Photogrammetric Engineering & Remote Sensing最新文献_第8页

Multi-Level Perceptual Network for Urban Building Extraction from High-Resolution Remote Sensing Images 基于多层次感知网络的高分辨率遥感影像城市建筑提取

Photogrammetric Engineering & Remote Sensing Pub Date : 2023-07-01 DOI: 10.14358/pers.22-00103r1

Yueming Sun, Jinlong Chen, Xiao Huang, Hongsheng Zhang

引用次数: 0

GIS Tips & Tricks GIS提示和技巧

Photogrammetric Engineering & Remote Sensing Pub Date : 2023-06-01 DOI: 10.14358/pers.89.6.343

Srinu Ratnala, Andrew C. Peters, Karlin

引用次数: 0

3D Scene Modeling Method and Feasibility Analysis of River Water-Land Integration 河流水陆一体化三维场景建模方法及可行性分析

Photogrammetric Engineering & Remote Sensing Pub Date : 2023-06-01 DOI: 10.14358/pers.22-00127r2

Xiaoguang Ruan, Fanghao Yang, Meijing Guo, Chao Zou

引用次数: 0

Strategies for Forest Height Estimation by High-Precision DEM Combined with Short-Wavelength PolInSAR TanDEM-X 结合短波PolInSAR TanDEM-X的高精度DEM森林高度估算策略

Photogrammetric Engineering & Remote Sensing Pub Date : 2023-06-01 DOI: 10.14358/pers.22-00116r2

Hongbin Luo, Wanqiu Zhang, C. Yue, Silu Chen

{"title":"Strategies for Forest Height Estimation by High-Precision DEM Combined with Short-Wavelength PolInSAR TanDEM-X","authors":"Hongbin Luo, Wanqiu Zhang, C. Yue, Silu Chen","doi":"10.14358/pers.22-00116r2","DOIUrl":"https://doi.org/10.14358/pers.22-00116r2","url":null,"abstract":"The purpose of this article is to explore forest height estimation strategies using topographic data (DEM) combined with TanDEM-X while comparing the effect of volume scattering complex coherence selection on forest height estimation in the traditional random volume over ground (RVoG)\u0000 three-stage algorithm. In this study, four experimental strategies were designed for comparison based on TanDEM-X polarized interferometric synthetic aperture radar (PolInSAR) data, TanDEM-DEM, and 42 field-measured data. Our results show that in the RVoG model, (1) a reference ground phase\u0000 to select the volume scattering complex coherence provides greater accuracy in determining forest height, (2) forest height estimation can be achieved by directly using DEM as ground phase information without relying on model solving and obtaining a more accurate forest height than TanDEM-X\u0000 alone, and (3) the highest estimation accuracy is obtained by using DEM as coherence information among all schemes. Although the difference in forest height estimation results is not significant in this study, it still proves that the forest height estimation strategy of high-precision DEM\u0000 combined with short-wavelength PolInSAR can not only improve the forest height estimation accuracy but also simplify the solving process of the RVoG model, which is an important reference for global forest parameter estimation and ecosystem detection based on spaceborne PolInSAR.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126019597","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

High-Resolution Aerosol Optical Depth Retrieval in Urban Areas Based on Sentinel-2 基于Sentinel-2的城市地区高分辨率气溶胶光学深度反演

Photogrammetric Engineering & Remote Sensing Pub Date : 2023-06-01 DOI: 10.14358/pers.22-00122r2

Yunping Chen, Yue Yang, Lei Hou, Kangzhuo Yang, J. Yu, Yuan Sun

引用次数: 1

Change Detection in SAR Images through Clustering Fusion Algorithm and Deep Neural Networks 基于聚类融合算法和深度神经网络的SAR图像变化检测

Photogrammetric Engineering & Remote Sensing Pub Date : 2023-06-01 DOI: 10.14358/pers.22-00108r2

Zhikang Lin, Wei Liu, Yulong Wang, Yan Xu, C. Niu

{"title":"Change Detection in SAR Images through Clustering Fusion Algorithm and Deep Neural Networks","authors":"Zhikang Lin, Wei Liu, Yulong Wang, Yan Xu, C. Niu","doi":"10.14358/pers.22-00108r2","DOIUrl":"https://doi.org/10.14358/pers.22-00108r2","url":null,"abstract":"The detection of changes in synthetic aperture radar (SAR) images based on deep learning has been widely used in landslides detection, flood disaster monitoring, and other fields of change detection due to its high classification accuracy. However, the inherent speckle noise in SAR images restricts the performance of existing SAR image change detection algorithms by clustering analysis. Therefore, this paper proposes a novel method for SAR image change detection based on clustering fusion and deep neural networks. We first used hierarchical fuzzy c-means clustering (HFCM ) to process two different images to obtain HFCM classification results. Then a fusion strategy is designed to obtain the fused image from the two HFCM classified images as the pre-classification result. Furthermore, a lightweight deep neural network com posed of a decomposition convolution module and an auxiliary classification module was proposed; the former module could reduce network parameters by 28%, and the latter could reduce network parameters by 33.3%. To improve the recognition performance of the network, the classification layer was replaced by the regression layer at the outcome of the network. By comparing the experiments of different methods on five data sets, the performance of our proposed method is superior.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121640200","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

UAS-Based Multi-Temporal Rice Plant Height Change Prediction 基于uas的水稻株高变化预测

Photogrammetric Engineering & Remote Sensing Pub Date : 2023-05-01 DOI: 10.14358/pers.22-00107r2

Yuanyang Lin, Jing He, Gang Liu, Biao Mou, Bing Wang, Rao Fu

{"title":"UAS-Based Multi-Temporal Rice Plant Height Change Prediction","authors":"Yuanyang Lin, Jing He, Gang Liu, Biao Mou, Bing Wang, Rao Fu","doi":"10.14358/pers.22-00107r2","DOIUrl":"https://doi.org/10.14358/pers.22-00107r2","url":null,"abstract":"Analyzing rice growth is essential for examining pests, illnesses, lodging, and yield. To create a Digital Surface Model (DSM ) of three important rice breeding stages, an efficient and fast (compared to manual monitoring) Unoccupied Aerial System was used to collect data. Outliers\u0000 emerge in DSM as a result of the influence of environ- ment and equipment, and the outliers related to rice not only affect the extraction of rice growth changes but are also more challenging to remove. Therefore, after using ground control points uniform geodetic level for filtering, statistical\u0000 outlier removal (SOR ) and quadratic surface filtering (QSF ) are used. After that, differential operations are applied to the DSM to create a differential digital surface model that can account for the change in rice plant height. Comparing the prediction accuracy before and after filtering:\u0000 R2 = 0.72, RMSE = 5.13cm, nRMSE = 10.65% for the initial point cloud; after QSF, R2 = 0.89, RMSE = 2.51cm, nRMSE = 5.21%; after SOR, R2 = 0.92, RMSE = 3.32cm, nRMSE = 6.89%. The findings demonstrate that point cloud filtering, particularly SOR, can increase\u0000 the accuracy of rice monitoring. The method is effective for monitoring, and after filtering, the accuracy is sufficiently increased to satisfy the needs of growth analysis. This has some potential for application and extension.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121258763","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

Automatic Satellite Images Orthorectification Using K–Means Based Cascaded Meta-Heuristic Algorithm 基于k均值级联元启发式算法的卫星图像自动正校正

Photogrammetric Engineering & Remote Sensing Pub Date : 2023-05-01 DOI: 10.14358/pers.22-00113r2

Oussama Mezouar, F. Meskine, I. Boukerch

{"title":"Automatic Satellite Images Orthorectification Using K–Means Based Cascaded Meta-Heuristic Algorithm","authors":"Oussama Mezouar, F. Meskine, I. Boukerch","doi":"10.14358/pers.22-00113r2","DOIUrl":"https://doi.org/10.14358/pers.22-00113r2","url":null,"abstract":"Orthorectification of high-resolution satellite images using a terrain- dependent rational function model (RFM) is a difficult task requiring a well-distributed set of ground control points (GCPs), which is often time-consuming and costly operation. Further, RFM is sensitive to over-parameterization\u0000 due to its many coefficients, which have no physical meaning. Optimization-based meta-heuristic algorithms ap- pear to be an efficient solution to overcome these limitations. This pa- per presents a complete automated RFM terrain-dependent orthorec- tification for satellite images. The proposed\u0000 method has two parts; the first part suggests automating the GCP extraction by combing Scale- Invariant Feature Transform and Speeded Up Robust Features algo- rithms; and the second part introduces the cascaded meta-heuristic al- gorithm using genetic algorithms and particle swarm optimization.\u0000 In this stage, a modified K-means clustering selection technique was used to support the proposed algorithm for finding the best combinations of GCPs and RFM coefficients. The obtained results are promising in terms of accuracy and stability compared to other literature methods.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123659256","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

Spherical Hough Transform for Robust Line Detection Toward a 2D–3D Integrated Mobile Mapping System 面向2D-3D集成移动地图系统的球面霍夫变换鲁棒线检测

Photogrammetric Engineering & Remote Sensing Pub Date : 2023-05-01 DOI: 10.14358/pers.22-00112r2

Daiwei Zhang, Bo Xu, Han Hu, Qing Zhu, Qiang Wang, X. Ge, Min Chen, Yan Zhou

引用次数: 0

GIS Tips &Tricks GIS提示与技巧

Photogrammetric Engineering & Remote Sensing Pub Date : 2023-05-01 DOI: 10.14358/pers.89.5.265

C. Lopez, Alma M. Karlin

引用次数: 0

Photogrammetric Engineering &amp; Remote Sensing最新文献

Photogrammetric Engineering & Remote Sensing最新文献