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arXiv 2023年

作者： Feng, Hao Zhou, keyi Zhou, Wengang Yin, Yufei Deng, Jiajun Sun, Qi Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China Zhangjiang Laboratory Shanghai China University of Adelaide Australian Institute for Machine Learning Australia

Contour-based instance segmentation has been actively studied, thanks to its flexibility and elegance in processing visual objects within complex backgrounds. In this work, we propose a novel deep network architecture, i.e., PolySnake, for generic contour-based instance segmentation. Motivated by the classic Snake algorithm, the proposed PolySnake achieves superior and robust segmentation performance with an iterative and progressive contour refinement strategy. Technically, PolySnake introduces a recurrent update operator to estimate the object contour iteratively. It maintains a single estimate of the contour that is progressively deformed toward the object boundary. At each iteration, PolySnake builds a semantic-rich representation for the current contour and feeds it to the recurrent operator for further contour adjustment. Through the iterative refinements, the contour progressively converges to a stable status that tightly encloses the object instance. Beyond the scope of general instance segmentation, extensive experiments are conducted to validate the effectiveness and generalizability of our PolySnake in two additional specific task scenarios, including scene text detection and lane detection. The results demonstrate that the proposed PolySnake outperforms the existing advanced methods on several multiple prevalent benchmarks across the three tasks. The codes and pre-trained models are available at https://***/fh2019ustc/PolySnake Copyright © 2023, The Authors. All rights reserved.

关键词： Network architecture

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Deep Unrestricted Document Image Rectification

arXiv

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arXiv 2023年

作者： Feng, Hao Liu, Shaokai Deng, Jiajun Zhou, Wengang Li, Houqiang The CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China Zhangjiang Laboratory Shanghai China The University of Adelaide Australian Institute for Machine Learning Australia

In recent years, tremendous efforts have been made on document image rectification, but existing advanced algorithms are limited to processing restricted document images, i.e., the input images must incorporate a complete document. Once the captured image merely involves a local text region, its rectification quality is degraded and unsatisfactory. Our previously proposed DocTr, a transformer-assisted network for document image rectification, also suffers from this limitation. In this work, we present DocTr++, a novel unified framework for document image rectification, without any restrictions on the input distorted images. Our major technical improvements can be concluded in three aspects. Firstly, we upgrade the original architecture by adopting a hierarchical encoder-decoder structure for multi-scale representation extraction and parsing. Secondly, we reformulate the pixel-wise mapping relationship between the unrestricted distorted document images and the distortion-free counterparts. The obtained data is used to train our DocTr++ for unrestricted document image rectification. Thirdly, we contribute a real-world test set and metrics applicable for evaluating the rectification quality. To our best knowledge, this is the first learning-based method for the rectification of unrestricted document images. Extensive experiments are conducted, and the results demonstrate the effectiveness and superiority of our method. We hope our DocTr++ will serve as a strong baseline for generic document image rectification, prompting the further advancement and application of learning-based algorithms. The source code and the proposed dataset are publicly available at https://***/fh2019ustc/DocTr-Plus. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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An Improved Imaging Method for Highly-Squinted SAR Based on Hyper-Optimized Admm

An Improved Imaging Method for Highly-Squinted SAR Based on ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Tiancheng Chen Yang Meng Guoru Zhou Zhe Zhang Bingchen Zhang Yirong Wu Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

Highly-squinted synthetic aperture radar (SAR) echo has the characteristic of severe range-azimuth coupling, requiring specialized imaging algorithms. applications of compressed sensing in SAR imaging can effectively improve the resolution and other indicators. However, inaccurate manual parameters can affect the algorithm output. This article proposes an improved alternating direction method of multipliers (ADMM) for solving sparse reconstruction models under highly-squinted conditions. By adaptively adjusting the penalty parameter in ADMM via hyper-gradient descent (HD), the problem caused by inaccurate manual parameter is solved. Compared with matched filtering methods and other optimization methods, this method can suppress noise and speed up convergence. The effectiveness of the proposed method can be validated through the approximate observation of both simulated scenes and real scenes captured by the GF-3 SAR satellite.

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Plug-And-Play TomoSAR Imaging With Prior information

Plug-And-Play TomoSAR Imaging With Prior Information

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Kun Wang Jie Li Yizhe Fan Bingchen Zhang Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Synthetic aperture radar (SAR) tomography (TomoSAR) is an advanced remote sensing technology that has the ability to acquire three-dimensional information of targets. To enhance target features, different reaularization terms, including L 1 norm, total variation norm, and morphology term, have been introduced into the TomoSAR inversion model. However, there is a lack of attention to establishing a TomoSAR imaging framework with a modular structure that provides flexibility in specifically enhancing different features. In this article, a novel 3D tomographic reconstruction framework based on plug-and-play (PnP) priors and Alternating Direction Method of Multipliers (ADMM) is proposed. The PnP-ADMM framework achieves flexibility through the selection of appropriate priors for the features of targets, leading to a trade-off between the performance of feature enhancement and computational complexity. Simulation and real data experiments verify the effectiveness of the proposed method in flexibly selecting priors for feature enhancement of specific targets.

关键词： Radar remote sensing Three-dimensional displays Morphology Tomography Apertures Convex functions Computational complexity

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From multi-view to hollow-3D: Hallucinated hollow-3D R-CNN for 3D object detection

arXiv

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arXiv 2021年

作者： Deng, Jiajun Zhou, Wengang Zhang, Yanyong Li, Houqiang The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230026 China The Department of Computer Science University of Science and Technology of China Hefei230026 China

As an emerging data modal with precise distance sensing, LiDAR point clouds have been placed great expectations on 3D scene understanding. However, point clouds are always sparsely distributed in the 3D space, and with unstructured storage, which makes it difficult to represent them for effective 3D object detection. To this end, in this work, we regard point clouds as hollow-3D data and propose a new architecture, namely Hallucinated Hollow-3D R-CNN (H23D R-CNN), to address the problem of 3D object detection. In our approach, we first extract the multi-view features by sequentially projecting the point clouds into the perspective view and the bird-eye view. Then, we hallucinate the 3D representation by a novel bilaterally guided multi-view fusion block. Finally, the 3D objects are detected via a box refinement module with a novel Hierarchical Voxel RoI Pooling operation. The proposed H23D R-CNN provides a new angle to take full advantage of complementary information in the perspective view and the bird-eye view with an efficient framework. We evaluate our approach on the public KITTI Dataset and Waymo Open Dataset. Extensive experiments demonstrate the superiority of our method over the state-of-the-art algorithms with respect to both effectiveness and efficiency. The code will be made available at https://***/djiajunustc/H-23D_R-CNN. Copyright © 2021, The Authors. All rights reserved.

关键词： Object recognition

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application Design of Virtual Immersion Command information system

Application Design of Virtual Immersion Command Information ...

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International Conference on Big Data and information Analytics (BigDIA)

作者： Renjie Liu Hongli Chen Mingyang Wang Jian Zhu Zhenqian Zhang Ming Zhang Key Laboratory of Information System and Technology Beijing Institute of Control and Electronic Technology Beijing China Laboratory of Artificial Intelligence Integration Development and Security Application China Industrial Control Systems Cyber Emergency Response Team Beijing China

Based on the existing resources of general command information system, the virtually immersive display system is constructed. In this paper, the application design of virtual and augmented reality display system was discussed. The main contents included the overall system design, business requirements, application design of virtual reality (VR) and augmented reality (AR) table display system, expected results, and equipment selection. The deployment and application of VR and AR display system were introduced. Therefore, the hardware selection of VR and AR were HTC Vive and XLOOG X200 products, respectively. The Unreal Engine 4 was selected as the software development platform. This solution meets the current immersive display requirements. The system integrates VR and AR display application capabilities and can fully serve the command and control requirements in the future virtual field.

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SFDE-net: A Spatial-Frequency Domain Feature Enhancement Network for Cloud Detection

SFDE-net: A Spatial-Frequency Domain Feature Enhancement Net...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Baotong Su Siyan Li Wenguang Zheng Yao Chen The School of Computer Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences and Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Cloud detection is a crucial step in the preprocessing of satellite remote sensing images. Existing methods tend to have misjudgments when dealing with specific scenarios, such as challenges in distinguishing thin clouds from the background and addressing missing cloud boundaries. To solve this problem, we designed a novel Spatial–Frequency Domain Feature Enhancement Block (SFDE) embedded in a U-shaped network called SFDE-net. SFDE consists of three units: the Dual Frequency Feature Unit (DFF), the Spatial Domain Feature Unit (SDF), and the Cross-Domain Feature Fusion Unit (CDF). DFF globally learns the boundaries and overall structure of clouds in the frequency domain, SDF captures fine-grained information in the spatial domain, and CDF adaptively fuses features from both DFF and SDF. Our method’s effectiveness was evaluated on two public datasets, GF-1 WFV and LandSat8. Extensive experiments demonstrated that the proposed SFDE-net achieved accurate detection accuracy and outperformed several state-of-the-art methods.

关键词： Earth Accuracy Satellites Artificial satellites Fuses Frequency-domain analysis Merging

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A high-efficiency 240 W-level thin-rod Yb:YAG amplifier with dual-wavelength pumping method

A high-efficiency 240 W-level thin-rod Yb:YAG amplifier with...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Shang Wang Zhaojun Liu Zhenhua Cong Xingyu Zhang Zhigang Zhao Key Laboratory of Laser & Infrared System Ministry of Education Shandong University Qingdao China School of Information Science and Engineering Shandong University Qingdao China Shandong Provincial Key Laboratory of Laser Technology and Application Shandong University Qingdao China

A high-efficiency 240 W-level ultrashort pulse laser amplifier based on thin-rod Yb:YAG was obtained by adopting a dual-wavelength (940 nm and 969 nm) pumping method, along with a power extraction efficiency of ~51%.

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Power lasers Lasers and electrooptics Laser excitation Optical pulse generation Electrooptical waveguides Pump lasers

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Inferring decelerated land subsidence and groundwater storage dynamics in Tianjin–Langfang using Sentinel-1 InSAR

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International Journal of Digital Earth 2022年第1期15卷 1526-1546页

作者： Xuguo Shi Tongtong Zhu Wei Tang Mi Jiang Houjun Jiang Chen Yang Wei Zhan Zutao Ming Shaocheng Zhang School of Geography and Information Engineering China University of GeosciencesWuhanPeople’s Republic of China College of Geoscience and Surveying Engineering China University of Mining and TechnologyBeijingPeople’s Republic of China School of Geospatial Engineering and Science Sun Yat-Sen UniversityGuangzhouPeople’s Republic of China School of Civil Engineering Anhui Jianzhu UniversityHefeiPeople’s Republic of China Institute of Karst Geology CAGS/Key Laboratory of Karst DynamicsMNR&GZARGuilinPeople’s Republic of China The First Monitoring and Application Center China Earthquake AdministrationTianjinPeople’s Republic of China

To meet the growing demand for socioeconomic development,a large amount of groundwater is extracted from confined aquifers *** North China Plain has experienced considerable groundwater depletion and subsidence during the past six *** this study,we use Sentinel-1A/B SAR images from 2015 to 2020 to map the ground subsidence of the Tianjin–Langfang *** subsiding zones centered at Guangyang,Wuqing–Bazhou,and Jinghai are identified with maximum subsidence rates of 98.1,121.8,and 104.7 mm/*** and long-term signals are separated from time series subsidence and hydraulic measurements using continuous wavelet transform to retrieve aquifer *** long-term subsidence,which fits well with an exponential decaying model,remarkably slows down in our study *** elastic skeletal storage coefficients range between 0.52×10−3 and 9.66×10−*** then retrieve the spatial–temporal variations of total groundwater storage,recoverable groundwater storage,and irreversible groundwater *** storage depletion rates are apparently reducing,which benefits from the operation of the South-to-North Water Transfer Project and local groundwater management practices.

关键词： North China Plain aquifer parameter South-to-North water transfer project groundwater storage subsidence

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Current situation and method of dynamic monitoring of desertification in Hunshandake Sandy Land

Current situation and method of dynamic monitoring of desert...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Pingping Huang Xiangli Yang Yuhai Bao Shan Yin Wen Hong Inner Mongolian Key Laboratory of Radar Technology and Application Inner Mongolia University of Technology Hohhot China School of Electronic Information Wuhan University Wuhan China Inner Mongolian Key Laboratory of Remote Sensing and Geographic Information System Inner Mongolia Normal University Hohhot China National Key Laboratory of Science and Technology on Microwave Imaging Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509033331

Desertification of semi-arid grasslands is a serious problem for economic development and ecological preservation. Using the Hunshandake Sandy Lands as an example, we present an overview of monitoring of land desertification using two different data sources including TM and MODIS data. The driving mechanisms of Hunshandake Sandy Land desertification are also discussed.

关键词： MODIS Remote sensing Monitoring Indexes Vegetation mapping Satellites Earth

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