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Intertidal area classification with generalized extreme value distribution and Markov random field in quad-polarimetric synthetic aperture radar imagery

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Frontiers of information technology & Electronic Engineering 2019年第2期20卷 253-264页

作者： Ting-ting JIN Xiao-qiang SHE Xiao-lan QIU Bin LEI Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of ElectronicsChinese Academy of Sciences University of Chinese Academy of Sciences Huawei Software Technologies Co. Ltd.

Classification of intertidal area in synthetic aperture radar(SAR) images is an important yet challenging issue when considering the complicatedly and dramatically changing features of tidal fluctuation. The difficulty of intertidal area classification is compounded because a high proportion of this area is frequently flooded by water, making statistical modeling methods with spatial contextual information often ineffective. Because polarimetric entropy and anisotropy play significant roles in characterizing intertidal areas, in this paper we propose a novel unsupervised contextual classification algorithm. The key point of the method is to combine the generalized extreme value(GEV) statistical model of the polarization features and the Markov random field(MRF) for contextual smoothing. A goodness-of-fit test is added to determine the significance of the components of the statistical model. The final classification results are obtained by effectively combining the results of polarimetric entropy and anisotropy. Experimental results of the polarimetric data obtained by the Chinese Gaofen-3 SAR satellite demonstrate the feasibility and superiority of the proposed classification algorithm.

关键词： Intertidal classification Polarimetric synthetic aperture radar Finite mixture model Markov random field Generalized extreme value model

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Multi-scale coupling measurement and driving factor analysis between carbon emission from energy consumption and ecological environment—The case study of Pearl River Delta urban agglomeration

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Sustainable Cities and Society 2025年 127卷

作者： Yanjun Wang Mengru Song Yongshun Han Yichang Wang School of Earth Science and Space Information Engineering Hunan University of Science and Technology Xiangtan 411201 China National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology Hunan University of Science and Technology Xiangtan 411201 China

The coupling relationship between energy carbon emissions and the ecological environment is crucial for sustainable regional development. With the acceleration of urbanization and the increase of energy consumption, carbon emission rises significantly, causing significant impacts on the ecological environment. Based on multi-source data such as NPP-VIIRS nighttime light images and statistical yearbooks, this study combined the entropy-weighted TOPSIS method, modified coupling coordination degree model (CCDM), modified Gravity Model, and geoDetector to analyze the coupled relationship between energy consumption carbon emissions and ecological environment of the Pearl River Delta (PRD) urban agglomeration at pixel, county, and city scales, and to reveal the characteristics of the spatio-temporal evolutions, the driving factors, and spatial correlation networks. The findings indicate that (1) during the study period, the ecological environment evaluation index (ENI) remains stable, with a fluctuation range of 0.22-0.26; the energy consumption carbon emission evaluation index (CEI) decreases significantly by 59.6 % in 2022 compared with 2012, indicating that the intensity of the regional carbon emissions is weakening; (2) the coupling coordination degree (CCD) shows a spatial distribution of “high in the periphery and low in the middle”, with Zhaoqing and Huizhou cities higher and Dongguan and Zhongshan cities lower, indicating that there are obvious spatial differences in the coupling relationship; (3) the network correlation of CCD fluctuates: from 2012 to 2016, per capita GDP is the dominant factor, with an average contribution rate of 0.959 %; from 2018 to 2022, population density is the main driver. County and pixel scales show stronger explanatory power and stable interactions, with key pairings like PD∩TI exceeding 0.98, highlighting multi-factor synergy and the value of finer spatial analysis. By demonstrating the multi-scale coupling coordination relationship b

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An azimuth resample method for bistatic SAR motion compensation 8

An azimuth resample method for bistatic SAR motion compensat...

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8th European Conference on Synthetic Aperture Radar, EUSAR 2010

作者： Qiu, Xiaolan Han, Bin Meng, Dadi Hu, Donghui Ding, Chibiao Key Laboratory of Technology in Geo-spatial Information Processing and Application System IECAS China China

ISBN: (纸本)9783800732722

Motion compensation is an indispensable step for generating a high quality image in the airbore SAR system. In bistatic SAR, there are two separate platforms and their velocities cannot keep same all the time. So the echoes are transmitted and received at uniform positions, respectively, which deteriorate the imaging results. In this paper, an azimuth resample method for compensating the velocity variance along the flying direction is promoted, and the difference of azimuth resample between mono static SAR and bistatic SAR is pointed out. Finally, simulated results show the validity of the method. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Motion compensation

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A model based on spectral invariant theory for correcting topographic effects on vegetation canopy reflectance

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Remote Sensing of Environment 2025年 322卷

作者： Li, Weihua Yan, Guangjian Geng, Jun Guo, Yuhan Xie, Tian Mu, Xihan Xie, Donghui Roujean, Jean-Louis Zhou, Guoqing Gastellu-Etchegorry, Jean-Philippe State Key Laboratory of Remote Sensing Science Faculty of Geographical Science Beijing Normal University Beijing100875 China Beijing Engineering Research Center for Global Land Remote Sensing Products Faculty of Geographical Science Beijing Normal University Beijing100875 China School of Resources and Environmental Engineering Hefei University of Technology Anhui 230002 China CESBIO CNES-CNRS-IRD-UT3 University of Toulouse CEDEX 09 Toulouse31401 France Guangxi Key Laboratory of Spatial Information and Geomatics College of Geomatics and Geoinformation Guilin University of Technology Guilin541006 China

Topography alters both the incident radiation and radiative transfer (RT) processes within the canopy, leading to changes in the canopy bidirectional reflectance factor (BRF). Most traditional semi-physical terrain correction (TC) methods for vegetation canopy BRFs rely on simplifying physically-based analytical RT models. However, these analytical RT models are not comprehensively parameterized for all RT computations, leading to the neglect of crucial processes, such as multiple scattering processes during the derivation of semi-physical TC methods. The spectral invariants theory (p-theory) offers an efficient approach to model canopy BRFs by simplifying RT computations. We extended p-theory to sloping terrain, considering the variation of the terrain-induced incident radiation and RT processes, and developed a canopy BRF TC model, termed the p-C method. The p-C method applies not only to spectral bands with lower multiple scattering within the canopy (e.g., visible bands) but also to near-infrared (NIR) bands, where multiple scattering effects may be more pronounced than in the visible bands within the canopy. We used the three-dimensional RT model DART (Discrete Anisotropic Radiative Transfer) to simulate BRFs of homogeneous, realistic canopies of the RAMI (RAdiation transfer Model Intercomparison) experiment, and BRF images with real DEM (Digital Elevation Model) to evaluate the p-C method and to compare it with traditional empirical and semi-physical TC methods (CC, SCS, SCS+C, DS, PLC-S, and SE). The p-C method reduced the RMSE (root mean square error) by 67 %, 64 %, 64 %, 85 %, 83 %, and 54 % respectively over these methods. Furthermore, when applied to Landsat 8 OLI remote sensing BRF images, the p-C method effectively eliminated terrain texture, as confirmed by visual interpretation and the linear regression between the corrected BRF images and the local solar incidence angle. Currently, the p-C method only considers illuminated slopes, and corrections for

关键词： Reflection

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Biophysical impact of forest age changes on land surface temperature in China

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Science of the Total Environment 2025年 964卷 178445页

作者： Zhang, Zhijiang Wang, Lunche Chen, Chao Zhang, Xiang Ding, Chao Yuan, Moxi Shen, Lixing Li, Xinxin School of Geography Science and Geomatics Engineering Suzhou University of Science and Technology Suzhou215009 China Suzhou Key Laboratory of Spatial Information Intelligent Technology and Application Suzhou University of Science and Technology Suzhou215009 China School of Geography and Information Engineering China University of Geosciences Wuhan430074 China Department of Geographic Science Faculty of Arts and Sciences Beijing Normal University Zhuhai519087 China School of Public Administration and Human Geography Hunan University of Technology and Business Changsha410205 China School of Atmospheric Science and Remote Sensing Wuxi University Wuxi214105 China School of Resource and Environmental Science Wuhan University Wuhan430079 China

Forest age structures have been substantially affected by natural disturbances and anthropogenic activities worldwide. Their changes can significantly influence local and nonlocal climate through both the biogeochemical and biophysical processes. However, numerous studies have focused on the biogeochemical effect of forest age changes whereas the biophysical effect has received far less attention. Here we investigated how forest age changes influence land surface temperature (LST) by comparing older forests and adjacent younger forests pixels and unraveled underlying biophysical mechanisms using satellite observations over China during 2003–2012. Our study showed that older forests had a substantial annual cooling benefit than adjacent medium-aged and young forests. Attribution analysis indicated that the cooling effect of latent heat flux counteracted the albedo-induced warming effect, leading to the net cooling effect of older evergreen needle-leaved forest or evergreen broadleaved forest. Furthermore, the cooling effect of sensible heat flux is greater than the albedo-driven warming effect, contributing to the net cooling effect of older deciduous broadleaved forest. Our work is a step forward to underscore the potential of preserving mature forests as a local climate adaptation strategy and provides important parameterization foundation for earth system models without incorporation of forest age modules. © 2025 Elsevier B.V.

关键词： Land surface temperature

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LLaMA-Unidetector: An LLaMA-Based Universal Framework for Open-Vocabulary Object Detection in Remote Sensing Imagery

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IEEE Transactions on geoscience and Remote Sensing 2025年 63卷

作者： Jianlin Xie Guanqun Wang Tong Zhang Yikang Sun He Chen Yin Zhuang Jun Li National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing (SBIIP) Beijing Institute of Technology Beijing China National Key Laboratory for Multimedia Information Processing Peking University Beijing China School of Computer Science and Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan China

The object detection is a crucial task in the computer vision for remote sensing applications. However, the reliance of traditional methods on predefined and trained object categories limits their applicability in open-world scenarios. A key challenge in open-vocabulary object detection lies in accurately identifying unseen objects. Existing approaches often focus solely on detecting object locations, struggling to recognize the categories of previously unseen targets. To address this issue, we propose a novel benchmark, where models are trained on known base classes and evaluated on their performance in detecting and recognizing unseen or novel classes. To this end, we introduce LLaMA-Unidetector, a universal framework that incorporates textual information into a closed-set detector, enabling the generalization to open-set scenarios. Our LLaMA-Unidetector leverages a decoupled learning strategy that separates localization and recognition. In the first stage, a class-agnostic detector identifies objects, distinguishing only between foreground and background. In the second stage, the detected foreground objects are passed through TerraOV-LLM, a multimodal large language model (MLLM), for recognition, utilizing the strong generalization capabilities of large language models to infer the correct categories. We propose a self-built vision question answering (VQA) remote sensing dataset, TerraVQA, and conduct extensive experiments on the NWPU-VHR10, DOTA1.0, and DIOR datasets. The LLaMA-Unidetector achieves impressive results, with a performance of 75.46% AP, 50.22% AP, and 51.38% AP on the zero-shot detection benchmarks for the NWPU-VHR10, DOTA1.0, and DIOR datasets, respectively. Our source code is available at: https://***/ChloeeGrace/LLaMA-Unidetector

关键词： Object detection Remote sensing Training Detectors Visualization Feature extraction Location awareness Semantics Data mining Optimization

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geometry-aware image completion via multiple examples 22

Geometry-aware image completion via multiple examples

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22nd Pacific Conference on Computer Graphics and Applications, PG 2014

作者： Wang, Guihang Chen, Xuejin Hu, Siyu CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China China

ISBN: (纸本)9783905674736

When browsing through photographs taken during a trip, it can be a distressing discovery to find many other bystanders captured within the frame. A visually compelling snapshot preserves the desired subject in the foreground, and eliminates irrelevant persons or objects. In this paper, we present a new image completion algorithm that retains the geometric consistency of the scene using Internet photo collections. Our key insight allows us to successfully fill the missing regions with content derived from related images with distinct features, or from visible parts of the input image with repetitive textures. The final composition for the missing regions is guided by a confidence map combining the two types of example images. We validate our algorithm on a variety of scenes which are challenging for state-of-the-art completion techniques. The completion results successfully preserve the geometry consistency for complex structures in a wide range of depths. © The Eurographics Association 2014.

关键词： geometry

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3D Grid-Based Resilient Pseudorange Error Prediction for Adaptive GNSS/IMU Integrated Navigation in Urban Areas

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IEEE Internet of Things Journal 2025年

作者： Sun, Rui Sheng, Qi Cheng, Qi Shang, Xiaotong Ochieng, Washington Yotto Ministry of Education Key Laboratory of Maritime Intelligent Cyberspace Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Nanjing University of Aeronautics and Astronautics College of Civil Aviation Nanjing211106 China The Hong Kong Polytechnic University Department of Land Surveying and Geo-Information Hong Kong100872 Hong Kong Imperial College London Department of Civil and Environmental Engineering SW7 2AZ United Kingdom

Non-line of sight (NLOS) and multipath are known to cause pseudorange measurement errors, leading to excessive positioning errors in challenging urban environments. Although GNSS and inertial measurement units (IMU) integrated system can enhance the positioning performance, the positioning accuracy is still constrained by the filter performance. The existing pseudorange correction algorithm employs simple measurement noise covariance adjusting strategy, which is unable to adapt the varying measurement noise in the complex urban environment. To solve this issue, a three-dimensional grid-based resilient pseudorange error prediction algorithm is proposed to adjust the measurement noise covariance R in Kalman filter (3D-RKF) in urban areas. The urban area is divided by the proposed 3D grid layout and the pseudorange errors are predicted by ensemble bagged regression tree (EBRT) grid by grid to achieve fine-scale pseudorange prediction. R is then updated by the proposed model reliability indicator (MRI)-based adaptive fusion strategy. Experimental results in complex urban areas prove the proposed algorithm can reach a 3D accuracy of 10.16 m, with an improvement of 52% compared to the EKF-based fusion, 33% compared to two-dimensional grid-based adaptive Kalman filter algorithm without MRI-based adaptive fusion strategy (2D-AKF) and 22% compared to 3D grid-based adaptive Kalman filter algorithm without MRI-based adaptive fusion strategy (3D-AKF), respectively. © 2014 IEEE.

关键词： Adaptive filters

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Construction and analysis of long-term surface temperature dataset in Fujian Province

Construction and analysis of long-term surface temperature d...

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ISPRS geospatial Week 2017

作者： Li, W.E. Wang, X.Q. Su, H. Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education National Engineering Research Centre of Geo-spatial Information Technology Fuzhou University China

Land surface temperature (LST) is a key parameter of land surface physical processes on global and regional scales, linking the heat fluxes and interactions between the ground and atmosphere. Based on MODIS 8-day LST products (MOD11A2) from the splitwindow algorithms, we constructed and obtained the monthly and annual LST dataset of Fujian Province from 2000 to 2015. Then, we analyzed the monthly and yearly time series LST data and further investigated the LST distribution and its evolution features. The average LST of Fujian Province reached the highest in July, while the lowest in January. The monthly and annual LST time series present a significantly periodic features (annual and interannual) from 2000 to 2015. The spatial distribution showed that the LST in North and West was lower than South and East in Fujian Province. With the rapid development and urbanization of the coastal area in Fujian Province, the LST in coastal urban region was significantly higher than that in mountainous rural region. The LST distributions might affected by the climate, topography and land cover types. The spatio-temporal distribution characteristics of LST could provide good references for the agricultural layout and environment monitoring in Fujian Province. © Authors 2017. CC BY 4.0 License.

关键词： Remote sensing

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Analysis and Prediction of Vehicle Exhaust Emission Using ANN 36

Analysis and Prediction of Vehicle Exhaust Emission Using AN...

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第36届中国控制会议

作者： Zhenyi Xu Yu Kang Wenjun Lv Department of Automation and State Key Laboratory of Fire Science University of Science and Technology of China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences

ISBN: (纸本)9781538629185

This paper focused on the analysis of vehicle emission based on the Hefei remote sensing data during the last three *** we propose a three-layer artificial neural network model for predicting vehicle exhaust emission using remote sensing ***,we take adaptive-lasso algorithm to analyze the various factors from the emission data,and determine the principal ***,after doing principal components analysis and selecting algorithm and architecture,the Back-Propagation neural network model with 7-12-1 architecture was established as the optimal ***,we give the prediction results on the testing data-set and prove the potentiality and validity of the proposed method in the prediction of vehicle exhaust emission.

关键词： Vehicle Exhaust Emission Adaptive-Lasso Artificial Neural Network

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