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检索条件"机构=Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems"

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Multi-scale Region-level Graph Convolutional Network for Unsupervised SAR image Change Detection

Multi-scale Region-level Graph Convolutional Network for Uns...

引用

2024 International Conference on Remote Sensing, Mapping, and Image processing, RSMIP 2024

作者： Zhu, Jingxing Liu, Rui Wang, Feng You, Hongjian Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781510680012

With the rapid advancement of synthetic aperture radar (SAR) sensors, it has become more important to extract change information between high-resolution SAR images. Considering the efficacy and robustness of segmentation-based strategy in solving fine-resolution images, as well as the excellent performance of deep learning in feature extraction, therefore, in this paper, we propose an unsupervised region-level graph convolutional network (URGCN) for SAR change detection. Firstly, we propose a multi-temporal joint segmentation method to generate multi-level regions. This method can simultaneously segment bi-temporal SAR images and automatically align bi-temporal regions during segmentation process. We then perform pre-classification on difference images generated by neighborhood ratio detector. The high confidence training samples are selected through hierarchical clustering. Thirdly, we encode the segmented regions are as a graph structure and construct GCN model. The graph can capture the spatial and temporal structure information. Finally, the GCN model parameters are optimized by labeled samples and inferring unlabeled regions. Experimental results on two VHR SAR change detection datasets demonstrated that the proposed method can extract complete change information with high accuracy compared to alternative approaches. © 2024 SPIE.

关键词： Change detection

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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...

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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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Electromagnetic Simulation Aided SAR Target Classification Via Deep Domain Adaptation

Electromagnetic Simulation Aided SAR Target Classification V...

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2021 CIE International Conference on Radar, Radar 2021

作者： Lv, Xiaoling Qiu, Xiaolan Yu, Wenming Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Suzhou Aerospace Information Research Institute Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Suzhou China Southeast University State Key Laboratory of Millimeter Waves Nanjing China

ISBN: (纸本)9781665498142

Convolutional neural networks (CNNs) have made tremendous success in optical images classification recently. However, in synthetic aperture radar (SAR) target classification, it is difficult to annotate a large amount of real SAR images to train CNNs. Sufficient annotated images can be easily obtained through simulation, but the disparity between the simulated images and the real images makes them difficult to directly apply to the real images classification. In this paper, we propose a model that integrates multi-kernel maximum mean discrepancy (MK-MMD) and domain-adversarial training to alleviate this problem. Simulated SAR images with annotation and unlabeled real SAR images are used to train our model. First, we use domain-adversarial training to prompt the model to extract domain-invariant features. Then, the MK-MMD between the hidden representations of simulated images and real images is reduced to narrow domain discrepancy. Experimental results on the real SAR dataset demonstrate that our method effectively solves the domain shift problem and improves the classification accuracy. © 2021 IEEE.

关键词： convolutional neural network (CNN) Synthetic aperture radar (SAR) target classification unsupervised domain adaptation (UDA)

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Improve spaceborne SAR geometric accuracy using DORIS

Improve spaceborne SAR geometric accuracy using DORIS

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IET International Radar Conference 2009

作者： Jiayin, Liu Wen, Hong Yueguan, Lin Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing 100190 China National Key Laboratory of Microwave Imaging Technology Institute of Electronics Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781849190107

The paper analyzes the effects of the orbit position errors in terms of the geometric accuracy of the space-borne SAR based on the range-Doppler method. The paper gives the simulation experiment results of the space-borne SAR geometric accuracy using DORIS, GPS and MADRAS. The results show the space-borne SAR geometric accuracy is 1 meter using DORIS, 80 meter higher than using MADRAS, and 50 meter higher than using GPS.

关键词： geometry

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A FEATURE ENHANCEMENT METHOD BASED ON THE SUB-APERTURE DECOMPOSITION FOR ROTATING FRAME SHIP DETECTION IN SAR IMAGES

A FEATURE ENHANCEMENT METHOD BASED ON THE SUB-APERTURE DECOM...

引用

2021 IEEE International geoscience and Remote Sensing Symposium, IGARSS 2021

作者： Lei, Songlin Qiu, Xiaolan Ding, Chibiao Lei, Shujie National Key Laboratory of Microwave Imaging Technology Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Shanghai Radio Equipment Research Institute Shanghai201100 China

ISBN: (纸本)9781665403696

Deep learning algorithms are widely used in SAR target detection. At present, most detection methods based on neural networks treat SAR images as optical images for processing, and do not fully exploit the characteristics of SAR images. In this paper, a method for feature enhancement using sub-aperture decomposition is proposed. Considering the advantages of the rotate box in eliminating interference, we conducted experiments on the rotating frame detection network Rotate RetinaNet using the Complex SAR images Rotation Ship Detection Dataset (CSRSDD). Compared with the base method, AP improves by 2.4% without bells and whistles. The results confirm the effectiveness of the proposed method. © 2021 IEEE

关键词： Neural networks Optical computing Object detection Optical fiber networks Optical detectors Optical imaging Radar polarimetry

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Saliency & structure preserving multi-operator image retargeting

Saliency & structure preserving multi-operator image retarge...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Lingling Zhu Zhibo Chen Xiaoming Chen Ning Liao University of Science and Technology of China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Hefei China

ISBN: (纸本)9781479999897

Content-aware image retargeting has attracted substantial research interests in the related research community. However, so far there is still no method can preserve important image contents and structure well without introducing deformation. To address this problem, we propose a Saliency & Structure Preserving Multi-operator (SSPM) method. SSPM classifies images into three categories utilizing SIFT density to improve performance of saliency preservation, helping to mitigate negative influence from center-bias property of most existing saliency detection models. SSPM also employs different principles to improve structure preservation performance, including Earth Mover's Distance (EMD) and Gray-Level Cooccurrence Matrix (GLCM) to get optimal operator sequences for smart content-aware image retargeting. SSPM method not only can well preserve salient contents and structure, but also can greatly improve deformation resilience. Experimental results demonstrated that our method outperforms state-of-art image retargeting methods.

关键词： Distortion Feature extraction Resilience Earth Loss measurement Visualization Visual perception

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Perceptual Evaluation of Pre-processing for Video Transcoding

Perceptual Evaluation of Pre-processing for Video Transcodin...

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IEEE Visual Communications and Image processing (VCIP)

作者： Shiyu Huang Ziyuan Luo Jiahua Xu Wei Zhou Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781728173221

Recently, the pre-processed video transcoding has attracted wide attention and has been increasingly used in practical applications for improving the perceptual experience and saving transmission resources. However, very few works have been conducted to evaluate the performance of pre-processing methods. In this paper, we select the source (SRC) videos and various pre-processing approaches to construct the first Pre-processed and Transcoded Video Database (PTVD). Then, we conduct the subjective experiment, showing that compared with the video sent to the codec directly at the same bitrate, the appropriate pre-processing methods indeed improve the perceptual quality. Finally, existing image/video quality metrics are evaluated on our database. The results indicate that the performance of the existing image/video quality assessment (IQA/VQA) approaches remain to be improved. We will make our database publicly available soon.

关键词： Measurement Codecs Databases Visual communication Image processing Buildings Bit rate

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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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AutoDerain: Memory-efficient Neural Architecture Search for Image Deraining

AutoDerain: Memory-efficient Neural Architecture Search for ...

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IEEE Visual Communications and Image processing (VCIP)

作者： Jun Fu Chen Hou Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781728173221

Learning-based image deraining methods have achieved remarkable success in the past few decades. Currently, most deraining architectures are developed by human experts, which is a laborious and error-prone process. In this paper, we present a study on employing neural architecture search (NAS) to automatically design deraining architectures, dubbed AutoDerain. Specifically, we first propose an U-shaped deraining architecture, which mainly consists of residual squeeze-and-excitation blocks (RSEBs). Then, we define a search space, where we search for the convolutional types and the use of the squeeze-and-excitation block. Considering that the differentiable architecture search is memory-intensive, we propose a memory-efficient differentiable architecture search scheme (MDARTS). In light of the success of training binary neural networks, MDARTS optimizes architecture parameters through the proximal gradient, which only consumes the same GPU memory as training a single deraining model. Experimental results demonstrate that the architecture designed by MDARTS is superior to manually designed derainers.

关键词： Training Visual communication Image processing Memory architecture Neural networks Graphics processing units

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Deep Multi-Scale Features Learning for Distorted Image Quality Assessment

Deep Multi-Scale Features Learning for Distorted Image Quali...

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IEEE International Symposium on Circuits and systems

作者： Wei Zhou Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

Image quality assessment (IQA) aims to estimate human perception based image visual quality. Although existing deep neural networks (DNNs) have shown significant effectiveness for tackling the IQA problem, it still needs to improve the DNN-based quality assessment models by exploiting efficient multi-scale features. In this paper, motivated by the human visual system (HVS) combining multi-scale features for perception, we propose to use pyramid features learning to build a DNN with hierarchical multi-scale features for distorted image quality prediction. Our model is based on both residual maps and distorted images in luminance domain, where the proposed network contains spatial pyramid pooling and feature pyramid from the network structure. Our proposed network is optimized in a deep end-to-end supervision manner. To validate the effectiveness of the proposed method, extensive experiments are conducted on four widely-used image quality assessment databases, demonstrating the superiority of our algorithm.

关键词： Image quality Visualization Databases Visual systems Streaming media Quality assessment Video recording

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