Photogrammetric Engineering and Remote Sensing最新文献_第2页

Evaluating Surface Mesh Reconstruction Using Real Data 使用真实数据评估表面网格重建

4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2023-10-01 DOI: 10.14358/pers.23-00007r3

Yanis Marchand, Laurent Caraffa, Raphael Sulzer, Emmanuel Clédat, Bruno Vallet

{"title":"Evaluating Surface Mesh Reconstruction Using Real Data","authors":"Yanis Marchand, Laurent Caraffa, Raphael Sulzer, Emmanuel Clédat, Bruno Vallet","doi":"10.14358/pers.23-00007r3","DOIUrl":"https://doi.org/10.14358/pers.23-00007r3","url":null,"abstract":"Surface reconstruction has been studied thoroughly, but very little work has been done to address its evaluation. In this article, we propose new visibility-based metrics to assess the completeness and accuracy of three-dimensional meshes based on a point cloud of higher accuracy than the one from which the reconstruction has been computed. We use the position from which each high-quality point has been acquired to compute the corresponding ray of free space. Based on the intersections between each ray and the reconstructed surface, our metrics allow evaluating both the global coherency of the reconstruction and the accuracy at close range. We validate this evaluation protocol by surveying several open-source algorithms as well as a piece of licensed software on three data sets. The results confirm the relevance of assessi ng local and global accuracy separately since algorithms sometimes fail at guaranteeing both simultaneously. In addition, algorithms making use of sensor positions perform better than the ones relying only on points and normals, indicating a potentially significant added value of this piece of information. Our implementation is available at https://github.com/umrlastig/SurfaceReconEval.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135367685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The FABDEM Outperforms the Global DEMs in Representing Bare Terrain Heights FABDEM在表示裸地高度方面优于Global dem

4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2023-10-01 DOI: 10.14358/pers.23-00026r2

Nahed Osama, Zhenfeng Shao, Mohamed Freeshah

{"title":"The FABDEM Outperforms the Global DEMs in Representing Bare Terrain Heights","authors":"Nahed Osama, Zhenfeng Shao, Mohamed Freeshah","doi":"10.14358/pers.23-00026r2","DOIUrl":"https://doi.org/10.14358/pers.23-00026r2","url":null,"abstract":"Many remote sensing and geoscience applications require a high-precision terrain model. In 2022, the Forest And Buildings removed Copernicus digital elevation model (FABDEM) was released, in which trees and buildings were removed at a 30 m resolution. Therefore, it was necessary to make a comprehensive evaluation of this model. This research aims to perform a qualitative and quantitative analysis of fabdem in comparison with the commonly used global dems. We investigated the effect of the terrain slope, aspect, roughness, and land cover types in causing errors in the topographic representation of all dems. The fabdem had the highest overall vertical accuracy of 5.56 m. It was the best dem in representing the terrain roughness. The fabdem and Copernicus dem were equally influenced by the slopes more than the other models and had the worst accuracy of slope representation. In the tree, built, and flooded vegetation areas of the fabdem, the mean errors in elevation have been reduced by approximately 3.34 m, 1.26 m and 1.55 m, respectively. Based on Welch's t-test, there was no significant difference between fabdem and Copernicus dem elevations. However, the slight improvements in the fabdem make it the best filtered dem to represent the terrain heights over different land cover types.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135367684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GIS Tips & Tricks GIS提示技巧

4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2023-08-01 DOI: 10.14358/pers.89.8.461

Al Karlin

引用次数: 0

Application of Improved YOLO V5s Model for Regional Poverty Assessment Using Remote Sensing Image Target Detection 改进YOLO V5s模型在遥感影像目标检测区域贫困评估中的应用

4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2023-08-01 DOI: 10.14358/pers.23-00005r3

Zhang Chenguang, Teng Guifa

{"title":"Application of Improved YOLO V5s Model for Regional Poverty Assessment Using Remote Sensing Image Target Detection","authors":"Zhang Chenguang, Teng Guifa","doi":"10.14358/pers.23-00005r3","DOIUrl":"https://doi.org/10.14358/pers.23-00005r3","url":null,"abstract":"This study aims at applying the improved You Only Look Once V5s model for the assessment of regional poverty using remote sensing image target detection. The model was improved from structure, algorithm, and components. Objects in the remote sensing images were used to identify poverty, and the poverty alleviation situation could be predicted according to the existing detection results. The results showed that the values of Precision, Recall, mean Average Precision (mAP)@0.5, and mAP@0.5:0.95 of the model increased 7.3%, 0.7%, 1%, and 7.2%, respectively on the Common Objects in Context data set in the detection stage; the four values increased 3.1%, 2.2%, 1.3%, and 5.7%, respectively on the custom remote sensing image data set in the verification stage. The loss values decreased 2.6% and 37.4%, respectively, on the two data sets. Hence, the application of the improved model led to the more accurate detection of the targets. Compared with the other papers, the improved model in this paper proved to be better. Artificial poverty alleviation can be replaced by remote sensing image processing because it is inexpensive, efficient, accurate, objective, does not require data, and has the same evaluation effect. The proposed model can be considered as a promising approach in the assessment of regional poverty.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136161543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Grids and Datums Update: This month we look at the Republic of Botswana 网格和基准更新:本月我们关注博茨瓦纳共和国

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2022-02-01 DOI: 10.14358/pers.88.2.87

C. Mugnier

引用次数: 0

Estimating the Aboveground Biomass of Urban Trees by Combining Optical and Lidar Data: A Case Study of Hengqin, Zhuhai, China 基于光学和激光雷达数据的城市树木地上生物量估算——以珠海横琴为例

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2022-02-01 DOI: 10.14358/pers.21-00045r2

Linze Bai, Q. Cheng, Yuxuan Shu, Sihang Zhang

引用次数: 0

GIS Tips & Tricks—Sometimes You Need to Turn the World Upside-Down GIS提示和技巧-有时你需要把世界颠倒过来

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2022-02-01 DOI: 10.14358/pers.88.2.83

Alma M. Karlin

引用次数: 0

Spatiotemporal Temperature Fusion Based on a Deep Convolutional Network 基于深度卷积网络的时空温度融合

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2022-02-01 DOI: 10.14358/pers.21-00023r2

Xuehan Wang, Z. Shao, Xiao Huang, D. Li

引用次数: 1

Three-Dimensional Point Cloud Analysis for Building Seismic Damage Information 建筑震害信息的三维点云分析

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2022-02-01 DOI: 10.14358/pers.21-00019r3

Fan Yang, Zhiwei Fan, Chao Wen, Xiaoshan Wang, Xiaoli Li, Zhiqiang Li, Xintao Wen, Zhanyu Wei

{"title":"Three-Dimensional Point Cloud Analysis for Building Seismic Damage Information","authors":"Fan Yang, Zhiwei Fan, Chao Wen, Xiaoshan Wang, Xiaoli Li, Zhiqiang Li, Xintao Wen, Zhanyu Wei","doi":"10.14358/pers.21-00019r3","DOIUrl":"https://doi.org/10.14358/pers.21-00019r3","url":null,"abstract":"Postearthquake building damage assessment requires professional judgment; however, there are factors such as high workload and human error. Making use of Terrestrial Laser Scanning data, this paper presents a method for seismic damage information extraction. This new method is based\u0000 on principal component analysis calculating the local surface curvature of each point in the point cloud. Then use the nearest point angle algorithm, combined with the data features of the actual measured value to identify point cloud seismic information, and filter the points that tend to\u0000 the plane by setting the threshold value. Based on the statistical analysis of the normal vector, the raw point cloud data are deplanarized to obtain the preliminary results of seismic damage information. The density clustering algorithm is used to denoise the initially extracted seismic damage\u0000 information. Ultimately, we can obtain the distribution patterns and characteristics of cracks in the walls of the building. The extraction result of the seismic damage information point cloud data is compared with the photos collected at the site, showing that the algorithm steps successfully\u0000 identify the crack and shed wall skin information recorded in the site photos (identification rate: 95%). Point cloud distribution maps of cracked and shed siding areas determine quantitative information on seismic damage, providing a higher level of performance and detail than direct contact\u0000 measurements.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90519162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effectiveness of Deep Learning Trained on SynthCity Data for Urban Point-Cloud Classification 基于SynthCity数据训练的深度学习在城市点云分类中的有效性

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2022-02-01 DOI: 10.14358/pers.21-00021r2

Steven Spiegel, Casey Shanks, Jorge Chen

引用次数: 0