Photogrammetric Engineering and Remote Sensing最新文献_第7页

Gaussian Mixture Model of Ground Filtering Based on Hierarchical Curvature Constraints for Airborne Lidar Point Clouds 基于分层曲率约束的机载激光雷达点云地面滤波高斯混合模型

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-09-01 DOI: 10.14358/pers.87.20-00080

Longjie Ye, Ka Zhang, W. Xiao, Y. Sheng, D. Su, Pengbo Wang, Shan Zhang, Na Zhao, Hui Chen

{"title":"Gaussian Mixture Model of Ground Filtering Based on Hierarchical Curvature Constraints for Airborne Lidar Point Clouds","authors":"Longjie Ye, Ka Zhang, W. Xiao, Y. Sheng, D. Su, Pengbo Wang, Shan Zhang, Na Zhao, Hui Chen","doi":"10.14358/pers.87.20-00080","DOIUrl":"https://doi.org/10.14358/pers.87.20-00080","url":null,"abstract":"This paper proposes a Gaussian mixture model of a ground filtering method based on hierarchical curvature constraints. Firstly, the thin plate spline function is iteratively applied to interpolate the reference surface. Secondly, gradually changing grid size and curvature threshold\u0000 are used to construct hierarchical constraints. Finally, an adaptive height difference classifier based on the Gaussian mixture model is proposed. Using the latent variables obtained by the expectation-maximization algorithm, the posterior probability of each point is computed. As a result,\u0000 ground and objects can be marked separately according to the calculated possibility. 15 data samples provided by the International Society for Photogrammetry and Remote Sensing are used to verify the proposed method, which is also compared with eight classical filtering algorithms. Experimental\u0000 results demonstrate that the average total errors and average Cohen's kappa coefficient of the proposed method are 6.91% and 80.9%, respectively. In general, it has better performance in areas with terrain discontinuities and bridges.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"40 10","pages":""},"PeriodicalIF":1.3,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72610097","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

Double Adaptive Intensity-Threshold Method for Uneven Lidar Data to Extract Road Markings 非均匀激光雷达数据提取道路标线的双自适应强度阈值法

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-09-01 DOI: 10.14358/pers.20-00099

C. Ye, Hongfu Li, Rui-long Wei, Lixuan Wang, Tianbo Sui, Wensen Bai, Pirasteh Saied

引用次数: 2

SectorInsight.edu—Making a Difference in a Developing Country – One Student at a Time sectorinsight .edu -在发展中国家做出改变-一次一个学生

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-09-01 DOI: 10.14358/pers.87.9.606

Jennifer Murphy

引用次数: 0

Detecting Geo-Positional Bias in Imagery Collected Using Small UASs 利用小UASs采集的图像检测地理位置偏差

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-09-01 DOI: 10.14358/pers.20-00124

J. Thayn, Aaron M. Paque, Megan C. Maher

引用次数: 1

Photogrammetry and Electrical Resistivity Tomography for the Investigation of the Clandestine Graves in Colombia 摄影测量和电阻率层析成像在哥伦比亚秘密坟墓的调查

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-09-01 DOI: 10.14358/pers.87.9.597

J. Drake, Carlos Martín Molina, Edier F Avila, A. Baena

引用次数: 1

GIS Tips & Tricks — You don't have to accept the defaults in GlobalMapper GIS提示和技巧-您不必接受GlobalMapper中的默认设置

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-08-01 DOI: 10.14358/pers.87.8.541

Brittany Capra, A. Karlin

引用次数: 0

Unsupervised Representation High-Resolution Remote Sensing Image Scene Classification via Contrastive Learning Convolutional Neural Network 基于对比学习卷积神经网络的无监督表示高分辨率遥感图像场景分类

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-08-01 DOI: 10.14358/pers.87.8.577

Fengpeng Li, Jiabao Li, Wei Han, Ruyi Feng, Lizhe Wang

{"title":"Unsupervised Representation High-Resolution Remote Sensing Image Scene Classification via Contrastive Learning Convolutional Neural Network","authors":"Fengpeng Li, Jiabao Li, Wei Han, Ruyi Feng, Lizhe Wang","doi":"10.14358/pers.87.8.577","DOIUrl":"https://doi.org/10.14358/pers.87.8.577","url":null,"abstract":"Inspired by the outstanding achievement of deep learning, supervised deep learning representation methods for high-spatial-resolution remote sensing image scene classification obtained state-of-the-art performance. However, supervised deep learning representation methods need a considerable\u0000 amount of labeled data to capture class-specific features, limiting the application of deep learning-based methods while there are a few labeled training samples. An unsupervised deep learning representation, high-resolution remote sensing image scene classification method is proposed in this\u0000 work to address this issue. The proposed method, called contrastive learning, narrows the distance between positive views: color channels belonging to the same images widens the gaps between negative view pairs consisting of color channels from different images to obtain class-specific data\u0000 representations of the input data without any supervised information. The classifier uses extracted features by the convolutional neural network (CNN)-based feature extractor with labeled information of training data to set space of each category and then, using linear regression, makes predictions\u0000 in the testing procedure. Comparing with existing unsupervised deep learning representation high-resolution remote sensing image scene classification methods, contrastive learning CNN achieves state-of-the-art performance on three different scale benchmark data sets: small scale RSSCN7 data\u0000 set, midscale aerial image data set, and large-scale NWPU-RESISC45 data set.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"63 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82651123","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

Enhanced Lunar Topographic Mapping Using Multiple Stereo Images Taken by Yutu-2 Rover with Changing Illumination Conditions 利用“玉兔二号”月球车在光照条件变化下拍摄的多幅立体图像增强月球地形测绘

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-08-01 DOI: 10.14358/pers.87.8.567

W. Wan, Jia Wang, K. Di, Jian Li, Zhaoqin Liu, Peng Man, Yexin Wang, Tzuyang Yu, Chuankai Liu, Lichun Li

引用次数: 3

Semi-Centennial of Landsat Observations & Pending Landsat 9 Launch 地球资源卫星观测半世纪&等待发射地球资源卫星9号

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-08-01 DOI: 10.14358/pers.87.8.533

S. Goward, J. Masek, T. Loveland, J. Dwyer, Darrel L. Williams, T. Arvidson, L. Rocchio, J. Irons

引用次数: 3

Remote Sensing Time Series Image Processing. First Edition 遥感时间序列图像处理。第一版

IF 1.3 4区地球科学

Photogrammetric Engineering and Remote Sensing Pub Date : 2021-08-01 DOI: 10.14358/pers.87.8.545

Qihao Weng

引用次数: 0