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Simulated analysis of the characteristics of SAR signal attenuation in forest areas

REMOTE SENSING LETTERS 2023年第7期14卷 681-691页

作者： Wang, Jian Fu, Wenxue Su, Tao Li, Xinwu Chinese Acad Sci 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 Anhui Univ Sci & Technol Sch Spatial Informat & Geomatics Engn Huainan Peoples R China Chinese Acad Sci Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China

To fully understand the contribution of individual components of the forest to electromagnetic wave attenuation, in this work, the forest is simplified to a volume layer consisting of randomly distributed leaves and branches. We calculated the specific attenuation and skin depths of broadleaves, coniferous needles and branches using generalized Rayleigh-Gans (GRG) approximation and finite length lossy dielectric cylinder scattering models, as well as Foldy-Lax multiple scattering and optical theories. The results indicate that the specific attenuation of a single scatterer is related to the scatterer size and incident angle. For a forest volume layer, the attenuation contributions of leaves and branches were investigated, and the results show that the attenuation of leaves is more significant in the high-frequency domain. Subsequently, the skin depth changes with frequencies and scatterer density was analysed. At L-band (1.3 GHz), a monostatic SAR system will be unable to acquire underlying ground information if the height of the forest volume layer is greater than 31.1 m when the broadleaf number density is 25. At last, the variations in the attenuation of the polarization waves with the permittivity of scatterers and leaf shape were also discussed.

关键词： Frequency domain analysis

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Fast and Accurate Road Crack Detection Based on Adaptive Cost-Sensitive Loss Function

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第2期53卷 1051-1062页

作者： Li, Kai Wang, Bo Tian, Yingjie Qi, Zhiquan Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Univ Int Business & Econ Sch Informat Technol & Management Beijing 100029 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Numerous detection problems in computer vision, including road crack detection, suffer from exceedingly foreground-background imbalance. Fortunately, modification of loss function appears to solve this puzzle once and for all. In this article, we propose a pixel-based adaptive weighted cross-entropy (WCE) loss in conjunction with Jaccard distance to facilitate high-quality pixel-level road crack detection. Our work profoundly demonstrates the influence of loss functions on detection outcomes and sheds light on the sophisticated consecutive improvements in the realm of crack detection. Specifically, to verify the effectiveness of the proposed loss, we conduct extensive experiments on four public databases, that is, CrackForest, AigleRN, Crack360, and BJN260. Compared to the vanilla WCE, the proposed loss significantly speeds up the training process while retaining the performance.

关键词： Roads Training Costs Image edge detection Training data Sampling methods Adaptation models Crack detection Jaccard distance U-Net weighted cross-entropy (WCE)

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Urban spatial location service prediction algorithm based on fast adaptive genetic algorithm-least squares support vector machine under the background of internet of Things

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第13期34卷 e5946-e5946页

作者： Xia, Xiangli Cheng, Wei Yang, Liu Chongqing Technol & Business Univ Rongzhi Coll Chongqing Key Lab Spatial Data Min & Big Data Int Chongqing Peoples R China Chongqing Univ Technol Coll Comp Sci & Engn Chongqing 400050 Peoples R China

With the hot issues such as smart city and ecological city put forward, the development of intelligence and informatization of urban space has been established, especially the internet of Things. With the wide application of location-based social networks, users can share their location of interest in location. By analyzing users' historical geographic information, location service recommendation can recommend geographic locations to users to help users obtain better access experience. Combined with genetic algorithm (GA) algorithm, a recommendation algorithm based on geographic location service optimization is proposed, which can better recommend to users. Aiming at the problem of slow convergence speed of GA, a fast adaptive genetic algorithm (FAGA) method is proposed to optimize location services. In the experimental part, comparing several functions, FAGA's test effect and convergence are ideal. By comparing FAGA-least squares support vector machine (LSSVM) algorithm with other methods in location service recommendation, FAGA-LSSVM method has more advantages.

关键词： FAGA-LSSVM fast adaptive genetic algorithm genetic algorithm internet of Things location services location-based social networks

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Sequence Fusion Algorithm of Tumor Gene Sequencing and Alignment Based on Machine Learning

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COMPUTATIONAL intELLIGENCE AND NEUROSCIENCE 2021年第1期2021卷 9444194页

作者： Tang, Chao Luo, Ling Xu, Yu Chen, Guobin Tang, Li Wang, Ying Wu, Yongzhong Shi, Xiaolong Chongqing Univ Canc Hosp & Chongqing Canc Inst & Chongqing Canc Hosp Radiat & Canc Biol LabRadiat Oncol CtrChongqing Chongqing 400030 Peoples R China Chongqing Univ Canc Hosp & Chongqing Canc Inst & Chongqing Canc Hosp Dept Internal Med Chongqing 400030 Peoples R China Chongqing Univ Coll Bioengn Chongqing Peoples R China Chongqing Finance & Econ Coll Chongqing Key Lab Spatial Data Min & Big Data Int Chongqing 401320 Peoples R China

With the rapid development of DNA high-throughput testing technology, there is a high correlation between DNA sequence variation and human diseases, and detecting whether there is variation in DNA sequence has become a hot research topic at present. DNA sequence variation is relatively rare, and the establishment of DNA sequence sparse matrix, which can quickly detect and reason fusion variation point, has become an important work of tumor gene testing. Because there are differences between the current comparison software and mutation detection software in detecting the same sample, there are errors between the results of derivative sequence comparison and the detection of mutation. In this paper, SNP and InDel detection methods based on machine learning and sparse matrix detection are proposed, and VarScan 2, Genome Analysis Toolkit (GATK), BCFtools, and FreeBayes are compared. In the research of SNP and InDel detection with intelligent reasoning, the experimental results show that the detection accuracy and recall rate are better when the depth is increasing. 2e reasoning fusion method proposed in this paper has certain advantages in comparison effect and discovery in SNP and InDel and has good effect on swelling and pain gene detection.

关键词： DNA sequences

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A rapid soil Chromium pollution detection method based on hyperspectral remote sensing data

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intERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2024年 128卷

作者： Chen, Lihan Tan, Kun Wang, Xue Chen, Yu China Univ Min & Technol Key Lab Land Environm & Disaster Monitoring NASG Xuzhou 221116 Peoples R China East China Normal Univ Key Lab Geog Informat Sci Minist Educ 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

Chromium (Cr) pollution in soil can cause serious harm to both human health and the environment. Cr (VI) is of particular concern due to its strong carcinogenic, teratogenic, and mutagenic effects on humans. It can also lead to soil hardening and crop yield reduction. Recently, hyperspectral approaches have become a popular way of analyzing heavy metal pollution in soil, and are both convenient and cost-effective, compared with chemical analysis. However, the complex composition of soil makes it challenging to directly analyze the influence of heavy metals on soil spectra and achieve efficient and rapid identification of heavy metal contamination in soil. In this research, a simulation experiment was designed for soil Cr content. Threshold detection-Hilbert-Huang transform (TD-HHT) was implemented to remove the complex environmental noise of soil spectra, to extract the weak heavy metal information. The results showed that the TD-HHT amplitude fluctuates at different Cr concentrations, and heavy metal pollution can be directly detected from the amplitude curve at around 600 mg/ kg. To verify this threshold value, the correlation between the soil optical constant and the soil heavy metal concentration was explored through the bidirectional reflectance distribution function, based on the threshold validation-Hapke (TV-Hapke) model. At around 600 mg/kg, Cr can be directly detected using the soil radiative transfer model, which can be utilized to rapidly detect soil heavy metal pollution.

关键词： Hyperspectral remote sensing Heavy metal pollution Spectral threshold detection Hilbert-Huang transform Hapke model

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Emergency Decision Model by Combining Preference Relations with Trapezoidal Pythagorean Fuzzy Probabilistic Linguistic Priority Weighted Averaging PROMETHEE Approach

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intERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第6期13卷 834-845页

作者： Xiao Yue Li Jianhui Liu Zhancen Chen Dan Chongqing Finance & Econ Coll Chongqing Iflytek Artificial Intelligence Chongqing Key Lab Spatial Data Min & Big Data Int Chongqing 401320 Peoples R China Chongqing Finance & Econ Coll Chongqing Peoples R China

The outbreak of COVID-19 in 2019 has brought greater international attention to emergency decision making and management. Since emergency situations are often uncertain, prevention and control are crucial. For better prevent and control, according to the characteristics of emergency incidents, the paper proposes a new form of linguistic expression trapezoidal Pythagorean fuzzy probabilistic linguistic variables to express decision-making information. Next, the paper develops the operational rules, value index and ambiguity of trapezoidal Pythagorean fuzzy probabilistic linguistic variables. Then, the new trapezoidal Pythagorean fuzzy probabilistic linguistic priority weighted averaging PROMETHEE approach is introduced to aggregate the trapezoidal Pythagorean fuzzy probabilistic linguistic information combining with preference relation. Finally, an emergency decision making case of prevention of infectious diseases analysis illustrate the necessity and effectiveness of this method, the results of comparative and experimental analyses demonstrate that the constructed new trapezoidal Pythagorean fuzzy probabilistic linguistic priority weighted averaging PROMETHEE approach owns better performances in terms of effectiveness and reasonability.

关键词： COVID-19 emergency decision model trapezoidal Pythagorean fuzzy probabilistic linguistic variables preference relations PROMETHEE approach

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Changes in the Water Area of an Inland River Terminal Lake (Taitma Lake) Driven by Climate Change and Human Activities, 2017-2022

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REMOTE SENSING 2024年第10期16卷 1703页

作者： Zi, Feng Wang, Yong Lu, Shanlong Ikhumhen, Harrison Odion Fang, Chun Li, Xinru Wang, Nan Kuang, Xinya Hunan Univ Sci & Technol Sch Earth Sci & Spatial Informat Engn Xiangtan 411201 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Southwest Univ Sch Geog Sci Chongqing 400715 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Constructed from a dataset capturing the seasonal and annual water body distribution of the lower Qarqan River in the Taitma Lake area from 2017 to 2022, and combined with the meteorological and hydraulic engineering data, the spatial and temporal change patterns of the Taitma Lake watershed area were determined. Analyses were conducted using Planetscope (PS) satellite images and a deep learning model. The results revealed the following: Deep learning-based water body extraction provides significantly greater accuracy than the conventional water body index approach. With an impressive accuracy of up to 96.0%, UPerNet was found to provide the most effective extraction results among the three convolutional neural networks (U-Net, DeeplabV3+, and UPerNet) used for semantic segmentation; Between 2017 and 2022, Taitma Lake's water area experienced a rapid decrease, with the distribution of water predominantly shifting towards the east-west direction more than the north-south. The shifts between 2017 and 2020 and between 2020 and 2022 were clearly discernible, with the latter stage (2020-2022) being more significant than the former (2017-2020); According to observations, Taitma Lake's changing water area has been primarily influenced by human activity over the last six years. Based on the research findings of this paper, it was observed that this study provides a valuable scientific basis for water resource allocation aiming to balance the development of water resources in the middle and upper reaches of the Tarim and Qarqan Rivers, as well as for the ecological protection of the downstream Taitma Lake.

关键词： climate change human activities Planetscope water area deep learning Taitma Lake

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Nighttime light in China's coastal zone: The type classification approach using SDGSAT-1 Glimmer Imager

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REMOTE SENSING OF ENVIRONMENT 2024年 305卷

作者： Jia, mingming Zeng, Haihang Chen, Zuoqi Wang, Zongming Ren, Chunying Mao, Dehua Zhao, Chuanpeng Zhang, Rong Wang, Yeqiao Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun 130102 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Fuzhou Univ Natl & Local Joint Engn Res Ctr Satellite Geospati Key Lab Spatial Data Min & Informat Sharing Minist Educ Fuzhou Peoples R China Fuzhou Univ Acad Digital China Fuzhou 350002 Peoples R China Univ Rhode Isl Dept Nat Resources Sci Kingston RI 02881 USA

Nighttime Light (NTL) is highly concentrated in China ' s coastal zone, leading to negative health impacts on both humans and wildlife. Particularly, in recent years, the widespread adoption of broad-spectrum Light -Emitting Diode (LED) light, a low -carbon technology providing substantial increases in luminosity, has led to certain ecological consequences. Thus, information regarding spatial distribution and composition of different NTL types is essential for formulating sustainable strategies that balance nighttime public security, energy consumption, and ecosystem conservation. However, the availability of such information remains limited. To address this challenge and meet the demand, we developed two new light indices, namely the Ratio Red Light Index (RRLI) and Ratio Blue Light Index (RBLI), based on SDGSAT-1 Glimmer Imager (GLI) multispectral NTL data. We then proposed a threshold method and applied it to the entire coastal zone of China to identify White LED (WLED), Red LED (RLED), and Other lights (Other). Results showed the following. (1) In the coastal zone of China, the total lighting area was 20,517 km 2 , including 20,257 km 2 of terrestrial lights and 260 km 2 of offshore lights;(2) WLED light covered 67% (13,727 km 2 ) of all lighting areas, while RLED lights accounted for only 1% (220 km 2 );(3) Guangdong had the largest lighting area (5221 km 2 ), with the proportion of WLEDs being the highest among all coastal provinces (almost 90%);(4) The proportions of lighting areas were relatively low in Guangxi, Liaoning, and Hebei. This study represents the first attempt to identify NTL types over large regions at a finer spatial resolution. The approach proposed, including the light indices of RRLI and RBLI, as well as the defined thresholds, is universal and robust for use in NTL type classification. The developed lighting type map, containing comprehensive information on the spatial distribution and composition of NTL, could facilitate the sustainable ma

关键词： Nighttime light (NTL) SDGSAT-1 GLI Light index Light -emitting diode (LED) China 's coastal zone

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IMG2HEIGHT: height estimation from single remote sensing image using a deep convolutional encoder-decoder network

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intERNATIONAL JOURNAL OF REMOTE SENSING 2023年第18期44卷 5686-5712页

作者： Du, Shouhang Xing, Jianghe Du, Shihong Cui, Ximin Xiao, Xiongwu Li, Wei Wang, Shaoyu China Univ Min & Technol Beijing Coll Geosci & Surveying Engn Beijing Peoples R China Shanghai Surveying & Mapping Inst Key Lab Spatial Temporal Big Data Anal & Applicat Shanghai Peoples R China Peking Univ Inst Remote Sensing Beijing Peoples R China Peking Univ GIS Beijing Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan Peoples R China

Height estimation from single remote sensing image is a challenging inherently ambiguous and technically ill-posed problem that we address in this study by resorting to deep learning approach. A spatial enhanced and multi-scale aggregated encoder-decoder network, SM-EDNet, is proposed, which takes a single image as input and produces an estimated height map as output. First, residual network (ResNet) is applied to extract low-level and deep features to cope with the heterogeneous characteristics of remote sensing scenes. Then, the multi-scale context information is aggregated through DenseASPP (Dense Atrous spatial Pyramid Pooling) by extracting features from multiple dilated convolution layers. The skip connection is constructed by using the structure preserving model, DULR, to aggregate ResNet low-level features and multi-scale high-level features. The deformable convolution module is constructed to enhance the sensitivity to differences in geometric shapes of ground objects. For model training, three-layer deep supervision mechanism is designed to counteract the adverse effects of unstable gradients changes. Experimental results on three benchmark datasets, including ISPRS Vaihingen, ISPRS Potsdam, and DFC2018, show that the proposed method achieves the most outstanding performance compared with the state-of-the-art networks. The source codes are available at: https://***/xjh0929/2HEIGHT.

关键词： Single remote sensing image height estimation encoder-decoder network DenseASPP deep supervision mechanism ResNet

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Provincial localization framework for SDGs in China: Enhancing support for sustainable governance

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APPLIED GEOGRAPHY 2025年 175卷

作者： Song, Wen Cao, Shisong Du, mingyi Lu, Linlin Guo, Huadong Wang, Sirui Liu, Yue Li, Xiaoxuan Beijing Univ Civil Engn & Architecture Sch Geomat & Urban Spatial Informat Beijing 100044 Peoples R China Minist Nat Resources Key Lab Urban Spatial Informat Beijing 100044 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China

The Sustainable Development Goals (SDGs) represent a globally accepted set of objectives, whose success heavily relies on localized implementation. Despite broad consensus, there is a notable lack of structured methodologies for assessing SDG integration at the local level. To address the knowledge gap, we proposed a novel framework for evaluating SDG integration at the provincial level, supported by an in-depth analysis of Chinese provinces from 2006 to 2021. Our framework allows for a more nuanced assessment by selecting regionally aligned indicators, establishing clear benchmarks, and evaluating SDG performance across multiple dimensions. A key finding is the necessity of utilizing at least three distinct indicators to capture the complexity of each SDG. Moreover, our approach-combining average targets with regional equity considerations-produces more consistent and meaningful results for local governance. By 2015, China's provincial SDG implementation surpassed key milestones, but regressions were observed in SDG12 (responsible consumption and production), SDG13 (climate action), SDG14 (life below water), and SDG15 (life on land), underscoring the need for renewed efforts. This research distinguishes itself by offering a localized, equity-driven assessment model that provides fresh insights into SDG progress, emphasizing the importance of continuous monitoring at sub-national levels and the need for tailored policy interventions to address regional disparities in sustainable development.

关键词： SDGs Localization Assessment rules Regional sustainability China

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