Photogrammetric Engineering and Remote Sensing最新文献_第5页

A Method of Extracting High-Accuracy Elevation Control Points from ICESat-2 Altimetry Data 一种从ICESat-2高程数据提取高精度高程控制点的方法

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-11-01 DOI: 10.14358/pers.21-00009r2

Binbin Li, Huan Xie, Shijie Liu, X. Tong, Hong Tang, Xu Wang

{"title":"A Method of Extracting High-Accuracy Elevation Control Points from ICESat-2 Altimetry Data","authors":"Binbin Li, Huan Xie, Shijie Liu, X. Tong, Hong Tang, Xu Wang","doi":"10.14358/pers.21-00009r2","DOIUrl":"https://doi.org/10.14358/pers.21-00009r2","url":null,"abstract":"Due to its high ranging accuracy, spaceborne laser altimetry technology can improve the accuracy of satellite stereo mapping without ground control points. In the past, full-waveform ICE, CLOUD, and Land Elevation Satellite (ICESat) laser altimeter data have been\u0000 used as one of the main data sources for global elevation control. As a second-generation satellite, ICESat-2 is equipped with an altimeter using photon counting mode. This can further improve the application capability for stereo mapping because of the six laser beams with high along-track\u0000 repetition frequency, which can provide more detailed ground contour descriptions. Previous studies have addressed how to extract high-accuracy elevation control points from ICESat data. However, these methods cannot be directly applied to ICESat-2 data because of the different\u0000 modes of the laser altimeters. Therefore, in this paper, we propose a method using comprehensive evaluation labels that can extract high-accuracy elevation control points that meet the different level elevation accuracy requirements for large scale mapping from the ICESat-2 land-vegetation\u0000 along-track product. The method was verified using two airborne lidar data sets. In flat, hilly, and mountainous areas, by using our method to extract the terrain elevation, the root-mean-square error of elevation control points decrease from 1.249–2.094 m, 2.237–3.225 m, and 2.791–4.822\u0000 m to 0.262–0.429 m, 0.484–0.596 m, and 0.611–1.003 m, respectively. The results show that the extraction elevations meet the required accuracy for large scale mapping.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"20 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91078709","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: 11

Grids and Datums Update: This month we look at the Commonwealth of Australia 网格和基准更新:这个月我们来看看澳大利亚联邦

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-11-01 DOI: 10.14358/pers.87.11.795

C. Mugnier

引用次数: 0

Diffuse Attenuation Coefficient (Kd) from ICESat-2 ATLAS Spaceborne Lidar Using Random-Forest Regression 基于随机森林回归的ICESat-2 ATLAS星载激光雷达散射衰减系数(Kd

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-11-01 DOI: 10.14358/pers.21-00013r2

Forrest Corcoran, Christopher E. Parrish

{"title":"Diffuse Attenuation Coefficient (Kd) from ICESat-2 ATLAS Spaceborne Lidar Using Random-Forest Regression","authors":"Forrest Corcoran, Christopher E. Parrish","doi":"10.14358/pers.21-00013r2","DOIUrl":"https://doi.org/10.14358/pers.21-00013r2","url":null,"abstract":"This study investigates a new method for measuring water turbidity—specifically, the diffuse attenuation coefficient of downwelling irradiance Kd —using data from a spaceborne, green-wavelength lidar aboard the National Aeronautics and Space Administration's ICESat-2\u0000 satellite. The method enables us to fill nearshore data voids in existing Kd data sets and provides a more direct measurement approach than methods based on passive multispectral satellite imagery. Furthermore, in contrast to other lidar-based methods, it does not rely on extensive signal\u0000 processing or the availability of the system impulse response function, and it is designed to be applied globally rather than at a specific geographic location. The model was tested using Kd measurements from the National Oceanic and Atmospheric Administration's Visible Infrared Imaging Radiometer\u0000 Suite sensor at 94 coastal sites spanning the globe, with Kd values ranging from 0.05 to 3.6 m –1 . The results demonstrate the efficacy of the approach and serve as a benchmark for future machine-learning regression studies of turbidity using ICESat-2.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"294 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79607085","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: 6

Building a Comprehensive Digital Toolkit for Aerial Acquisition 构建航空采集综合数字工具包

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-11-01 DOI: 10.14358/pers.87.11.785

David Beattie, Tracy Ray

引用次数: 0

Improving Remote Sensing Multiple Classification by Data and Ensemble Selection 基于数据和集合选择的遥感多重分类改进

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-11-01 DOI: 10.14358/pers.20-00071r3

S. Boukir, L. Guo, N. Chehata

{"title":"Improving Remote Sensing Multiple Classification by Data and Ensemble Selection","authors":"S. Boukir, L. Guo, N. Chehata","doi":"10.14358/pers.20-00071r3","DOIUrl":"https://doi.org/10.14358/pers.20-00071r3","url":null,"abstract":"In this article, margin theory is exploited to design better ensemble classifiers for remote sensing data. A semi-supervised version of the ensemble margin is at the core of this work. Some major challenges in ensemble learning are investigated using this paradigm in the difficult context\u0000 of land cover classification: selecting the most informative instances to form an appropriate training set, and selecting the best ensemble members. The main contribution of this work lies in the explicit use of the ensemble margin as a decision method to select training data and base classifiers\u0000 in an ensemble learning framework. The selection of training data is achieved through an innovative iterative guided bagging algorithm exploiting low-margin instances. The overall classification accuracy is improved by up to 3%, with more dramatic improvement in per-class accuracy (up to 12%).\u0000 The selection of ensemble base classifiers is achieved by an ordering-based ensemble-selection algorithm relying on an original margin-based criterion that also targets low-margin instances. This method reduces the complexity (ensemble size under 30) but maintains performance.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"132 5 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79618006","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: 1

Persistent Scatterer Interferometry for Pettimudi (India) Landslide Monitoring using Sentinel-1A Images 基于Sentinel-1A图像的持续散射体干涉法在印度Pettimudi滑坡监测中的应用

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-11-01 DOI: 10.14358/pers.21-00020r3

H. Shankar, A. Roy, P. Chauhan

{"title":"Persistent Scatterer Interferometry for Pettimudi (India) Landslide Monitoring using Sentinel-1A Images","authors":"H. Shankar, A. Roy, P. Chauhan","doi":"10.14358/pers.21-00020r3","DOIUrl":"https://doi.org/10.14358/pers.21-00020r3","url":null,"abstract":"The continuous monitoring of land surface movement over time is of paramount importance for assessing landslide triggering factors and mitigating landslide hazards. This research focuses on measuring horizontal and vertical surface displacement due to a devastating landslide event in\u0000 the west-facing slope of the Rajamala Hills, induced by intense rainfall. The landslide occurred in Pettimudi, a tea-plantation village of the Idukki district in Kerala, India, on August 6–7, 2020. The persistent-scatterer synthetic aperture radar interferometry (PSInSAR ) technique,\u0000 along with the Stanford Method for Persistent Scatterers (StaMPS), was applied to investigate the land surface movement over time. A stack of 20 Sentinel-1A single-look complex images (19 interferograms) acquired in descending passes was used for PSInSAR processing. The line-of-sight (LOS\u0000 ) displacement in long time series, and hence the average LOS velocity, was measured at each measurement-point location. The mean LOS velocity was decomposed into horizontal east–west (EW ) and vertical up–down velocity components. The results show that the mean LOS, EW, and up–down\u0000 velocities in the study area, respectively, range from –18.76 to +11.88, –10.95 to +6.93, and –15.05 to +9.53 mm/y, and the LOS displacement ranges from –19.60 to +19.59 mm. The displacement values clearly indicate the instability of the terrain. The time-series LOS\u0000 displacement trends derived from the applied PSInSAR technique are very useful for providing valuable inputs for disaster management and the development of disaster early-warning systems for the benefit of local residents.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"39 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75151251","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: 3

Spectral Reflectance Estimation of UAS Multispectral Imagery Using Satellite Cross-Calibration Method 基于卫星交叉定标法的UAS多光谱影像光谱反射率估算

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-10-01 DOI: 10.14358/pers.20-00091r2

Saket Gowravaram, Haiyang Chao, A. Molthan, Tiebiao Zhao, Pengzhi Tian, H. Flanagan, L. Schultz, J. Bell

引用次数: 3

A Deep Multi-Modal Learning Method and a New RGB-Depth Data Set for Building Roof Extraction 一种深度多模态学习方法和一种新的rgb深度数据集用于建筑屋顶提取

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-10-01 DOI: 10.14358/pers.21-00007r2

M. Khoshboresh-Masouleh, R. Shah-Hosseini

引用次数: 9

Early Classification Method for US Corn and Soybean by Incorporating MODIS-Estimated Phenological Data and Historical Classification Maps in Random-Forest Regression Algorithm 基于modis估计物候数据和随机森林回归算法历史分类图的美国玉米和大豆早期分类方法

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-10-01 DOI: 10.14358/pers.21-00003r2

T. Sakamoto

引用次数: 3

GIS Tips & Tricks—Need to Find Something Fast? Here are a Few Tips GIS提示和技巧-需要快速找到一些东西?这里有一些建议

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-10-01 DOI: 10.14358/pers.87.10.697

Kristopher Gallagher, Alex S. Karlin

引用次数: 0