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2009 Joint Urban Remote Sensing Event

作者： Hui, Long Kun, Fu Chuanzhao, Han Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing China Beijing Remote Sensing Institute Beijing China

ISBN: (纸本)9781424434619

In this paper, we propose a novel procedure of detecting control points from SAR image based on Optical Image Patches procedure. The method presented here is a three-step procedure. In the first stage, a local image patch from the SAR image is estimated from the geo-information of the SAR image and the GCP geo-coordinate, and the local patch is performed an affine transformation by using the four geo-coordinates of the SAR image in advance. In the second stage, the NCC procedure is performed on the GCP and the preprocessed local SAR image patch to find the candidate image control point(ICP). In the third stage, the pairs of ICP and GCP are refined, and the false pairs are discarded by the RANSAC algorithm. Experiments on SAR-Optical satellite images demonstrate the reliability and effectiveness of the presented method. ©2009 IEEE.

关键词： Synthetic aperture radar

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REVERSE-RANGE-DOPPLER METHOD FOR AUTOMATED GEOCODING SAR IMAGES

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Journal of Electronics(China) 2012年第3期29卷 242-247页

作者： Liu Jiayin Hong Wen Key Laboratory of Technology on Geo-spatial Information Processing and Application System Chinese Academy of SciencesBeijing 100190China National Key Laboratory of Microwave Imaging Technology Beijing 100190China Institute of Electronics Chinese Academy of SciencesBeijing 1001q0China

Range-Doppler (RD) method and Reverse-Range-Doppler (RRD) method are combined together to achieve automatic geocoding of Synthetic Aperture Radar (SAR) image quickly and accurately in the paper. The RD method is firstly used to locate the four corners of the image, then the other pixels of the image can be located by Reverse-Range-Doppler (RRD) method. Resampling is performed at last. The approach has an advantage over previous techniques in that it does not require ground control points and is independent of spacecraft attitude knowledge or control. It can compensate the shift due to the assumed Doppler frequency in SAR image preprocessing. RRD simplifies the process of RD, therefore speeds up the computation. The experimental results show that a SAR image can be automated geocoded in 30 s using the single CPU (3 GHz) with 1 G memory and an accuracy of 10 m is attainable with this method.

关键词： Range-Doppler (RD) Reverse-Range-Doppler (RRD) Geolocation Automated geocoding Space-borne Synthetic Aperture Radar (SAR)

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A novel and fast corner detection method for SAR imagery

A novel and fast corner detection method for SAR imagery

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ISPRS geospatial Week 2017

作者： Jiao, Niangang Kang, Wenchao Xiang, Yuming You, Hongjian Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Corners play an important role on image processing, while it is difficult to detect reliable and repeatable corners in SAR images due to the complex property of SAR sensors. In this paper, we propose a fast and novel corner detection method for SAR imagery. First, a local processing window is constructed for each point. We use the local mean of a 3 × 3 mask to represent a single point, which is weighted by a Gaussian template. Then the candidate point is compared with 16 surrounding points in the processing window. Considering the multiplicative property of speckle noise, the similarity measure between the center point and the surrounding points is calculated by the ratio of their local means. If there exist more than M continuous points are different from the center point, then the candidate point is labelled as a corner point. Finally, a selection strategy is implemented by ranking the corner score and employing the non-maxima suppression method. Extreme situations such as isolated bright points are also removed. Experimental results on both simulated and real-world SAR images show that the proposed detector has a high repeatability and a low localization error, compared with other state-of-the-art detectors.

关键词： Synthetic aperture radar

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Automatic coregistration for multiview SAR images in urban areas

Automatic coregistration for multiview SAR images in urban a...

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ISPRS geospatial Week 2017

作者： Xiang, Yuming Kang, Wenchao Wang, Feng You, Hongjian Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Due to the high resolution property and the side-looking mechanism of SAR sensors, complex buildings structures make the registration of SAR images in urban areas becomes very hard. In order to solve the problem, an automatic and robust coregistration approach for multiview high resolution SAR images is proposed in the paper, which consists of three main modules. First, both the reference image and the sensed image are segmented into two parts, urban areas and nonurban areas. Urban areas caused by double or multiple scattering in a SAR image have a tendency to show higher local mean and local variance values compared with general homogeneous regions due to the complex structural information. Based on this criterion, building areas are extracted. After obtaining the target regions, L-shape structures are detected using the SAR phase congruency model and Hough transform. The double bounce scatterings formed by wall and ground are shown as strong L- or T-shapes, which are usually taken as the most reliable indicator for building detection. According to the assumption that buildings are rectangular and flat models, planimetric buildings are delineated using the L-shapes, then the reconstructed target areas are obtained. For the orignal areas and the reconstructed target areas, the SAR-SIFT matching algorithm is implemented. Finally, correct corresponding points are extracted by the fast sample consensus (FSC) and the transformation model is also derived. The experimental results on a pair of multiview TerraSAR images with 1-m resolution show that the proposed approach gives a robust and precise registration performance, compared with the orignal SAR-SIFT method.

关键词： Synthetic aperture radar

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Propagation and Mechanisms of the Quasi-Biweekly Oscillation over the Asian Summer Monsoon Region

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Journal of Meteorological Research 2017年第2期31卷 321-335页

作者： Meirong WANG Jun WANG Anmin DUAN Center of Data Assimilation for Research and Application/Key Laboratory of Meteorological Disaster of Ministry of Education Nanjing University of Information Science & TechnologyNanjing 210044 Plateau Atmosphere and Environment Key Laboratory of Sichuan Province Chengdu 610225 International Institute for Earth System Science Nanjing UniversityNanjing 210023 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029

The propagation and underlying mechanisms of the boreal summer quasi-biweekly oscillation（QBWO）over the entire Asian monsoon region are investigated,based on ECMWF Interim reanalysis（ERA-Interim）data,GPCP precipitation data,and an atmospheric general circulation model（AGCM）.Statistical analyses indicate that the QBWO over the Asian monsoon region derives its main origin from the equatorial western Pacific and moves northwestward to the Bay of Bengal and northern India,and then northward to the Tibetan Plateau（TP）area,with a baroclinic vertical *** propagation of the QBWO is promoted by three main mechanisms：barotropic vorticity,boundary moisture advection,and surface sensible heating（SSH）.It is dominated by the barotropic vorticity effect when the QBWO signals are situated to the south of 20°*** the propagation taking place farther north toward the TP,the boundary moisture advection and SSH are the leading *** use an AGCM to verify the importance of SSH on the northward propagation of the *** simulations confirm the diagnostic conclusion that the equatorial western Pacific is the source of the ***,the model can accurately simulate the propagation pathway of the QBWO signals over the Asian monsoon ***,sensitivity experiments demonstrate that the SSH over northern India and the southern slope of the TP greatly contributes to the northward propagation of the QBWO as far as the TP area.

关键词： quasi-biweekly oscillation Asian monsoon region northward-propagating mechanism surface sensible heating atmospheric general circulation model

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MACHINE LEARNING-BASED MULTI-CHANNEL EVALUATION POOLING STRATEGY FOR IMAGE QUALITY ASSESSMENT

MACHINE LEARNING-BASED MULTI-CHANNEL EVALUATION POOLING STRA...

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IEEE International Conference on Image Processing

作者： Anzhou Hu Rong Zhang Dong Yin Wenlong Hu Department of Electronic Engineering and Information Science University of Science and Technology of China Key Laboratory of Geospatial Information Processing and Application System Technology Chinese Academy of Sciences

ISBN: (纸本)9781479923427

Multi-channel peculiarity is one of the most widely accepted human visual system (HVS) models for perceptual image quality assessment (IQA). Otherwise than extensive studies of channel decomposition and intra-channel distortion measure, relatively scant research effort has been devoted to develop efficient multichannel evaluation pooling strategies. In this paper, we review and address the limitations of the conventional pooling models based on HVS sensitivities-weighted average. Instead, we explore the utilization of machine learning for this pooling problem, since machine learning can establish an optimal and generalized mapping that models the highly complex relationship between the multi-channel distortion evaluations and the perceived image quality. Experiments based on available subjective IQA databases demonstrate the rationality, reliability and robustness of our proposed scheme.

关键词： Image quality assessment (IQA) Multi-channel model Evaluation pooling Machine learning

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Ecological Environment Evaluation of Liuxi River Basin During Rapid Urbanization 7th

Ecological Environment Evaluation of Liuxi River Basin Durin...

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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： Xie, Yunwen Liu, Xulong Wu, Zhifeng Yang, Ji Zhang, Jingjun Key Laboratory 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 School of Geographical Sciences Guangzhou University Guangzhou510006 China Guangzhou511458 China Xinhua College of Sun Yat-Sen University Guangzhou510520 China

ISBN: (纸本)9789811561054

This article takes Liuxi River Basin in Guangzhou as the research object, selects Landsat remote sensing data of 1993, 2000, 2011 and 2018 as data source, to access the five indices, including vegetation coverage, DEM, land use, soil index and slope. The comprehensive index method is used to obtain the ecological environment comprehensive index of this study area. The environmental quality is divided into 4 grades. The ecological environment of the basin and its dynamic changes have been comprehensively evaluated in the past 25 years. The results show that: In the Liuxi River Basin in 2018, the percentage of excellent natural ecological environment quality was 4.80%, the percentage of the areas with good quality was 44.53%;the percentage of the areas with general quality was 45.97% and the percentage of the areas with poor quality was 4.68%. Overall, the good and the general ecological environment quality is the majority. From the perspective of time from 1993 to 2018, the percentage of the area with excellent environmental quality increased by 1.69%;the percentage of the area with good environmental quality gradually decreased, with a total of 32.88%, but still occupied the largest proportion;The percentage of area with general environmental quality went up and down, with a total of 29.64%;the percentage of the area with poor environmental quality rose by 1.54%, the overall change was not big. The downstream ecological environment is showing a trend of deterioration. In upstream, the excellent ecological environment is the majority. The overall ecological environment of the river basin is developing for the better. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Landsat

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An interactive system for eficient 3D furniture arrangement

An interactive system for eficient 3D furniture arrangement

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2017 Computer Graphics International Conference, CGI 2017

作者： Yan, Meng Chen, Xuejin Zhou, Jie CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Dept 6 P.O. Box 4 Hefei Anhui230026 China

ISBN: (纸本)9781450352284

We present an interactive example-based system for non-expert users to generate 3D indoor scenes intuitively. From a set of examples of an interior scene, we extract furniture layout constraints including pairwise and group relationships, ergonomic factors and user habits. Instead of inserting a single furniture object to a visual pleasing and functional position independently, we take advantages of manipulating a group of functionality-related furniture objects for smart editing. To deal with the arrangement problem which requires jointly optimizing a variety of functional and visual criteria, we introduce an efficient two-step pipeline consisting of rough arrangement and layout refinement. In the rough arrangement step, furniture objects are roughly placed according to pairwise relationships for accelerating the further optimization process. In the layout refinement step, we optimize the rough furniture layout to a semantical and functional layout considering spatial relationships, and ergonomic factors and avoiding collisions. User interactions are also allowed to move a specific object or a structural group while our system automatically refines the entire layout. The experimental results demonstrate that our system for scene generation measurably increases the visual quality and time efficiency compared with the completely manual scene editing or modeling. © 2017 ACM.

关键词： Virtual reality

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Learning to Infer Weather States Using Partial Observations

Journal of Geophysical Research: Machine Learning and Comput...

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Journal of Geophysical Research: Machine Learning and Computation 2025年第1期2卷

作者： Jie Chao Baoxiang Pan Quanliang Chen Shangshang Yang Jingnan Wang Congyi Nai Yue Zheng Xichen Li Huiling Yuan Xi Chen Bo Lu Ziniu Xiao National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Science Beijing China School of Atmospheric Sciences Chengdu University of Information Technology Chengdu China Key Laboratory of Mesoscale Severe Weather Ministry of Education and School of Atmospheric Sciences Nanjing University Nanjing China College of Computer National University of Defense Technology Hunan China Clustertech LTD Hong Kong China National Climate Center China Meteorological Administration Beijing China

Accurate state estimation of the high-dimensional, chaotic Earth's atmosphere marks a Sisyphean task, yet is indispensable for initiating weather forecasts and gauging climate variability. While much effort is devoted to assimilating observations and forecasts to infer weather states, the inherent low-dimensional statistical structure in atmospheric circulation, shaped by geophysical laws and geographic boundaries, is underutilized as an informative prior for state inference, or as reference for assessing representative of existing observations and planning new ones. We realize these potential by learning climatological distribution from climate reanalysis/simulation, using a deep generative model. For a case study of estimating 2 m temperature spatial patterns, the learned distribution faithfully reproduces climatology statistics. A combination of the learned climatological prior with few station observations yields strong posterior of spatial pattern estimates, which are spatially coherent, faithful and adaptive to observational constraints, and uncertainty-aware. This allows us to evaluate each observation's value in reducing pattern estimation uncertainty, and guide optimal observation network design by pinpointing the most informative sites. Our study showcases how generative models can extract and utilize information produced in the chaotic evolution of climate system. Deep generative model enables accurate spatial interpolation of weather variables from sparse observations The generative model produces spatially coherent and uncertainty-aware estimates, effectively constrained by observational data The approach provides a framework to quantify observation impact on uncertainty reduction, enabling optimization of observation networks Accurate estimation of weather conditions across a large area is crucial but challenging due to the complex and chaotic nature of the atmosphere. Traditional methods rely on combining observations with forecasts, which can be comp

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Injecting spectral indices to transferable convolutional neural network under imbalanced and noisy labels for Landsat image classification

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International Journal of Digital Earth 2022年第1期15卷 437-462页

作者： Xuemei Zhao Jun Wu Haijian Wang Xingyu Gao Longlong Zhao School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuangxiPeople's Republic of China Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology School of Mechanical and Electrical EngineeringGuilin University of Electronic TechnologyGuangxiPeople's Republic of China Center for Geospatial Information Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesBeijingPeople's Republic of China

Stable and continuous remote sensing land-cover mapping is important for agriculture,ecosystems,and land *** neural networks(CNNs)are promising methods for achieving this ***,the large number of high-quality training samples required to train a CNN is difficult to *** practice,imbalanced and noisy labels originating from existing land-cover maps can be used as *** have shown that the inconsistency in the training samples has a significant impact on the performance of the *** overcome this drawback,a method is proposed to inject highly consistent information into the network,to learn general and transferable features to alleviate the impact of imperfect training *** indices are important features that can provide consistent *** indices can be fused with CNN feature maps which utilize information entropy to choose the most appropriate CNN layer,to compensate for the inconsistency caused by the imbalanced,noisy *** proposed transferable CNN,tested with imbalanced and noisy labels for inter-regional Landsat time-series,not only is superior in terms of accuracy for land-cover mapping but also demonstrates excellent transferability between regions in both time series and cross-regional Landsat image classification.

关键词： Landsat image classification imbalanced and noisy label convolutional neural network(CNN) transferability feature fusion information entropy

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