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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Zhongling Huang Mihai Datcu Zongxu Pan Bin Lei Key Laboratory of Technology in Geo-spatial Information Processing and Application System CAS Remote Sensing Technology Institute (IMF) German Aerospace Center (DLR)

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Deep learning based patch-wise Synthetic Aperture Radar (SAR) image classification usually requires a large number of labeled data for training. Aiming at understanding SAR images with very limited annotation and taking full advantage of complex-valued SAR data, this paper proposes a general and practical framework for quad-, dual-, and single-polarized SAR data. In this framework, two important elements are taken into consideration: image representation and physical scattering properties. Firstly, a convolutional neural network is applied for SAR image representation. Based on time-frequency analysis and polarimetric decomposition, the scattering labels are extracted from complex SAR data with unsupervised deep learning. Then, a bag of scattering topics for a patch is obtained via topic modeling. By assuming that the generated scattering topics can be regarded as the abstract attributes of SAR images, we propose a soft constraint between scattering topics and image representations to refine the network. Finally, a classifier for land cover and land use semantic labels can be learned with only a few annotated samples. The framework is hybrid for the combination of deep neural network and explainable approaches. Experiments are conducted on Gaofen-3 complex SAR data and the results demonstrate the effectiveness of our proposed framework.

关键词： Deep learning Training Annotations Scattering Image representation Radar polarimetry Synthetic aperture radar

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Spatial Pattern of Housing Sales Vacancy in Guangzhou’s Urban District, China

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Journal of World Architecture 2021年第6期5卷 47-51页

作者： Xiaoli Yue Yang Wang Yabo Zhao Hong’ou Zhang Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and ApplicationGuangzhou Institute of GeographyGuangdong Academy of SciencesGuangzhou 510070Guangdong ProvinceChina School of Architecture and Urban Planning Guangdong University of TechnologyGuangzhou 510090Guangdong ProvinceChina Faculty of Geography Yunnan Normal UniversityKunming 650500Yunnan ProvinceChina

Housing vacancy can reflect the destocking degree of the real estate *** on the data of 57 opened residential quarters(46,622 units)from 2015 to 2018,this paper constructs a calculation formula of the sales vacancy rate and then analyzes the spatial pattern in Guangzhou’s urban *** results show that there is obvious differentiation in the spatial pattern of housing sales vacancy in Guangzhou’s urban district,showing a higher spatial pattern in the old area and urban district and a lower spatial pattern in the core *** with high vacancy rates are mainly located in the east of the old area,the south and east of the urban district and near Baiyun Mountain in the north.

关键词： Housing vacancy Sales vacancy Guangzhou’s urban district Spatial pattern

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Research and application of Automatic Classification Method for Patrol Targets of Transmission Lines Based on LiDAR Point Cloud 6th

Research and Application of Automatic Classification Method ...

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6th International Conference on Geo-informatics in Sustainable Ecosystem and Society, GSES 2018

作者： Zhang, Chen Li, Yong Zhou, Xia Yang, Ji Yang, Chuanxun Liu, Hongkai Guangzhou Institute of Geography Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou510070 China

ISBN: (纸本)9789811370243

The development of airborne laser radar technology and cloud data processing technology provides a new technical means for the acquisition and processing of geospatial 3D data. Based on the self-developed LiDAR UAV, this paper proposes a new point cloud automatic classification method (TL-PCACM) of transmission lines by using the improved fast 3D convex hull construction algorithm, k-means clustering analysis algorithm and Region-growing algorithm, which have high classification accuracy, good classification effect and fast processing speed. The accuracy of point cloud automatic classification is over 95%, the efficiency can reach 60 km/h. The proposed method is applied to the analysis of tree obstacles under transmission lines. The efficiency of the automatic identification of tree obstacles can reach 720 towers/hour. The accuracy can reach 50 cm. It provides high efficiency, high quality, fully automatic and intelligent data processing method for the classification of tree obstacles, effectively improving the processing accuracy and speed of geospatial 3D information data. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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Multi-Channel Spaceborne SAR Imaging Method for Maritime Scenarios

Multi-Channel Spaceborne SAR Imaging Method for Maritime Sce...

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Sensor Array and Multichannel Signal processing Workshop

作者： Xiaolan Qiu Junying Yang Mingyang Shang Lihua Zhong Chibiao Ding Key Laboratory of Technology in GeoSpatial Information Processing and Application Systems Aerospace Information Research Institute Chinese Academy of Sciences Beijing China National Key Laboratory of Microwave Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

The spaceborne SAR is required to fulfill the increasing demands for improved spatial resolution and wider swath coverage in recent years. The azimuth multi-channel SAR system is a typical technique adopted for realizing high-resolution and wide-swath (HRWS) simultaneously. The flexibility of this system also provides favorable conditions for moving target detection. Aiming at the problems of moving target detection, velocity estimation, and imaging of SAR maritime scenes in the spaceborne multi-channel system, this paper proposes a set of processes based on coarse imaging, detection, velocity estimation and refocusing. The experimental results of the simulation verify the effectiveness of the method.

关键词： Azimuth Conferences Imaging Estimation Object detection Radar polarimetry Doppler effect

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Large-Scale Single Tree information Extraction of Oil Palm in Malaysia Based on Sub-Meter Visible Light Satellite Images

SSRN

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SSRN 2024年

作者： Zhang, Sen Li, Nan Cui, Yaoping Dong, Jinwei Yu, Le Ran, Lishan Chen, Ziyue Niu, Jiqiang Yan, Wei Kanniah, Kasturi Devi Xiao, Xiangming Zhang, Yongguang Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions Henan University Ministry of Education Kaifeng475004 China College of Geography and Environmental Science Henan University Kaifeng475004 China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application International Institute for Earth System Sciences Nanjing University Nanjing210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing100084 China Department of Geography The University of Hong Kong Pokfulam999077 Hong Kong State Key Laboratory of Earth Surface Processes and Resource Ecology College of Global Change and Earth System Science Beijing Normal University Beijing100875 China College of Geographic Sciences Xinyang Normal University Xinyang464000 China Department of Geoinformation Faculty of Built Environment & Surveying Universiti Teknologi Malaysia Johor Bahru81310 Malaysia Department of Microbiology and Plant Biology Center for Spatial Analysis University of Oklahoma NormanOK73019 United States College of Environment and Planning Faculty of Geoinformation and Real Estate

The distribution of specific tree species and single tree information can shift down the research scale of geography and ecology, bridging research from regional to local scales. However, efficiently and finely extracting the spatial distribution and single tree information for a tree species regionally has always been a challenge due to the constraints of data and algorithms. With sub-meter visible light satellite images (RGB bands) in 2021 to 2022, we constructed a novel method for extracting the spatial distribution and single tree information of oil palm across Malaysia, namely the Single Tree Extraction Method - Oil Palm (STEM-OP). We developed a graphic morphological algorithm based on gradient difference window detection to extract the spatial distribution of oil palms. Then, we proposed an algorithm of double ring sliding detection algorithm to identify the canopy texture features of single oil palm. Finally, an adaptive iterative erosion algorithm was employed to separate overlapping and adjacent canopies into single trees. The overall accuracy of both the spatial distribution and number of single oil palm extracted by STEM-OP exceeded 90%. Our study revealed that the total area of oil palm in Malaysia was 5.25 million hectares, with a total of 572.6 million trees and an average planting density of 109 palms per hectare. The STEM-OP addresses the computational inefficiencies of methods with training samples and limitations of satellite image bands. For the first time, our study extracts the refined spatial distribution of oil palm and obtains the planting density and single tree position on a national scale. All of these are of great value to the formulation of oil palm development policies and ecological protection measures. © 2024, The Authors. All rights reserved.

关键词： Morphology

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Sasl: Saliency-adaptive sparsity learning for neural network acceleration

arXiv

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arXiv 2020年

作者： Shi, Jun Xu, Jianfeng Tasaka, Kazuyuki Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China KDDI Research Japan

Accelerating the inference speed of CNNs is critical to their deployment in real-world applications. Among all the pruning approaches, those implementing a sparsity learning framework have shown to be effective as they learn and prune the models in an end-to-end data-driven manner. However, these works impose the same sparsity regularization on all filters indiscriminately, which can hardly result in an optimal structuresparse network. In this paper, we propose a Saliency-Adaptive Sparsity Learning (SASL) approach for further optimization. A novel and effective estimation of each filter, i.e., saliency, is designed, which is measured from two aspects: The importance for the prediction performance and the consumed computational resources. During sparsity learning, the regularization strength is adjusted according to the saliency, so our optimized format can better preserve the prediction performance while zeroing out more computation-heavy filters. The calculation for saliency introduces minimum overhead to the training process, which means our SASL is very efficient. During the pruning phase, in order to optimize the proposed data-dependent criterion, a hard sample mining strategy is utilized, which shows higher effectiveness and efficiency. Extensive experiments demonstrate the superior performance of our method. Notably, on ILSVRC-2012 dataset, our approach can reduce 49.7% FLOPs of ResNet-50 with very negligible 0.39% top-1 and 0.05% top-5 accuracy degradation. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Cascaded Regression Tracking: Towards Online Hard Distractor Discrimination

arXiv

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arXiv 2020年

作者： Wang, Ning Zhou, Wengang Tian, 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 Hefei China Huawei Noah’s Ark Laboratory

Visual tracking can be easily disturbed by similar surrounding objects. Such objects as hard distractors, even though being the minority among negative samples, increase the risk of target drift and model corruption, which deserve additional attention in online tracking and model update. To enhance the tracking robustness, in this paper, we propose a cascaded regression tracker with two sequential stages. In the first stage, we filter out abundant easily-identified negative candidates via an efficient convolutional regression. In the second stage, a discrete sampling based ridge regression is designed to double-check the remaining ambiguous hard samples, which serves as an alternative of fully-connected layers and benefits from the closed-form solver for efficient learning. During the model update, we utilize the hard negative mining technique and an adaptive ridge regression scheme to improve the discrimination capability of the second-stage regressor. Extensive experiments are conducted on 11 challenging tracking benchmarks including OTB-2013, OTB-2015, VOT2018, VOT2019, UAV123, Temple-Color, NfS, TrackingNet, LaSOT, UAV20L, and OxUvA. The proposed method achieves state-of-the-art performance on prevalent benchmarks, while running in a real-time speed. Copyright © 2020, The Authors. All rights reserved.

关键词： Regression analysis

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SFEF-Net: Scattering Feature Extraction and Fusion Network for Aircraft Detection in SAR Images

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Sensors (Basel, Switzerland) 2025年第10期25卷 2988页

作者： Qiang Zhou Zongxu Pan Ben Niu Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China. Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing 100190 China. School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing 100049 China.

Synthetic aperture radar (SAR) offers robust Earth observation capabilities under diverse lighting and weather conditions, making SAR-based aircraft detection crucial for various applications. However, this task presents significant challenges, including extracting discrete scattering features, mitigating interference from complex backgrounds, and handling potential label noise. To tackle these issues, we propose the scattering feature extraction and fusion network (SFEF-Net). Firstly, we proposed an innovative sparse convolution operator and applied it to feature extraction. Compared to traditional convolution, sparse convolution offers more flexible sampling positions and a larger receptive field without increasing the number of parameters, which enables SFEF-Net to better extract discrete features. Secondly, we developed the global information fusion and distribution module (GIFD) to fuse feature maps of different levels and scales. GIFD possesses the capability for global modeling, enabling the comprehensive fusion of multi-scale features and the utilization of contextual information. Additionally, we introduced a noise-robust loss to mitigate the adverse effects of label noise by reducing the weight of outliers. To assess the performance of our proposed method, we carried out comprehensive experiments utilizing the SAR-AIRcraft1.0 dataset. The experimental results demonstrate the outstanding performance of SFEF-Net.

关键词： aircraft detection deep learning global information fusion and distribution noise-robust loss scattering feature extraction synthetic aperture radar

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Static Background Imaging and Moving Target Signature Removal in Multiaspect SAR Image 6

Static Background Imaging and Moving Target Signature Remova...

引用

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

作者： Shen, Wenjie Hong, Wen Han, Bing Wang, Yanping Lin, Yun Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing China Beijing China Beijing China North China University of Technology Beijing China

ISBN: (纸本)9781728129129

Multiaspect SAR has capability of providing high resolution image of static scene due to its long aperture feature. However, moving target can generate long and complex image trace in Multiaspect SAR, which may hamper the applications like SAR image interpretation and target detection. In this paper, two methods are proposed to remove moving target signature in single channel multiaspect SAR image. The two methods are all based on logarithm background subtraction. The first one is a fast scheme with cost of reduced resolution, while the second one focus on providing the high resolution but it takes more time than previous one. The first method utilize fact of target signal position changes in subaperture image sequence to obtain the static background image. The second method combines the detection results to exclude the moving target signal in each subaperture image. Then obtain the high resolution of static background by coherent summation. The methods are validated by both airborne SAR data. © 2019 IEEE.

关键词： Synthetic aperture radar

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Directional Analysis of Urban Expansion Based on Sub-pixel and Regional Scale: A Case Study of Main Districts in Guangzhou, China

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Chinese Geographical Science 2019年第4期29卷 652-666页

作者： ZHAO Yi ZHONG Kaiwen XU Jianhui SUN Caige WANG Yunpeng Guangzhou Institute of Geochemistry Chinese Academy of Sciences Guangzhou 510640 China Guangzhou Institute of Geography Guangzhou 510070 China University of Chinese Academy of Sciences Beijing 100049 China Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangzhou 510070 China Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou 510070 China

Multi-scale data have had a wide-ranging level of performance in the area of urban change monitoring. Herein we investigate the correlation between the impervious surface fraction(ISF) and the Defense Meteorological Satellite Program/Operational Linescan system(DMSP/OLS) nighttime stable light(NTL) data with respect to the urban expansion in the main districts of Guangzhou. Landsat 5 Thematic Mapper and Landsat 8 Operational Land Image(OLI) data from 1988 to 2015 were used to extract the ISF using the linear spectral mixture analysis model and normal difference build-up index at the sub-pixel scale. DMSP/OLS NTL data from 1992 to 2013 were calibrated to illustrate the urban nighttime light conditions at the regional scale. Urban expansion directions were identified by statistics and kernel density analysis for the ISF study area at the sub-pixel scale. In addition, the correlation between the ISF and DMSP/OLS NTL data were illustrated by linear regression analysis. Furthermore, Profile Graph in ArcGIS was employed to illustrate the urban expansion from the differences in correlation in different directions. The conclusions are as follows: 1) The impervious surface(IS)in the study area has expanded to the northeast and the east, starting with the old urban zones, and the high-density IS area has increased by321.14 km^2. 2) The linear regression analysis reveals a positive correlation between the ISF and the DMSP/OLS NTL data. The multi-scale data changes are consistent with the actual urban planning of Guangzhou. 3) The DMSP/OLS NTL data overestimate the urban extent because of its saturation and blooming effects, causing its correlation with ISF to decrease. The pattern of urban expansion influences the saturation and blooming effects of the DMSP/OLS NTL data.

关键词： impervious surface fraction linear spectral mixture analysis nighttime light kernel density profile graph

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