Photogrammetric Engineering and Remote Sensing最新文献_第10页

A Real-Time Photogrammetric System for Acquisition and Monitoring of Three-Dimensional Human Body Kinematics 三维人体运动学的实时摄影测量系统

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-05-01 DOI: 10.14358/pers.87.5.363

Long Chen, Bo Wu, Yao Zhao, Yuan Li

{"title":"A Real-Time Photogrammetric System for Acquisition and Monitoring of Three-Dimensional Human Body Kinematics","authors":"Long Chen, Bo Wu, Yao Zhao, Yuan Li","doi":"10.14358/pers.87.5.363","DOIUrl":"https://doi.org/10.14358/pers.87.5.363","url":null,"abstract":"Real-time acquisition and analysis of three-dimensional (3D) human body kinematics are essential in many applications. In this paper, we present a real-time photogrammetric system consisting of a stereo pair of red-green-blue (RGB) cameras. The system incorporates a multi-threaded and\u0000 graphics processing unit (GPU)-accelerated solution for real-time extraction of 3D human kinematics. A deep learning approach is adopted to automatically extract two-dimensional (2D) human body features, which are then converted to 3D features based on photogrammetric processing, including\u0000 dense image matching and triangulation. The multi-threading scheme and GPU-acceleration enable real-time acquisition and monitoring of 3D human body kinematics. Experimental analysis verified that the system processing rate reached ∼18 frames per second. The effective detection distance\u0000 reached 15 m, with a geometric accuracy of better than 1% of the distance within a range of 12 m. The real-time measurement accuracy for human body kinematics ranged from 0.8% to 7.5%. The results suggest that the proposed system is capable of real-time acquisition and monitoring of 3D human\u0000 kinematics with favorable performance, showing great potential for various applications.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"25 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75263766","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

Comparative Assessment of Target-Detection Algorithms for Urban Targets Using Hyperspectral Data 基于高光谱数据的城市目标检测算法的比较评估

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-05-01 DOI: 10.14358/pers.87.5.349

Shalini Gakhar, K. C. Tiwari

{"title":"Comparative Assessment of Target-Detection Algorithms for Urban Targets Using Hyperspectral Data","authors":"Shalini Gakhar, K. C. Tiwari","doi":"10.14358/pers.87.5.349","DOIUrl":"https://doi.org/10.14358/pers.87.5.349","url":null,"abstract":"Hyperspectral data present better opportunities to exploit the treasure of spectral and spatial content that lies within their spectral bands. Hyperspectral data are increasingly being considered for exploring levels of urbanization, due to their capability to capture the spectral variability\u0000 that a modern urban landscape offers. Data and algorithms are two sides of a coin: while the data capture the variations, the algorithms provide suitable methods to extract relevant information. The literature reports a variety of algorithms for extraction of urban information from any given\u0000 data, with varying accuracies. This article aims to explore the binary-classifier approach to target detection to extract certain features. Roads and roofs are the most common features present in any urban scene. These experiments were conducted on a subset of AVIRIS-NG hyperspectral data\u0000 from the Udaipur region of India, with roads and roofs as targets. Four categories of target-detection algorithms are identified from a literature survey and our previous experience—distance measures, angle-based measures, information measures, and machine-learning measures—followed\u0000 by performance evaluation. The article also presents a brief taxonomy of algorithms; explores methods such as the Mahalanobis angle, which has been reported to be effective for extraction of urban targets; and explores newer machine-learning algorithms to increase accuracy. This work is likely\u0000 to aid in city planning, sustainable development, and various other governmental and nongovernmental efforts related to urbanization.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"22 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85499753","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—Understanding Aerial Triangulation GIS提示和技巧-了解空中三角测量

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-05-01 DOI: 10.14358/pers.87.5.319

D. Maune, A. Karlin

引用次数: 0

Cartography. A Compendium of Design Thinking for Mapmakers 制图。地图绘制者设计思维纲要

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-05-01 DOI: 10.14358/pers.87.5.322

K. Field, Adam Steer

引用次数: 0

Inversion of Solar-Induced Chlorophyll Fluorescence Using Polarization Measurements of Vegetation 利用植被偏振测量反演太阳诱导的叶绿素荧光

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-05-01 DOI: 10.14358/pers.87.5.331

Haiyan Yao, Ziying Li, Yang Han, Haofang Niu, Tianyi Hao, Yuyu Zhou

{"title":"Inversion of Solar-Induced Chlorophyll Fluorescence Using Polarization Measurements of Vegetation","authors":"Haiyan Yao, Ziying Li, Yang Han, Haofang Niu, Tianyi Hao, Yuyu Zhou","doi":"10.14358/pers.87.5.331","DOIUrl":"https://doi.org/10.14358/pers.87.5.331","url":null,"abstract":"In vegetation remote sensing, the apparent radiation of the vegetation canopy is often combined with three components derived from different parts of vegetation that have different production mechanisms and optical properties: volume scattering Lvol, polarized light Lpol,\u0000 and chlorophyll fluorescence ChlF. The chlorophyll fluorescence plays a very important role in vegetation remote sensing, and the polarization information in vegetation remote sensing has become an effective way to characterize the physical characteristics of vegetation. This study analyzes\u0000 the difference between these three types of radiation flux and utilizes polarization radiation to separate them from the apparent radiation of the vegetation canopy. Specifically, solar-induced chlorophyll fluorescence is extracted from vegetation canopy radiation data using standard Fraunhofer-line\u0000 discrimination. The results show that polarization measurements can quantitatively separate Lvol, Lpol, and ChlF and extract the solar-induced chlorophyll fluorescence. This study improves our understanding of the light-scattering properties of vegetation canopies and\u0000 provides insights for developing building models and research algorithms.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"8 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76047086","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

Scene Classification of Remotely Sensed Images via Densely Connected Convolutional Neural Networks and an Ensemble Classifier 基于密集连接卷积神经网络和集成分类器的遥感图像场景分类

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-04-01 DOI: 10.14358/PERS.87.3.295

Q. Cheng, Yuan Xu, Peng Fu, Jinling Li, Wen Wang, Y. Ren

{"title":"Scene Classification of Remotely Sensed Images via Densely Connected Convolutional Neural Networks and an Ensemble Classifier","authors":"Q. Cheng, Yuan Xu, Peng Fu, Jinling Li, Wen Wang, Y. Ren","doi":"10.14358/PERS.87.3.295","DOIUrl":"https://doi.org/10.14358/PERS.87.3.295","url":null,"abstract":"Deep learning techniques, especially convolutional neural networks, have boosted performance in analyzing and understanding remotely sensed images to a great extent. However, existing scene-classification methods generally neglect local and spatial information that is vital to scene\u0000 classification of remotely sensed images. In this study, a method of scene classification for remotely sensed images based on pretrained densely connected convolutional neural networks combined with an ensemble classifier is proposed to tackle the under-utilization of local and spatial information\u0000 for image classification. Specifically, we first exploit the pretrained DenseNet and fine-tuned it to release its potential in remote-sensing image feature representation. Second, a spatial-pyramid structure and an improved Fisher-vector coding strategy are leveraged to further strengthen\u0000 representation capability and the robustness of the feature map captured from convolutional layers. Then we integrate an ensemble classifier in our network architecture considering that lower attention to feature descriptors. Extensive experiments are conducted, and the proposed method achieves\u0000 superior performance on UC Merced, AID, and NWPU-RESISC45 data sets.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"87 1","pages":"295-308"},"PeriodicalIF":1.3,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47399327","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: 2

A Digital Terrain Modeling Method in Urban Areas by the ICESat-2 (Generating precise terrain surface profiles from photon-counting technology) ICESat-2在城市地区的数字地形建模方法（利用光子计数技术生成精确的地形表面轮廓）

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-04-01 DOI: 10.14358/PERS.87.4.237

Nahed Osama, Bisheng Yang, Yue Ma, Mohamed Freeshah

{"title":"A Digital Terrain Modeling Method in Urban Areas by the ICESat-2 (Generating precise terrain surface profiles from photon-counting technology)","authors":"Nahed Osama, Bisheng Yang, Yue Ma, Mohamed Freeshah","doi":"10.14358/PERS.87.4.237","DOIUrl":"https://doi.org/10.14358/PERS.87.4.237","url":null,"abstract":"The ICE, Cloud and land Elevation Satellite-2 (ICES at-2) can provide new measurements of the Earth's elevations through photon-counting technology. Most research has focused on extracting the ground and the canopy photons in vegetated areas. Yet the extraction of the ground photons\u0000 from urban areas, where the vegetation is mixed with artificial constructions, has not been fully investigated. This article proposes a new method to estimate the ground surface elevations in urban areas. The ICES at-2 signal photons were detected by the improved Density-Based Spatial Clustering\u0000 of Applications with Noise algorithm and the Advanced Topographic Laser Altimeter System algorithm. The Advanced Land Observing Satellite-1 PALSAR –derived digital surface model has been utilized to separate the terrain surface from the ICES at-2 data. A set of ground-truth data was\u0000 used to evaluate the accuracy of these two methods, and the achieved accuracy was up to 2.7 cm, which makes our method effective and accurate in determining the ground elevation in urban scenes.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"87 1","pages":"237-248"},"PeriodicalIF":1.3,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42851596","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: 5

GIS Tips & Tricks—Using GIS to Hunt for Easter Eggs GIS小技巧——利用GIS寻找复活节彩蛋

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-04-01 DOI: 10.14358/PERS.87.4.225

A. Karlin, K. Patterson, Carly Bradshaw, Savannah Carter, Todd Waldorf

引用次数: 0

Discovering Potential Illegal Construction Within Building Roofs from UAV Images Using Semantic Segmentation and Object-Based Change Detection 利用语义分割和基于对象的变化检测从无人机图像中发现屋顶内潜在的非法建筑

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-04-01 DOI: 10.14358/PERS.87.4.263

Yang Liu, Yujie Sun, Shikang Tao, Min Wang, Qian Shen, Jiru Huang

{"title":"Discovering Potential Illegal Construction Within Building Roofs from UAV Images Using Semantic Segmentation and Object-Based Change Detection","authors":"Yang Liu, Yujie Sun, Shikang Tao, Min Wang, Qian Shen, Jiru Huang","doi":"10.14358/PERS.87.4.263","DOIUrl":"https://doi.org/10.14358/PERS.87.4.263","url":null,"abstract":"A novel potential illegal construction (PIC) detection method by bitemporal unmanned aerial vehicle (UAV ) image comparison (change detection) within building roof areas is proposed. In this method, roofs are first extracted from UAV images using a depth-channel improved UNet model.\u0000 A two-step change detection scheme is then implemented for PIC detection. In the change detection stage, roofs with appearance, disappearance, and shape changes are first extracted by morphological analysis. Subroof primitives are then obtained by roof-constrained image segmentation within\u0000 the remaining roof areas, and object-based iteratively reweighted multivariate alteration detection (IR-MAD ) is implemented to extract the small PICs from the subroof primitives. The proposed method organically combines deep learning and object-based image analysis, which can identify entire\u0000 roof changes and locate small object changes within the roofs. Experiments show that the proposed method has better accuracy compared with the other counterparts, including the original IR-MAD, change vector analysis, and principal components analysis-K-means.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"87 1","pages":"263-271"},"PeriodicalIF":1.3,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42408808","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

Parsing of Urban Facades from 3D Point Clouds Based on a Novel Multi-View Domain 基于多视域的三维点云城市立面分析

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-04-01 DOI: 10.14358/PERS.87.4.283

Wei Wang, Yuanzi Xu, Y. Ren, Gang Wang

引用次数: 2