK German Aerospace Center Microwaves and Radar Institute Wessling Germany Institute of Geography University of Augsburg Augsburg Germany Dep. of Civil and Environmental Engineering Massachusetts Institute of Technology Cambridge USA

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The Copernicus Imaging Microwave Radiometer (CIMR) is a Copernicus Expansion Mission with an expected launch in 2028+. The satellite will carry a multi-frequency microwave radiometer operating in the L-, C-, X-, Ku-, and Ka-bands. In addition to providing L-band continuity from other missions (e.g., SMOS, SMAP), the CIMR mission brings new soil moisture remote sensing opportunities due to its multi-frequency, multi-resolution, and high temporal revisit characteristics. This study outlines a preliminary version of a soil moisture retrieval approach designed within CIMR preparatory activities. It aims to provide two soil moisture products: the first is based on the inversion of L-band brightness temperature measurements at their native resolution (<60 km). The second product is based on the inversion of enhanced resolution L-band measurements (<15 km) achieved through sharpening the L-band with higher resolution C/X-band measurements. In both cases, the soil moisture retrieval is based on the inversion of the zeroth-order tau-omega radiative transfer model. We present current efforts of algorithm development and performance evaluation based on simulated CIMR L1B data. The results presented here showcase the potential of CIMR to provide soil moisture estimates not only at hydroclimatological scales (<60 km, from L-band) but also at hydrometeorological scales (~10 to 25 km, from L-band, sharpened with C/X bands), meeting the needs of a wide range of science and applications.

关键词： Temperature measurement Performance evaluation Soil measurements Current measurement Soil moisture Satellite broadcasting Microwave radiometry

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Multi-frequency PolSAR image Fusion Classification Based on Semantic Interactive Information and Topological Structure

arXiv

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arXiv 2022年

作者： Cao, Yice Wu, Yan Li, Ming Zheng, Mingjie Zhang, Peng Wang, Jili The Remote Sensing Image Processing and Fusion Group School of Electronic Engineering Xidian University Xi’an710071 China The National Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China The Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

Compared with the rapid development of single-frequency multi-polarization SAR image classification technology, there is less research on the land cover classification of multifrequency polarimetric SAR (MF-PolSAR) images. In addition, the current deep learning methods for MF-PolSAR classification are mainly based on convolutional neural networks (CNNs), only local spatiality is considered but the nonlocal relationship is ignored. Therefore, based on semantic interaction and nonlocal topological structure, this paper proposes the MF semantics and topology fusion network (MF-STFnet) to improve MF-PolSAR classification performance. In MF-STFnet, two kinds of classification are implemented for each band, semantic information-based (SIC) and topological property-based (TPC). They work collaboratively during MF-STFnet training, which can not only fully leverage the complementarity of bands, but also combine local and nonlocal spatial information to improve the discrimination between different categories. For SIC, the designed cross-band interactive feature extraction module (CIFEM) is embedded to explicitly model the deep semantic correlation among bands, thereby leveraging the complementarity of bands to make ground objects more separable. For TPC, the graph sample and aggregate network (GraphSAGE) is employed to dynamically capture the representation of nonlocal topological relations between land cover categories. In this way, the robustness of classification can be further improved by combining nonlocal spatial information. Finally, an adaptive weighting fusion (AWF) strategy is proposed to merge inference from different bands, so as to make the MF joint classification decisions of SIC and TPC. The effectiveness of the proposed modules is proved by ablation experiments on three measured MF-PolSAR datasets. In addition, the comparative experiments show that MF-STFnet can achieve more competitive classification performance than some state-of-the-art methods. © 2022, CC

关键词： image classification

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Data Augmentation for Deep Learning Based a Utomaticmo Dulation Recognition

SSRN

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SSRN 2023年

作者： Ma, Hongbin Feng, Zhixi Yang, Shuyuan Wang, Min Wu, Tao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University Xi’an710071 China Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China Xidian University China Institute School of Artificial Intelligence Xidian University TaiBai road 2# Shannxi Province Xi’an China

Data augmentation, as a strategy to expand training datasets, can be utilized to improve the accuracy of Deep Learning(DL)-based models to a certain extent. However, existing data augmentation methods for the DL-based Automatic Modulation Recognition (AMR) task lack a theoretical basis, and the availability of these methods to different types of AMR models has not been evaluated. In this paper, we first model the transformation process from an information source to a modulation signal and analyze the parameters that influence the difference between modulation signals for the theoretical basis of data augmentation in the AMR field. Then we propose nine signal augmentation methods to generate argumentation signals by modifying the influence parameters in the amplitude domain, the time domain, and the frequency domain. Finally, we conduct a series of experiments on the public RML2016.10a and RML2016.10b datasets to evaluate the effectiveness of the proposed methods andanalyze the availability of these methods to Convolutional Neural Network (CNN)-based AMR models, and hybrid AMR models based on CNN and Recurrent Neural Network. © 2023, The Authors. All rights reserved.

关键词： Recurrent neural networks

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A feature fusion-net using deep spatial context encoder and nonstationary joint statistical model for high resolution SAR image classification

arXiv

引用

arXiv 2021年

作者： Liang, Wenkai Wu, Yan Li, Ming Zhang, Peng Cao, Yice Hu, Xin Remote Sensing Image Processing and Fusion Group School of Electronic Engineering Xidian University Xi'an710071 China National Key Laboratory of Radar Signal Processing Xidian University Xi'an710071 China

Convolutional neural networks (CNNs) have been applied to learn spatial features for high-resolution (HR) synthetic aperture radar (SAR) image classification. However, there has been little work on integrating the unique statistical distributions of SAR images which can reveal physical properties of terrain objects, into CNNs in a supervised feature learning framework. To address this problem, a novel end-to-end supervised classification method is proposed for HR SAR images by considering both spatial context and statistical features. First, to extract more effective spatial features from SAR images, a new deep spatial context encoder network (DSCEN) is proposed, which is a lightweight structure and can be effectively trained with a small number of samples. Meanwhile, to enhance the diversity of statistics, the nonstationary joint statistical model (NS-JSM) is adopted to form the global statistical features. Specifically, SAR images are transformed into the Gabor wavelet domain and the produced multi-subbands magnitudes and phases are modeled by the log-normal and uniform distribution. The covariance matrix is further utilized to capture the inter-scale and intrascale nonstationary correlation between the statistical subbands and make the joint statistical features more compact and distinguishable. Considering complementary advantages, a feature fusion network (Fusion-Net) base on group compression and smooth normalization is constructed to embed the statistical features into the spatial features and optimize the fusion feature representation. As a result, our model can learn the discriminative features and improve the final classification performance. Experiments on four HR SAR images validate the superiority of the proposed method over other related algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： image classification

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