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Analysis of phase preservation and interferometric offset test in sparse SAR imaging

Science China(information Sciences) 2024年第2期67卷 219-231页

作者： Zhongqiu XU Bingchen ZHANG Guangzuo LI Xueli ZHAN Yanfei BAO Yirong WU Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of Sciences PLA Academy of Military Sciences

Sparse synthetic aperture radar(SAR) imaging has emerged as a reliable microwave imaging scheme in the recent decade and excels in down-sampling reconstruction and full-sampling performance improvements such as noise, sidelobe, speckle, and ambiguity suppression. To utilize complex image products of sparse reconstruction for improvement in polarimetric, interferometric, and tomographic SAR imaging, it is necessary to evaluate the phase preservation of sparse SAR imaging. In this study, we first introduce the general alternating direction method of multipliers(ADMM) as the universal framework for sparse reconstruction algorithms and adopt chirp scaling algorithm(CSA)-based azimuth-range decouple operators to avoid expensive data storage and processing. Further, we theoretically analyze the phase preservation of the sparse reconstruction algorithm through a comparison with the reconstruction results of CSA. Finally,we conduct the interferometric offset test on the sparse reconstruction results of simulated and real Gaofen-3(GF-3) SAR data, demonstrating the phase-preserving ability of sparse methods.

关键词： sparse SAR imaging phase preservation interferometric offset test general ADMM azimuth-range decouple

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High-Resolution Urban Flows Forecasting With Coarse-Grained Spatiotemporal Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 315-327页

作者： Xu, Zhenyi Kang, Yu Cao, Yang Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Department of Automation University of Science and Technology of China Hefei230026 China Chinese Academy of Sciences Key Laboratory of Technology in GeoSpatial Information Processing and Application System Beijing100192 China

Urban flow monitoring and forecasting systems play important roles in smart city management. However, due to the long-lasting and enormous deployment cost of ubiquitous traffic monitoring devices (e.g., loop detectors, traffic video detection), it is very difficult to predict flow in high-resolution (HR) with limited monitoring devices. The existing spatiotemporal network based methods usually predict the urban flow in the same spatial scale without considering the spatial correlations between coarse-grained and fine-grained urban flows. To tackle these issues, we propose a HR spatiotemporal transformer network (HRSTT) to predict fine-grained urban flow. Specifically, residual convolution units are employed to constructs the high-level features of three-view temporal (e.g., closeness, period, and trend) flow data. Then, a transformer block is designed to jointly learn the spatiotemporal dynamic features of each temporal flow with self-attention mechanism. For the external factors (e.g., holidays, weather conditions) are extracted by embedding dense networks, which are fused with high level coarse-grained flow feature maps with gated-fusion scheme. Finally, the coarse-grained fusion feature maps are transferred to the distributional upsampling module, generating the fined-grained flow map of target predicted time. Furthermore, the proposed model is evaluated with several baselines on real-world TaxiBJ datasets, demonstrating the state-of-the-art performance of our approach on the fine-grained urban flow forecasting problem. © 2020 IEEE.

关键词： Deep neural networks

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AUTOMATIC MODULATION RECOGNITION BASED ON SST AND DEEP LEARNING MODEL

AUTOMATIC MODULATION RECOGNITION BASED ON SST AND DEEP LEARN...

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IET International Radar Conference 2023, IRC 2023

作者： Xiang, Jianbing Lv, Xiaolei Han, Bing Zhong, Lihua Fang, Linlin Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in GeoSpatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781839539954

As the electromagnetic environment in battlefields becomes increasingly complex, automatic modulation recognition for noncooperation radiation source signal is becoming vital and challenging. Most previous works were more likely to cause low recognition accuracy with high computational complexity in low signal-to-noise ratio (SNR), and only considered spatial or temporal attributes of signal which ignores the feature interaction. In this paper, we firstly apply synchrosqueezing transform (SST) to extract high resolution and robustness time-frequency feature as one of signal representation, and convert the signal into the in-phase/quadrature (I/Q) components as another signal representation, which enhances the diversity of signal representation. Then, we propose a deep learning model to achieve automatic recognition, which has two streams and each stream consists of the convolutional neural network (CNN), bistatic long short-term memory (BiLSTM) and temporal attention (TAT). It efficiently explores the feature from each signal representation and enhances the key features by the introduction of TAT, furthermore, it implements feature interaction to form a new feature matrix. Finally, experiments and analysis were conducted using the RadioML2016.10a dataset. The experimental results showed that compared to other algorithms, this method has higher robustness and perform better recognition accuracy under low SNR. © The Institution of Engineering & technology 2023.

关键词： Convolutional neural networks

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Detection and analysis of landslide geomorphology based on UAV vertical photogrammetry

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Journal of Mountain Science 2024年第4期21卷 1190-1214页

作者： BI Rui GAN Shu YUAN Xiping LI Kun LI Raobo LUO Weidong CHEN Cheng GAO Sha HU Lin ZHU Zhifu School of Land and Resources Engineering Kunming University of Science and TechnologyKunming 650093China Plication Engineering Research Center of Spatial Information Surveying and Mapping Technology in Plateau and Mountainous Areas Set by Universities in Yunnan Province Kunming 650093China West Yunnan University of Applied Sciences Key Laboratory of Mountain Real Scene Point Cloud Data Processing and Application for Universities in Yunnan ProvinceDali 671006China

High-resolution landslide images are required for detailed geomorphological analysis in complex topographic environment with steep and vertical landslide *** study proposed a vertical route planning method for unmanned aerial vehicles(UAVs),which could achieve rapid image collection based on strictly calculated route *** effectiveness of this method was verified using a DJI Mavic 2 Pro,obtaining high-resolution landslide images within the Dongchuan debris flow gully,in the Xiaojiang River Basin,Dongchuan District,Yunnan,China.A three-dimensional(3D)model was constructed by the structure-from-motion and multi-view stereo(SfM-MVS).Micro-geomorphic features were analyzed through visual interpretation,geographic information system(GIS),spatial analysis,and mathematical statistics *** results demonstrated that the proposed method could obtain comprehensive vertical information on landslides while improving measurement *** 3D model was constructed using the vertically oriented flight route to achieve centimeter-level accuracy(horizontal accuracy better than 6 cm,elevation accuracy better than 3 cm,and relative accuracy better than 3.5 cm).The UAV technology could further help understand the micro internal spatial and structural characteristics of landslides,facilitating intuitive acquisition of surface *** slope of landslide clusters ranged from 36°to 72°,with the majority of the slope facing east and *** elevation levels were relatively consistent while middle to lower elevation levels gradually decreased from left to right with significant variations in lower elevation *** the rainy season,surface runoff was abundant,and steep topography exacerbated changes in surface *** route method is suitable for unmanned aerial vehicle(UAV)landslide surveys in complex mountainous *** geomorphological analysis methods used will provide references for identifying and describing topographic

关键词： UAV Landslide Vertical route SfM-MVS Topographic features

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DEEP UNFOLDING NETWORK FOR SPARSE SAR IMAGING BASED ON COMPOUND REGULARIZATION

DEEP UNFOLDING NETWORK FOR SPARSE SAR IMAGING BASED ON COMPO...

引用

IET International Radar Conference 2023, IRC 2023

作者： Zhou, Guoru Xu, Zhongqiu Fan, Yizhe Zhang, Zhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781839539954

How to get an accurate image of multi-class targets scene is the key to synthetic aperture radar (SAR) imaging research. In sparse SAR imaging, compound regularization constrains multi-class target features by setting multiple regularizations prior to the model. However, the solution method based on a traditional iterative algorithm needs to set parameters manually and has a large number of iterations. In this paper, a novel SAR imaging method for the l1 norm and the total variation (TV) compound regularization based on a deep unfolding network (DUN) is proposed. The l1 norm and TV compound regularization can promote the sparsity and despeckle at the same time. Besides, the deep unfolded network can learn iteration parameters in a data-driven manner to speed up convergence. Finally, real SAR data experiments can verify the effectiveness of the proposed method. © The Institution of Engineering & technology 2023.

关键词： Radar imaging

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iWavePro: An improved framework for iWave++

iWavePro: An improved framework for iWave++

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Dongmei Xue Cunhui Dong Fan Ye Hang Chen Bowei Kang Li Li Dong Liu CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China

End-to-end image coding methods based on wavelet-like transform have made great progress in recent years. The most advanced one is iWave++, which adopts multi-level lifting schemes based on convolutional neural networks. However, iWave++ still has many unresolved problems. First, the independent entropy coding of each component makes it impossible to use the correlation between components better. Secondly, additive wavelet transform limits the nonlinear ability of learnable wavelet transform. Moreover, the offline training strategy makes the iWave++ unable to adjust according to the content. In this paper, we propose an improved framework for iWave++ called iWave-Pro. iWavePro is designed with several techniques to overcome the problems mentioned above. These techniques are the joint multi-component Gaussian mixture entropy coding, the affine wavelet-like transform, and the online training. Experimental results show that our method can save 10.73% bit rate compared with iWave++ at the same quality.

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Padding-Aware Learned Image Compression

Padding-Aware Learned Image Compression

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Haotian Zhang Junqi Liao Yiheng Jiang Li Li Dong Liu CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China

For current learned image compression methods, padding input images is necessary to meet the resolution requirements of down-sampling layers. However, the impact of padding has not been studied thoroughly. Most previous studies ignore padded images in the training process. In this paper, we analyze the impact of padding on compression performance. Then, we propose a padding-aware training (PAT) strategy, handling the padding effect during the training. Specifically, our PAT strategy calculates the loss of pre-padding image through a masking operation. Finally, according to our systematic experimental results, we find that images with different resolutions tend to favor different padding modes. Therefore, we further propose to conduct padding mode decision in the encoding process for rate-distortion optimization. Experiments demonstrate that our proposed PAT strategy and padding mode decision effectively compensate for the performance drop caused by padding.

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FreqAlign: Excavating Perception-oriented Transferability for Blind Image Quality Assessment from A Frequency Perspective

arXiv

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arXiv 2023年

作者： Li, Xin Lu, Yiting Chen, Zhibo The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Blind Image Quality Assessment (BIQA) is susceptible to poor transferability when the distribution shift occurs, e.g., from synthesis degradation to authentic degradation. To mitigate this, some studies have attempted to design unsupervised domain adaptation (UDA) based schemes for BIQA, which intends to eliminate the domain shift through adversarial-based feature alignment. However, the feature alignment is usually taken at the low-frequency space of features since the global average pooling operation. This ignores the transferable perception knowledge in other frequency components and causes the sub-optimal solution for the UDA of BIQA. To overcome this, from a novel frequency perspective, we propose an effective alignment strategy, i.e., Frequency Alignment (dubbed FreqAlign), to excavate the perception-oriented transferability of BIQA in the frequency space. Concretely, we study what frequency components of features are more proper for perception-oriented alignment. Based on this, we propose to improve the perception-oriented transferability of BIQA by performing feature frequency decomposition and selecting the frequency components that contained the most transferable perception knowledge for alignment. To achieve a stable and effective frequency selection, we further propose the frequency movement with a sliding window to find the optimal frequencies for alignment, which is composed of three strategies, i.e., warm up with pre-training, frequency movement-based selection, and perturbation-based finetuning. Extensive experiments under different domain adaptation settings of BIQA have validated the effectiveness of our proposed method. The code will be released at https://***/lixinustc/Openworld-IQA. Copyright © 2023, The Authors. All rights reserved.

关键词： Alignment

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VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Vision

arXiv

引用

arXiv 2023年

作者： Sheng, Xihua Li, Li Liu, Dong Li, Houqiang The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Almost all digital videos are coded into compact representations before being transmitted. Such compact representations need to be decoded back to pixels before being displayed to humans and – as usual – before being enhanced/analyzed by machine vision algorithms. Intuitively, it is more efficient to enhance/analyze the coded representations directly without decoding them into pixels. Therefore, we propose a versatile neural video coding (VNVC) framework, which targets learning compact representations to support both reconstruction and direct enhancement/analysis, thereby being versatile for both human and machine vision. Our VNVC framework has a feature-based compression loop. In the loop, one frame is encoded into compact representations and decoded to an intermediate feature that is obtained before performing reconstruction. The intermediate feature can be used as reference in motion compensation and motion estimation through feature-based temporal context mining and cross-domain motion encoder-decoder to compress the following frames. The intermediate feature is directly fed into video reconstruction, video enhancement, and video analysis networks to evaluate its effectiveness. The evaluation shows that our framework with the intermediate feature achieves high compression efficiency for video reconstruction and satisfactory task performances with lower complexities. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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AN AUTOFOCUS NETWORK FOR MULTI-CHANNEL PHASE ERRORS WITH application TO TOMOSAR IMAGING

AN AUTOFOCUS NETWORK FOR MULTI-CHANNEL PHASE ERRORS WITH APP...

引用

IET International Radar Conference 2023, IRC 2023

作者： Wang, Muhan Gao, Silin Zhang, Zhe Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781839539954

Synthetic aperture radar (SAR) tomography (TomoSAR) has garnered significant attention due to its capability for three-dimensional reconstruction. Compressed sensing (CS) methods are widely employed to address the TomoSAR inversion challenge. Nevertheless, practical applications reveal phase errors among different channels, resulting in defocusing and blurring when relying solely on CS for 3D reconstruction. Current state-of-the-art autofocus techniques suffer from prohibitive computational complexity, limiting their applicability to large-scale 3D imaging. In pursuit of efficient TomoSAR 3-D autofocusing, we proposed ASAMP-Net, an innovative deep unfolding network. Operating within a two-step framework, each layer comprises two stages: phase error estimation and iterative scattering coefficient reconstruction using the sparse adaptive matching pursuit (SAMP) algorithm. Additionally, phase error estimation is obtained through mathematical derivation, while challenges associated with fixed sparsity and limited efficiency in conventional methods are mitigated through deep learning techniques. Simulation experiments and real data validation affirm the effectiveness and superiority of the proposed method. © The Institution of Engineering & technology 2023.

关键词： Radar imaging

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