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7th International Conference on Geoinformatics in Sustainable Ecosystem and Society, GSES 2019, and 1st International Conference on geospatial Artificial Intelligence for Urban Computing, GeoAI 2019

作者： Zhang, Chen Zhou, Xia Li, Yong Yang, Ji Yang, Chuanxun Wen, Kaixiang Chen, Yuchan Yang, Libo Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangzhou510070 China Guangzhou511458 China State Grid Hunan Maintenance Company Hengyang421000 China

ISBN: (纸本)9789811561054

Hyperspectral remote sensing provides a highly efficient, convenient, and non-destructive technical means for the quantitative study of the dust retention content (DRC) of plants. Based on the common greening plants in Guangzhou, this study used hyperspectral technology to explore the spectral characteristics of different plant leaves under dust pollution, and used the Pearson correlation method to construct the DRC estimation models of plants. The results show that the spectral characteristics of the dust-retention effect of plants are mainly concentrated in the visible and near-infrared wavelengths (350–1360 nm), which are important band intervals for the study of spectral characteristics and the inversion band selection. The first derivative of spectral reflectance is more suitable for constructing the DRC estimation model than the spectral reflectance, and CF has a good fitting effect, and the deviation between the Model estimated DRC and the measured DRC is small, and its R2 is 0.55 and the RMSE is 0.300 g/m2. Through this research, the application of hyperspectral remote sensing technology in the dust retention effect research of plants can be effectively promoted, which can help to quickly and non-destructively evaluate the DRC of plants, and provide a scientific basis for rational selection of green plant types and urban air quality monitoring, and lay a foundation for large-scale DRC estimation. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Reflection

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A Fusion processing Method for Satellite Detection Data by Beidou Short Message system 12th

A Fusion Processing Method for Satellite Detection Data by B...

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12th China Satellite Navigation Conference, CSNC 2021

作者： Li, Bing Yu, Xiao Sun, Xiaojuan Hu, Yuxin Wu, Haiyan Shi, Tao Chen, Ling School of Cyber Security University of Chinese Academy of Sciences Beijing100149 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Beijing Institute of Tracking and Telecommunications Technology Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100149 China National Space Science Center Chinese Academy of Science Beijing100190 China

ISBN: (纸本)9789811631375

With the support of Beidou terminal of short message service system, the space science satellite can transmit all-day data that are astronomical alert data such as gravitational waves and Gamma ray bursts and satellite telemetry parameters. Then Beidou message service system has become an important means for finding the space astronomical events quickly, planning the follow-up observation and monitoring the real-time health of satellite and payloads. Then the application fields of the Beidou system has expanded into space science observation and satellite emergency operation. The short message data receiving and processing in ground system has many features of near real-time and continuity which is different from the processing methods of satellite data transmitted by data channel and telemetry channel. And the multi-source satellite data of the telemetry parameters and the astronomical alert information from these three channels need to be integrated. So, the real-time processing system of satellite data based on Beidou short message transmission has great challenges. In this paper, we explored an organization method of Beidou short messages, and proposed a multi-source data fusion algorithm for the ground satellite data processing system. The data processing system with this method was designed and implemented. Applying to the ground system of the satellite named gravitational wave high-energy electromagnetic counterpart all-sky monitor, the performance analysis of improved system shows that the processing delay for important telemetry parameters and astronomical alert information is greatly shortened from average nine hours to several minutes. The Beidou short message service system shows great supports in real time observation of gamma ray bursts. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Satellites

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Multi-Angular SAR Statistical Properties Analysis and Man-Made Target Detection

Multi-Angular SAR Statistical Properties Analysis and Man-Ma...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Fei Teng Yun Lin Yanping Wang Wenjie Shen Shanshan Feng Wen Hong Key Laboratory of Technology in Geospatial Information Processing and Application System Chinese Academy of Sciences Beijing China School of Electronic Information Engineering North China University of Technology Beijing China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

In conventional synthetic aperture radar (SAR) working mode, targets are assumed isotropic due to the limited aperture length. However, most of man-made targets are anisotropic. Therefore, the anisotropic scattering can help us do man-made target detection. Circular SAR (CSAR) [1] is a new SAR working mode and it can obtain the anisotropic scattering of the target by 360° observation. In this paper, the multi-angular statistical properties of targets are analyzed. The probability density functions (PDF) of the anisotropic target are various under different aspect viewing angles, while the PDFs of the isotropic target are basically stable. Then a man-made target detection method is proposed based on the multi-angular statistical property. Likelihood ratio test [2] is used to judge whether the statistical property of scattering is anisotropic or isotropic. Then anisotropic scatterings, which represent the man-made targets, can be discriminated from isotropic scatterings by thresholding. An X-band chamber circular SAR data and a C-band airborne circular SAR data are used to illustrated our idea.

关键词： Scattering Radar polarimetry Synthetic aperture radar Object detection Apertures Fitting Buildings

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TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network

arXiv

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arXiv 2022年

作者： Wang, Muhan Zhang, Zhe Wang, Yue Gao, Silin 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 School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Electrical and Computer Engineering Department George Mason University FairfaxVA22030 United States

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA algorithm are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

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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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Satellite-Based Water Depth Estimation: A Review 7th

Satellite-Based Water Depth Estimation: A Review

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7th International Conference on Geoinformatics in Sustainable Ecosystem and Society, GSES 2019, and 1st International Conference on geospatial Artificial Intelligence for Urban Computing, GeoAI 2019

作者： Wen, Kaixiang Li, Yong Wang, Hua Jing, Wenlong Yang, Ji Zhang, Chen Wang, Zhou Guangdong University of Technology Guangzhou510070 China Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangzhou510070 China State Grid Panzhihua Power Supply Company Panzhihua617000 China

ISBN: (纸本)9789811561054

This paper reviews the advances in water depth estimations from satellite images. According to the sensor type, current satellite-based water depth estimations are divided into two categories: optical-based and SAR-based (synthetic aperture radar) methods. By analyzing and discussing on the advantages and disadvantages of various methods. The following points can be summarized: First, the accuracy of optical remote sensing water depth detection method is higher than that of SAR-based models. Besides, optical-based method is simpler and less restricted than the SAR-based methods. Second, hyperspectral-based approach performed better than the multispectral optical remote sensing approaches. Since hyperspectral images provide richer spectral information than multispectral, which significantly matter in the water depth detection. Third, remote sensing water depth detection method is the best way to obtain the underwater topographic features of the inaccessible waters. Finally, on the basis of summarizing the previous water depth estimation methods, the development prospects of shallow seawater deep inversion technology are discussed and analyzed. For example, the establishment of an inversion model by fusing multiple remote sensing data, and the use of artificial intelligence technology for water depth inversion. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Synthetic aperture radar

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3-D context entropy model for improved practical image compression

arXiv

引用

arXiv 2020年

作者： Guo, Zongyu Wu, Yaojun Feng, Runsen Zhang, Zhizheng Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

In this paper, we present our image compression framework designed for CLIC 2020 competition. Our method is based on Variational AutoEncoder (VAE) architecture which is strengthened with residual structures. In short, we make three noteworthy improvements here. First, we propose a 3-D context entropy model which can take advantage of known latent representation in current spatial locations for better entropy estimation. Second, a light-weighted residual structure is adopted for feature learning during entropy estimation. Finally, an effective training strategy is introduced for practical adaptation with different resolutions. Experiment results indicate our image compression method achieves 0.9775 MS-SSIM on CLIC validation set and 0.9809 MS-SSIM on test set. Copyright © 2020, The Authors. All rights reserved.

关键词： Image compression

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Learned Video Compression with Feature-level Residuals

Learned Video Compression with Feature-level Residuals

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Runsen Feng Yaojun Wu Zongyu Guo Zhizheng Zhang Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

In this paper, we present an end-to-end video compression network for P-frame challenge on CLIC. We focus on deep neural network (DNN) based video compression, and improve the current frameworks from three aspects. First, we notice that pixel space residuals is sensitive to the prediction errors of optical flow based motion compensation. To suppress the relative influence, we propose to compress the residuals of image feature rather than the residuals of image pixels. Furthermore, we combine the advantages of both pixel-level and feature-level residual compression methods by model ensembling. Finally, we propose a step-by-step training strategy to improve the training efficiency of the whole framework. Experiment results indicate that our proposed method achieves 0.9968 MS-SSIM on CLIC validation set and 0.9967 MS-SSIM on test set.

关键词： Image coding Training Optical imaging Adaptive optics Motion compensation Optical distortion Decoding

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Vibration Error Compensation Imaging Algorithm for Arc Array SAR Besed on Parameter Estimation 6

Vibration Error Compensation Imaging Algorithm for Arc Array...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Dong, Yifan Wu, Congfei Xu, Wei Huang, Pingping Tan, Weixian Hong, Wen College of Information Engineering Inner Mongolia University of Technology China Inner Mongolia Key Laboratory of Radar Technology and Application Hohhot China Institute of Electronics Chinese Academy of Sciences China Key Laboratory of Technology in Geospatial Information Processing and Application System Beijing China

ISBN: (纸本)9781728129129

Arc array synthetic aperture radar (SAR) is a novel array imaging system for wide-area observation, with wide observation range and high resolution. Arc array synthetic aperture radar uses W-band as carrier signal, and W-band wavelength is short. The small high-frequency vibration of the helicopter-borne platform will cause significant changes in the phase of the echo signal, which will seriously deteriorate the imaging performance of arc array synthetic aperture radar. Based on the high order approximate imaging algorithm of arc array radar, this paper proposes a vibration phase error compensation imaging algorithm based on Short-time Fourier transform (STFT) parameter estimation. The algorithm uses the high-order approximation imaging algorithm of curved array radar to perform high-order approximation of the slant range model, compensates the range cell migration (RCM) and range azimuth coupling in the two-dimensional frequency domain, and finally realizes the vibration error compensation in the range Doppler domain. The simulation results of the lattice target verify the effectiveness of the imaging method. © 2019 IEEE.

关键词： Synthetic aperture radar

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Learned video compression with feature-level residuals

arXiv

引用

arXiv 2020年

作者： Feng, Runsen Wu, Yaojun Guo, Zongyu Zhang, Zhizheng Jin, Xin Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

关键词： Deep neural networks

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