ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences最新文献_第7页

SHORELINE EXTRACTION USING TIME SERIES OF SENTINEL-2 SATELLITE IMAGES BY GOOGLE EARTH ENGINE PLATFORM 基于Google earth引擎平台的sentinel-2卫星时间序列岸线提取

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-653-2023

E. Rostami, M. A. Sharifi, M. Hasanlou

{"title":"SHORELINE EXTRACTION USING TIME SERIES OF SENTINEL-2 SATELLITE IMAGES BY GOOGLE EARTH ENGINE PLATFORM","authors":"E. Rostami, M. A. Sharifi, M. Hasanlou","doi":"10.5194/isprs-annals-x-4-w1-2022-653-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-653-2023","url":null,"abstract":"Abstract. In recent decades, global warming and sea level rise, population growth, and intensification of human activities, have directly affected the coasts and as such, their monitoring for the accretion and retreat are among the issues that are considered by the coastal countries. This study, compares two supervised classification algorithms for classifying Sentinel-2 satellite imagery for shoreline extraction. Median monthly images from 2020/01 to 2021/12 are taken and classified by Random Forest (RF) and Support Vector Machine (SVM) algorithms. By validating the maps, it is found that the RF algorithm has better accuracy and as such by averaging the accuracy of all maps, the overall accuracy (OA) values of 97.18% and the kappa coefficient (KC) of 0.97, and the mean overall accuracy and kappa coefficient of maps from SVM algorithm of 85.15% and 0.79, respectively, is obtained. After extracting the shorelines, the Digital Shoreline Analysis System (DSAS) is used to calculate the displacement rate. By calculating the Linear Regression Rate (LRR) factor, it is found that in 91% of transects (166 transects) we see the shoreline retreat to land. In 54% of them, the average rate of the retreat is 5.42 meters per year and in only 9% (16 transects) we see the accretion towards the sea.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91237047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NEW AND RAPID METHODS FOR GRIDDING OF POINT CLOUDS 点云网格化的快速新方法

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-437-2023

A. Mahphood, H. Arefi

引用次数: 0

EVALUATION OF DIFFERENT PARAMETERS FOR PLANT CLASSIFICATION BY PRE-TRAINED DEEP LEARNING MODELS WITH BIGEARTHNET DATASET 基于bigearthnet数据集的预训练深度学习模型对植物分类不同参数的评价

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-569-2023

F. Naali, T. Alipour-Fard, H. Arefi

{"title":"EVALUATION OF DIFFERENT PARAMETERS FOR PLANT CLASSIFICATION BY PRE-TRAINED DEEP LEARNING MODELS WITH BIGEARTHNET DATASET","authors":"F. Naali, T. Alipour-Fard, H. Arefi","doi":"10.5194/isprs-annals-x-4-w1-2022-569-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-569-2023","url":null,"abstract":"Abstract. Vegetation monitoring and mapping are essential for a diverse range of environmental problems such as forest management, food resources, and climate change assessment. Several methods have been developed to classify different vegetation types based on remote sensing (RS) data. Land use classification has been revolutionized with the advent of neural networks. Various vegetation types were classified using multispectral Sentinel-2 satellite images due to their high spatial resolution and spectral information. Deep Convolutional Neural Network is considered a promising method for classifying remote sensing images with high spatial resolution due to its powerful feature extraction capabilities. However, large labeled datasets are required for better classification performance, so we have used pre-trained ResNet networks with 152 layers, 50 layers, and 101 layers trained on Big Earth Net (BEN). In order to obtain the best network performance and evaluate the sensitivity of the parameters in this study, we have performed two experiments: 1) the effect of different patch sizes and 2) increasing the number of images. The results demonstrate that ResNet 152 shows the highest accuracy with patches of 120 × 120 pixels, with an accuracy of 76.62%, and ResNet 50 is the best with an accuracy of 76.2% since the process of this network does not take much time.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85012526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MAPPING OF URBAN FLOOD INUNDATION USING 3D DIGITAL SURFACE MODEL AND SENTINEL-1 IMAGES 基于三维数字地表模型和sentinel-1图像的城市洪水淹没制图

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-715-2023

M. Sharif, S. Heidari, S. M. Hosseini

{"title":"MAPPING OF URBAN FLOOD INUNDATION USING 3D DIGITAL SURFACE MODEL AND SENTINEL-1 IMAGES","authors":"M. Sharif, S. Heidari, S. M. Hosseini","doi":"10.5194/isprs-annals-x-4-w1-2022-715-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-715-2023","url":null,"abstract":"Abstract. Flooding in urban areas poses serious risks to citizens, infrastructures, and transportation. Precise and real-time delineation of the inundated areas is crucial for a better understanding of the extent of damage and high-risk areas and people evacuation actions. It also increases citizens' awareness that living in areas with high flood risk. Yazd city is characterized by low rainfall (<70 mm/yr) and the desert climate is considered the study area of this research. This city encountered a flash flood event that was generated by severe rainfall with a depth of 75 mm in 3hr (i.e., the intensity of 25 mm/hr) on July 29, 2022. Many strategic infrastructures of this city especially the railway station were flooded, which caused heavy casualties and financial losses. This study aims to monitor the flood inundated areas of Yazd city due to this flood event using remote sensing. In this research, the Sentinel-1 polarimetric radar images and the 3D model of the Yazd city surf ace were used to delineate the flooded areas. The field information of the flooded areas and the available Sentinel-1 images during or near the occurrence time of maximum flood extension were adopted. The Convolutional Neural Network (CNN) model in combination with the 3D model of the studied area was used to identify the flooded pixels in the city of Yazd. The results showed that the adopted 3D model and CNN algorithm indicated a good ability to identify flooded areas with an accuracy of 88% and a kappa coefficient of 0.83.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86101840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MEASURING SPATIAL ACCESSIBILITY TO HEALTHCARE FACILITIES IN ISFAHAN METROPOLITAN AREA IN IRAN 衡量伊朗伊斯法罕大都市区卫生保健设施的空间可达性

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-623-2023

H. Rabiei-Dastjerdi, S. Mohammadi, R. Samouei, M. Kazemi, S. Matthews, G. Mcardle, S. Homayouni, B. Kiani, R. Sadeghi

{"title":"MEASURING SPATIAL ACCESSIBILITY TO HEALTHCARE FACILITIES IN ISFAHAN METROPOLITAN AREA IN IRAN","authors":"H. Rabiei-Dastjerdi, S. Mohammadi, R. Samouei, M. Kazemi, S. Matthews, G. Mcardle, S. Homayouni, B. Kiani, R. Sadeghi","doi":"10.5194/isprs-annals-x-4-w1-2022-623-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-623-2023","url":null,"abstract":"Abstract. Appropriate spatial accessibility to healthcare facilities is an important component of the efficient delivery of healthcare. This study aims to measure spatial accessibility to healthcare facilities in Isfahan Metropolitan Area, a rapidly growing megacity in Iran. We used two methods of population-weighted fuzzy analytic hierarchy process and the two-step floating catchment area (2SFCA) to measure spatial accessibility to urban healthcare facilities, including hospitals, pharmacies, clinics, and laboratories. The results of these two methods were compared. Not surprisingly, the center of Isfahan has the highest accessibility scores for healthcare facilities, and the peripheral areas of the city have the lowest levels. Despite the existence of higher numbers of healthcare facilities in northern parts of Isfahan, accessibility to healthcare facilities is low because of the large population. Both methods show that healthcare services are not evenly distributed in Isfahan. Some areas with a high population have low accessibility to health facilities. Ideally, these areas would be prioritized for future health infrastructure investment. The methods used here can help urban healthcare policymakers identify spatial inequalities in access to care and thus target the areas in need.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"C-17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85039726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

INTELLIGENT 3D CRACK RECONSTRUCTION USING CLOSE RANGE PHOTOGRAMMETRY IMAGERY 使用近距离摄影测量图像的智能三维裂缝重建

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-443-2023

S. Majidi, M. Omidalizarandi, M. A. Sharifi

{"title":"INTELLIGENT 3D CRACK RECONSTRUCTION USING CLOSE RANGE PHOTOGRAMMETRY IMAGERY","authors":"S. Majidi, M. Omidalizarandi, M. A. Sharifi","doi":"10.5194/isprs-annals-x-4-w1-2022-443-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-443-2023","url":null,"abstract":"Abstract. Civil infrastructure Structural Health Monitoring (SHM) and its preservation from deterioration is a crucial task. In general, natural disasters like severe earthquakes, extreme landslides, subsidence or intensive floods directly influence the health of civil structures such as buildings, bridges, roads, and dams. Evaluation and inspection of defects and damages of the aforementioned structures help to preserve them from destruction by accelerating rehabilitation and reconstruction. An automatic and precise crack detection framework is required for periodic assessment and inspection due to the large number of the structures. In this study, a two-step crack segmentation and its 3D reconstruction procedure is proposed. The crack segmentation is carried out by using Deeplabv3+ architecture and Xception as the backbone. Next, Squeeze-and-Excitation is added as an attention module to achieve higher accuracy. Integration of predicted masks and original images into a structure-from-motion procedure is additionally taken into account. In the last step, ground control points and scale bars are considered to overcome the problem of datum rank deficiency in absolute orientation through the bundle adjustment procedure in aerial triangulation. The most probable segmented cracks are overlaid on the 3D point clouds in the global coordinate system with true scales. Our network is trained based on 8000 images and their corresponding masks, leading to 69% in Intersection over Union (IoU) index. Sub-millimetre accuracy of crack reconstruction using the proposed methodology is validated with a scale bar.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90761352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HOTSPOT ANALYSIS AND COMPARISON BETWEEN SATELLITE-DERIVED AEROSOL OPTICAL DEPTH AND GROUND-BASED PARTICULATE MATTER MEASUREMENTS IN METRO MANILA 马尼拉大都会地区卫星气溶胶光学深度与地面颗粒物测量的热点分析与比较

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-639-2023

B. A. B. Recto, R. Torres, R. Ramos, A. Tamondong, M. Cayetano, B. J. D. Jiao

{"title":"HOTSPOT ANALYSIS AND COMPARISON BETWEEN SATELLITE-DERIVED AEROSOL OPTICAL DEPTH AND GROUND-BASED PARTICULATE MATTER MEASUREMENTS IN METRO MANILA","authors":"B. A. B. Recto, R. Torres, R. Ramos, A. Tamondong, M. Cayetano, B. J. D. Jiao","doi":"10.5194/isprs-annals-x-4-w1-2022-639-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-639-2023","url":null,"abstract":"Abstract. Highly urbanized regions such as the Metro Manila area in the Philippines contribute to the deterioration of air quality through overpopulation, excessive vehicle emissions, and industrialization. However, the limited number of ground monitoring stations hinders the detailed estimation of the region’s overall air quality. Satellite-derived air pollutant concentrations have been used in several research studies as a substitute or supplementary to ground-based data due to their extensive spatial and temporal coverage. Using the aerosol optical depth (AOD) from the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm and ground measurements of coarse particulate matter (PM10), this study explores the comparison between satellite-derived and ground-based air pollutant concentrations measured from 2017 to 2020 through trend analysis of monthly average values per city. With 16 stations located in different cities, the monthly average values of AOD vs PM10 showed inconsistent results due to significant gaps in the ground data. Through optimized hotspot analysis, it was found that 7.24% of the Metro Manila region are considered hotspots using the MAIAC AOD values from 2017 to 2019 (pre-pandemic). From 2018 to 2020 (pandemic), 23.86% of Metro Manila are counted as hotspots. The AOD derived from satellite imagery and hotspot analysis can be used for future studies that focus on the development of models to predict ground pollutant values and the designation of non-attainment areas.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88948324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SEGMENTATION OF ELECTRICAL SUBSTATIONS USING DEEP CONVOLUTIONAL NEURAL NETWORK 基于深度卷积神经网络的变电站分割

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-495-2023

M. Mesvari, R. Shah-Hosseini

引用次数: 0

EVALUATION OF ENVIRONMENTAL FACTORS IN SMART HOMES IMPLEMENTATION (CASE STUDY: CITIES OF MAZANDARAN PROVINCE IN IRAN) 智能家居实施中的环境因素评估(案例研究:伊朗马赞达兰省城市)

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-563-2023

S. Motevalli, S. Seyedian

{"title":"EVALUATION OF ENVIRONMENTAL FACTORS IN SMART HOMES IMPLEMENTATION (CASE STUDY: CITIES OF MAZANDARAN PROVINCE IN IRAN)","authors":"S. Motevalli, S. Seyedian","doi":"10.5194/isprs-annals-x-4-w1-2022-563-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-563-2023","url":null,"abstract":"Abstract. The growing trend of IT technologies has continued at a faster pace in recent decades. The efforts of experts in this field have always been reducing the problems and challenges existing in urban communities and improving the quality of citizens' lives. They have concentrated on using the artificial intelligence and Internet of Things in a way which leads to maximize self-actualization of citizens. Among these technologies, IoT has played a significant role. Since it can improve the urban infrastructures, and also can be implemented in the scale of home facilities and equipment, can improve the quality of life in smart homes. However, it seems that the implementation of smart residential buildings in the context of smart cities is not simple as been defined in theory and may be accompanied by challenges. In this paper, an attempt has been made to evaluate the environmental factors affecting this field by SWOT analysis tool in a case study in cities of Mazandaran Province in Iran. As a result, strategy and tactics to emphasize strengths, to make the most of existing opportunities, to eliminate the weaknesses, and to reduce the risk of perceived threats, have been identified and been introduced. Based on the results, OS strategy should be held to obtain the most valuable goals of a smart city; since the points of opportunities and strengths are relatively more than others.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"84 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86950187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ESTIMATING AIRBORNE PARTICULATE MATTER IN THE NATIONAL CAPITAL REGION, PHILIPPINES USING MULTIPLE LINEAR REGRESSION AND GRADIENT BOOSTING ALGORITHM ON MODIS MAIAC AEROSOL OPTICAL DEPTH 基于modis maiac气溶胶光学深度的多元线性回归和梯度增强算法估算菲律宾国家首都地区大气颗粒物

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-729-2023

R. Torres, R. Ramos, B. A. B. Recto, A. Tamondong, B. J. D. Jiao, M. Cayetano

{"title":"ESTIMATING AIRBORNE PARTICULATE MATTER IN THE NATIONAL CAPITAL REGION, PHILIPPINES USING MULTIPLE LINEAR REGRESSION AND GRADIENT BOOSTING ALGORITHM ON MODIS MAIAC AEROSOL OPTICAL DEPTH","authors":"R. Torres, R. Ramos, B. A. B. Recto, A. Tamondong, B. J. D. Jiao, M. Cayetano","doi":"10.5194/isprs-annals-x-4-w1-2022-729-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-729-2023","url":null,"abstract":"Abstract. The generation of air quality concentration data is imperative for the health and environment of highly urbanized regions. Through remote sensing, air pollutant concentrations can be obtained over large areas for a long time. In this study, particulate matter (PM2.5 and PM10) concentrations were estimated using satellite-derived Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Optical Depth (AOD) values observed in the National Capital Region (NCR), Philippines. Models were generated using multiple linear regression (MLR) and gradient boosting regression to determine the best models for the whole data from 2017 to 2020, dry season, and wet season with a 70–30 split for the train-test sets. Initial models resulted with the best coefficient of determination R2 values of 2.6% and 1.2% using MLR and 2.0% using gradient boosting regression. The results for PM2.5 and PM10 showed the lowest Root Mean Square Error (RMSE) values of 8.79 μg/m3 and 18.99 μg/m3 using MLR and 8.08 μg/m3 and 16.85 μg/m3 using gradient boosting, respectively. The preliminary results indicate the relatively poor performance of models in estimating particulate matter using satellite-derived AOD images. Improvements in the models will include the integration of more in-situ data from air quality monitoring stations and the addition of additional variables and features such as meteorological parameters and geographical layers.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82350091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0