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A semi-analytical radiative transfer model for explaining soil spectral features

intERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2023年 118卷

作者： Wu, Fuyu Tan, Kun Wang, Xue Ding, Jianwei Liu, Zhaoxian Han, Bo China Univ Min & Technol Key Lab Land Environm & Disaster Monitoring NASG Xuzhou 221116 Peoples R China East China Normal Univ Minist Educ Key Lab Geog Informat Sci Shanghai 200241 Peoples R China East China Normal Univ Key Lab Spatial temporal Big Data Anal & Applicat Minist Nat Resources Shanghai 200241 Peoples R China East China Normal Univ Sch Geog Sci Shanghai 200241 Peoples R China Second Surveying & Mapping Inst Hebei Shijiazhuang 050037 Peoples R China China Acad Space Technol Inst Remote Sensing Satellite Beijing 100094 Peoples R China

Spectral techniques play a key role in the estimation of large-scale soil parameters, but most of the current estimation models are data-driven models lacking a physical basis. Therefore, in this study, we developed a novel radiative transfer model named the soil multifactor radiative transfer (SMRT) model that predicts the soil spectra as a function of the soil organic matter (SOM), particle size distribution (PSD), and iron oxide content. The SMRT model explains the soil spectral features commonly used in the data-driven estimation models and can be used to explore the mechanisms by which soil-related parameters affect soil spectra according to the model parameters. A total of 79 soil profile datasets were used in the experiments conducted in this study. The reflectance of the soil after air-drying can ignore the effect of moisture, thereby ensuring that the main influences on the soil spectra are the SOM, iron oxide content, and PSD. The SMRT model performed very well in the spectral simulation (R2 = 0.9681, RMSE = 0.0266, MAE = 0.0160). The absorption and scattering coefficients of the SMRT model explain the radiative transfer mechanism for the color representation of black soil and laterite soil. The spectral features around 1910 nm and 2210 nm are caused by not only the soil moisture (SM) and clay mineral content, but several other factors, including the SOM, iron oxide content, and PSD. Overall, these findings show that the SMRT model has a superior ability to describe the soil radiative transfer processes. However, as the coefficients in the SMRT model are dependent on the soil properties, the coefficients should be calibrated by an optimization algorithm and cannot be constants, which leads to the requirement for soil samples to calibrate the coefficients in practical applications.

关键词： Soil multifactor radiative transfer model Absorption and scattering coefficients Spectral features

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Application of GPS technology in space geological survey

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ARABIAN JOURNAL OF GEOSCIENCES 2019年第23期12卷 713-713页

作者： Chen, Guobin Chongqing Technol & Business Univ Rongzhi Coll Chongqing Key Lab Spatial Data Min & Big Data Int Chongqing 401320 Peoples R China

In order to solve the shortcomings of traditional space geological survey technology, such as weak universality, heavy workload of human-computer interaction, and low accuracy, global positioning system (GPS) and real-time kinematics (RTK) are combined in this study to explore the application of GPS technology in space geological survey by using its global, all-weather, continuity, practicability, navigation, positioning, and timing characteristics. The results show that GPS technology not only has good performance and high precision but also improves the level of surveying and mapping technology compared with traditional geological surveying technology, unifies surveying and mapping benchmarks, avoids repeated construction of surveying marks, and provides multiple information services. The research of this study has great practical significance for traditional space geological survey to change the way of surveying and mapping geological map, to use advanced GPS aided design technology, to improve the level of modernization of design and the efficiency of designers, to realize the reduction of staff and increase efficiency, and to improve the productivity of the whole staff. At the same time, it also has important theoretical and practical value for geological survey. Moreover, the breakthrough of its key technologies will have good popularization value and application prospects.

关键词： Global positioning system Real-time kinematics Geological survey Technology Surveying and mapping benchmark Diversification

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RGSR: A two-step lossy JPG image super-resolution based on noise reduction

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NEUROCOMPUTING 2021年 419卷 322-334页

作者： Li, Biao Shi, Yong Wang, Bo Qi, Zhiquan Liu, Jiabin Southwestern Univ Finance & Econ Sch Business Adm Chengdu 611130 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 101408 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA Univ Int Business & Econ Sch Informat Technol & Management Beijing 100029 Peoples R China Samsung Res China Beijing SRC B Beijing 100028 Peoples R China Southwest Minzu Univ Coll Elect & Informat Engn Chengdu 610041 Peoples R China

Single Image Super-Resolution (SISR) is a fundamental and important low-level computer vision (CV) task, yet its performance on real-world applications is not always satisfactory. Different from the previous SISR research, we focus on a specific but realistic SR issue: How can we obtain satisfied SR results from com-pressed JPG (C-JPG) images, which is a ubiquitous image format to greatly release storage space while missing fine details. the JPG SR task is deeply analyzed to discover the connotation. Then, we propose an effective two-step model structure named RGSR, involving two specifically designed components, i.e., JPG recovering and SR generation, instead of the perspective of noise elimination in traditional SR approaches. Besides, we further integrate the cycle loss to build a hybrid objective across scales for better SR generation. Experimental results on both of the standard test data sets and real images show that our approach achieves outstanding results and succeed in applying to practical C-JPG SR tasks. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Super-resolution JPEG compression Cycle loss Image denoising

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spatial disparity and hierarchical cluster analysis of final energy consumption in China

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ENERGY 2020年第0期197卷 117195-000页

作者： Wang, Shaobin Liu, Haimeng Pu, Haixia Yang, Hao Chinese Acad Sci Inst Geog Sci & Nat Resources Res A11 Datun Rd Beijing 100101 Peoples R China Chongqing Technol & Business Univ Coll Tourism & Land Resources Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Chongqing Key Lab Spatial Data Min & Big Data Int Chongqing 400067 Peoples R China Beijing Acad Social Sci Beijing 100101 Peoples R China

This paper evaluated the spatial patterns and hierarchical clustering of final energy consumption in China from 2014 to 2016. For this purpose, exploratory spatial data analysis, kernel density estimation, and spatial hierarchical clustering were applied. The results presented various spatial characteristics of final energy consumption with different fuel types and consumption sectors. i) Coal and heat consumption showed significant positive spatial autocorrelations, whereas petroleum products exhibited significant negative spatial autocorrelations, and natural gas and electricity without any significant spatial autocorrelations. ii) The industry sector exhibited clusters of hot spots in eastern China and cold spots in southwestern China, which was obviously different from the north-south difference of coal and heat in the sector of residential energy and of transport, storage and post. iii) Residential coal consumption in rural areas exhibited the most significant disparity. In addition, northeastern, northern and eastern parts of China were identified as three spatial clusters of final energy consumption. Further, it indicated to improve the residential coal utilization level in key coal-producing regions such as Shanxi and Guizhou Provinces. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Final energy consumption spatial disparity spatial autocorrelation Hierarchical cluster analysis

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Semisupervised Hypergraph Discriminant Learning for Dimensionality Reduction of Hyperspectral Image

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2020年 13卷 4242-4256页

作者： Luo, Fulin Guo, Tan Lin, Zhiping Ren, Jinchang Zhou, Xiaocheng Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Lanark Scotland Fuzhou Univ Minist Educ Key Lab Spatial Data Min & Informat Sharing Fuzhou 350116 Peoples R China

Semisupervised learning is an effective technique to represent the intrinsic features of a hyperspectral image (HSI), which can reduce the cost to obtain the labeled information of samples. However, traditional semisupervised learning methods fail to consider multiple properties of an HSI, which has restricted the discriminant performance of feature representation. In this article, we introduce the hypergraph into semisupervised learning to reveal the complex multistructures of an HSI, and construct a semisupervised discriminant hypergraph learning (SSDHL) method by designing an intraclass hypergraph and an interclass graph with the labeled samples. SSDHL constructs an unsupervised hypergraph with the unlabeled samples. In addition, a total scatter matrix is used to measure the distribution of the labeled and unlabeled samples. Then, a low-dimensional projection function is constructed to compact the properties of the intraclass hypergraph and the unsupervised hypergraph, and simultaneously separate the characteristics of the interclass graph and the total scatter matrix. Finally, according to the objective function, we can obtain the projection matrix and the low-dimensional features. Experiments on three HSI data sets (Botswana, KSC, and PaviaU) show that the proposed method can achieve better classification results compared with a few state-of-the-art methods. The result indicates that SSDHL can simultaneously utilize the labeled and unlabeled samples to represent the homogeneous properties and restrain the heterogeneous characteristics of an HSI.

关键词： Linear programming Manifolds Dimensionality reduction Hyperspectral imaging Principal component analysis Dimensionality reduction (DR) graph learning hyperspectral image (HSI) classification locality-constrained linear coding neighborhood margin

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Understanding fine-scale heat health risks and the role of green infrastructure based on remote sensing and socioeconomic data in the megacity of Beijing, China

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ECOLOGICAL INDICATORS 2024年 160卷

作者： Zha, Fukang Lu, Linlin Wang, Ran Zhang, Shuangcheng Cao, Shisong Baqa, Muhammad Fahad Li, Qingting Chen, Fang Changan Univ Coll Geol Engn & Geomat Xian Peoples R China China Meteorol Adm Key Lab Urban Meteorol Beijing Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing Peoples R China Nankai Univ Coll Econ & Social Dev Tianjin Peoples R China Beijing Univ Civil Engn & Architecture Sch Geomat & Urban Spatial Informat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Airborne Remote Sensing Ctr Beijing Peoples R China

The frequency and intensity of extreme heat events have been increasing due to the combined effects of global climate change and urbanization. Urban green infrastructure, including urban green and blue space, has been recognized as an effective measure to mitigate urban heat. However, the effects of green infrastructure on heat health risk were insufficiently addressed. To address this gap, we conducted a comprehensive assessment in the megacity of Beijing with a rapidly aging population. Various data sources were collected, including remote sensing images, meteorological data from weather stations, point of interest(POI) data, and social statistics. Following the risk triangle theory, the hazard, population exposure, and social vulnerability components of heat health risk were evaluated at the census tract level. The weights of vulnerability indicators were determined using Principal Component Analysis. Moran's I and Getis-Ord Gi* statistics were used to identify risk hotspot areas. To evaluate the effects of green infrastructure on heat health risk, a Green Infrastructure Index (GII) was created to quantitatively measure the abundance and accessibility of green infrastructure. The analysis, using a spatially-explicit Heat Health Risk Index (HHRI), indicated that the HHRI in the central urban area inhabited by high-income population groups is 2.66 times that of its suburban counterpart. The primary driving factors of heat health risk were identified as high population density and elevated temperatures. Census tracts with abundant green infrastructure exhibited a low likelihood of becoming high-risk areas, with a probability of less than 2%, while regions with limited green infrastructure had a 54.26% probability of becoming high-risk areas. This highlights the significance of expanding the coverage of green spaces and water areas to reduce heat health risk. The findings provide valuable insights for the development of risk mitigation measures enhancing urban therma

关键词： Heat Health Risk Index Urban green space SDG11.7 Nature -based solutions

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RETRACTED: Power Control for Full-Duplex Device-to-Device Underlaid Cellular Networks: A Stackelberg Game Approach (Retracted Article)

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COMPLEXITY 2020年第1期2020卷

作者： Yang, Zhen Liu, Titi Chen, Guobin Chongqing Technol & Business Univ Chongqing Key Lab Spatial Data Min & Big Data Int Rongzhi Coll Chongqing 401320 Peoples R China Shandong First Med Univ & Shandong Acad Med Sci Coll Med Informat Engn Tai An 271016 Shandong Peoples R China

In spectrum sharing cognitive radio networks, unauthorized users (secondary users) are allowed to use the spectrum of authorized users (primary users) to improve spectrum utilization. Due to limited spectrum resources, how to formulate a reasonable spectrum allocation scheme is very important. As a mathematical analysis tool, game theory can solve the problem of resource allocation well. In recent years, it has been applied to the research of resource allocation in spectrum sharing networks by some literatures. In a cellular network consisting of multiple cellular users and full-duplex end-to-end communication users D2D (device-to-device), the self-interference caused by full-duplex communication and the interference caused by the D2D users to the cellular users will significantly reduce system throughput. In order to reduce the interference in the network, this paper introduces a power control algorithm based on Stackelberg game, which sets the cellular users and D2D users as the leaders and followers, respectively. The cellular users and the D2D users compete with each other to minimize the cost, and we propose new utility functions. We build an optimization problem under the outage probability constraint and power constraint and the transmission power of the users is obtained by using the Lagrangian dual decomposition method. The simulation results show that the proposed game algorithm improves network performance compared with other existing schemes.

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Study on ultrasonic treatment for degradation of Microcystins (MCs)

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ULTRASONICS SONOCHEMISTRY 2020年 63卷 104900-104900页

作者： Chen, Guobin Ding, Xinmin Zhou, Wen Chongqing Technol & Business Univ Rongzhi Coll Chongqing Key Lab Spatial Data Min & Big Data Int Chongqing 401320 Peoples R China Chengdu Univ Technol Coll Nucl Technol & Automat Engn Chengdu 610059 Peoples R China GuangZhou Univ Chinese Med Guangdong Prov Acad Chinese Med Sci Clin Coll 2 55 Neihuanxi Rd Guangzhou 510006 Peoples R China

In recent years, The ecological environment of rivers and lakes have been seriously polluted, and the eutrophication of water bodies has become increasingly prominent, which not only seriously affects the living environment of surrounding residents, but also poses a major threat to the ecological security of water environment. The growth of algae is characterized by short cycle, rapid reproduction and great harmfulness. Conventional algal removal technology is expensive, easy to produce secondary pollution, and difficult to effectively inhibit algae outbreaks, therefore, a new environmental protection technology, ultrasonic algae removal technology, has been put forward. Under the background of ecological environment pollution, in this paper, the effect of ultrasonic technology on degradation of Microcystins (MCs) under different conditions and is investigated. Results show that Microcystins removal rate reaches 81% when Microcystin solution with a concentration of 12.43 mu/L is treated by ultrasound (1200 W) for 5 min;the removal rate of Microcystin reaches 99% after 15 min of ultrasound treatment (1200 W), and almost all of them are removed;no matter wastewater containing Microcystis is treated by ultrasound alone or ultrasound-coagulation method, the levels of Microcystins in the water do not increase. The results also prove that ultrasound can directly destroy the wall and kill algae, inhibit the growth activity of un-killed algae and degrade Microcystins. In addition, the technical principle and application prospect of ultrasonic algae removal instrument in ecological environment are introduced. The paper provided certain direction and theoretical support for the subsequent improvement of ultrasonic algae removal technology.

关键词： Ecological environment Microcystins Ultrasound Microcystis

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Landslide Identification and Gradation Method Based on Statistical Analysis and spatial Cluster Analysis

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REMOTE SENSING 2022年第18期14卷 4504-4504页

作者： Dai, Huayan Zhang, Hong Dai, Huayang Wang, Chao Tang, Wei Zou, Lichuan Tang, Yixian Chinese Acad Sci Key Lab Digital Earth Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China China Univ Min & Technol Beijing Coll Geosci & Surveying Engn Beijing 100083 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China

As a type of earth observation technology, interferometric synthetic aperture radar (InSAR) is increasingly widely used in the field of geological disaster detection. However, the application of InSAR in low-coherence areas, such as alpine canyon areas and vegetation coverage areas, is subject to considerable limitations. How to accurately identify landslides from InSAR measurement data in these areas remains the subject of several challenges and shortcomings. Based on statistical analysis and spatial cluster analysis, in this paper, we propose an automatic landslide identification and gradation method suitable for low-coherence areas. The proposed method combines the small baseline subset InSAR (SBAS-InSAR) method and the interferogram stacking (stacking-InSAR) method to obtain a deformation map in the study area, using statistical analysis and spatial cluster analysis to extract deformation regions and landslide polygons to propose a landslide screening model (LSM) based on multivariate features to screen landslides and reduce the interference of noise in landslide identification, in addition to proposing a landslide gradation model (LGM) based on signum function to grade the identified landslides and provide support to distinguish landslides with different deformation degrees. The method was applied to landslide identification in the upper section of the Jinsha River basin, and 47 potential landslides were identified, including 15 high-risk landslides and 13 landslides endangering villages. The experimental results show that the proposed method can identify landslides accurately and hierarchically in low-coherence areas, providing support for geological hazard investigation agencies and local departments.

关键词： InSAR landslide identification statistical analysis spatial cluster analysis low coherence Sentinel-1

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A comparative study of the performances of joint RFE with machine learning algorithms for extracting Moso bamboo (Phyllostachys pubescens) forest based on UAV hyperspectral images

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GEOCARTO intERNATIONAL 2023年第1期38卷

作者： Li, Yi-fan Xu, Zhang-hua Hao, Zhen-bang Yao, Xiong Zhang, Qi Huang, Xu-ying Li, Bin He, An-qi Li, Zeng-lu Guo, Xiao-yu Fuzhou Univ Acad Geog & Ecol Environm Coll Environm & Safety Engn Fuzhou Peoples R China Fujian Prov Key Lab Resources & Environm Monitorin Sanming Peoples R China Fujian Agr & Forestry Univ Coll Forestry Fuzhou Peoples R China Fujian Univ Technol Coll Architecture & Planning Fuzhou Peoples R China Fuzhou Univ Acad Digital China Minist Educ Key Lab Spatial Data Min & Informat Sharing Fuzhou Peoples R China Nanjing Univ Int Inst Earth Syst Sci Nanjing Peoples R China SEGi Univ Kota Damansara Malaysia

Unmanned aerial vehicle (UAV) hyperspectral remote sensing technology has developed rapidly in recent years, providing a new scheme for intelligent monitoring of forest resources. This study aims to deeply mining the UAV hyperspectral information, and construct the best model for extracting Moso bamboo (Phyllostachys pubescens) forest information. Firstly, the spectral information features of Moso bamboo, broadleaf, bare area and shadow were obtained and their spectral anisotropies were analyzed. Secondly, multiple machine learning algorithms were combined with the recursive feature elimination (RFE) to obtain three sets of optimized feature subsets. Eventually, three sets of optimization features were substituted into multiple machine learning algorithms to build nine models of Moso bamboo information extraction, comparing their classification effects and generalization capabilities. The results showed that: (1) The subset of features optimized by different base classifiers in combination with RFE and the ranking results of optimized features' importance were different, but all the three sets of optimized feature subsets showed relatively obvious feature difference patterns;(2) The nine models based on SVM-RFE, RF-RFE and XGBoost-RFE were all capable of extracting Moso bamboo information, among which, the classification effect of the XGBoost-RFE-XGBoost model was the best and had significant advantages, with the overall classification accuracy OA, Kappa coefficient and R (2) of 98.75%, 0.983 and 0.980, respectively, and the classification accuracy for Moso bamboo was 98.34%. Thus, XGBoost was the most applicable algorithm for feature optimization in combination with RFE, and the most effective algorithm with a better generalization ability for extracting Moso bamboo information from UAV hyperspectral images. This study is crucial for Moso bamboo's large area monitoring and fine identification, providing a technical reference for obtaining information about the spat

关键词： Recursive feature elimination (RFE) machine learning algorithms Moso bamboo forest UAV hyperspectral remote sensing feature optimization

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