Building extraction from high-resolution remote sensing images benefits various practical applications. However, automation of this process is challenging due to the variety of building surface coverings, complex spatial layouts, different types of structures, and tree occlusion. In this study, we propose a multilayer perception network for building extraction from high-resolution remote sensing images. By constructing parallel networks at different levels, the proposed network retains spatial information of varying feature resolutions and uses the parsing module to perceive the prominent features of buildings, thus enhancing the model's parsing ability to target scale changes and complex urban scenes. Further, a structure-guided loss function is constructed to optimize building extraction edges. Experiments on multi-source remote sensing data sets show that our proposed multi-level perception network presents a superior performance in building extraction tasks.
{"title":"Multi-Level Perceptual Network for Urban Building Extraction from High-Resolution Remote Sensing Images","authors":"Yueming Sun, Jinlong Chen, Xiao Huang, Hongsheng Zhang","doi":"10.14358/pers.22-00103r1","DOIUrl":"https://doi.org/10.14358/pers.22-00103r1","url":null,"abstract":"Building extraction from high-resolution remote sensing images benefits various practical applications. However, automation of this process is challenging due to the variety of building surface coverings, complex spatial layouts, different types of structures, and tree occlusion. In\u0000 this study, we propose a multilayer perception network for building extraction from high-resolution remote sensing images. By constructing parallel networks at different levels, the proposed network retains spatial information of varying feature resolutions and uses the parsing module to perceive\u0000 the prominent features of buildings, thus enhancing the model's parsing ability to target scale changes and complex urban scenes. Further, a structure-guided loss function is constructed to optimize building extraction edges. Experiments on multi-source remote sensing data sets show that our\u0000 proposed multi-level perception network presents a superior performance in building extraction tasks.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114072288","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}
{"title":"GIS Tips & Tricks","authors":"Srinu Ratnala, Andrew C. Peters, Karlin","doi":"10.14358/pers.89.6.343","DOIUrl":"https://doi.org/10.14358/pers.89.6.343","url":null,"abstract":"","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"92 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":"124391171","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}
Pub Date : 2023-06-01DOI: 10.14358/pers.22-00127r2
Xiaoguang Ruan, Fanghao Yang, Meijing Guo, Chao Zou
Aiming at the problem of rapid construction of a river three-dimensional 3D scene, this article integrates remote sensing, 3D modeling, and CityEngine technology to construct a 3D scene model reconstruction method of river water-land integration. The method includes intelligent extraction of underwater topography, refined modeling of hydraulic structures, and construction of a water-land integrated real scene model. Based on this method, the high-fidelity land-underwater seamless digital terrain and the water-land 3D real scene models can be formed. Through experiments, the feasibility and limitations of this method are verified. It can effectively extract the shallow underwater terrain of inland rivers, and the overall accuracy of the study area is less than 2 m. The performance of the seamless fusion 3D terrain is better than the public digital elevation model data set. In the inland basin of Class I to II water quality, it can meet the needs of intelligent perception of a river- and lake-integrated 3D scene model.
{"title":"3D Scene Modeling Method and Feasibility Analysis of River Water-Land Integration","authors":"Xiaoguang Ruan, Fanghao Yang, Meijing Guo, Chao Zou","doi":"10.14358/pers.22-00127r2","DOIUrl":"https://doi.org/10.14358/pers.22-00127r2","url":null,"abstract":"Aiming at the problem of rapid construction of a river three-dimensional 3D scene, this article integrates remote sensing, 3D modeling, and CityEngine technology to construct a 3D scene model reconstruction method of river water-land integration. The method includes intelligent extraction\u0000 of underwater topography, refined modeling of hydraulic structures, and construction of a water-land integrated real scene model. Based on this method, the high-fidelity land-underwater seamless digital terrain and the water-land 3D real scene models can be formed. Through experiments, the\u0000 feasibility and limitations of this method are verified. It can effectively extract the shallow underwater terrain of inland rivers, and the overall accuracy of the study area is less than 2 m. The performance of the seamless fusion 3D terrain is better than the public digital elevation model\u0000 data set. In the inland basin of Class I to II water quality, it can meet the needs of intelligent perception of a river- and lake-integrated 3D scene model.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"88 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":"121644032","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}
Pub Date : 2023-06-01DOI: 10.14358/pers.22-00116r2
Hongbin Luo, Wanqiu Zhang, C. Yue, Silu Chen
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) 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 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 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 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.
{"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}
Pub Date : 2023-06-01DOI: 10.14358/pers.22-00122r2
Yunping Chen, Yue Yang, Lei Hou, Kangzhuo Yang, J. Yu, Yuan Sun
In this paper, an improved aerosol optical depth (AOD ) retrieval algorithm is proposed based on Sentinel-2 and AErosol RObotic NETwork (AERONET ) data. The surface reflectance for AOD retrieval was estimated from the image that had minimal aerosol contamination in a temporal window determined by AERONET data. Validation of the Sentinel-2 AOD retrievals was conducted against four Aerosol Robotic Network (AERONET ) sites located in Beijing. The results show that the Sentinel-2 AOD retrievals are highly consistent with the AERONET AOD measurements (R = 0.942), with 85.56% falling within the expected error. The mean absolute error and the root-mean-square error are 0.0688 and 0.0882, respectively. In addition, the AOD distribution map obtained by this algorithm well reflects the fine-spatial-resolution changes in AOD distribution. These results suggest that the improved high-resolution AOD retrieval algorithm is robust and has the potential advantage of retrieving high-resolution AOD over urban areas.
{"title":"High-Resolution Aerosol Optical Depth Retrieval in Urban Areas Based on Sentinel-2","authors":"Yunping Chen, Yue Yang, Lei Hou, Kangzhuo Yang, J. Yu, Yuan Sun","doi":"10.14358/pers.22-00122r2","DOIUrl":"https://doi.org/10.14358/pers.22-00122r2","url":null,"abstract":"In this paper, an improved aerosol optical depth (AOD ) retrieval algorithm is proposed based on Sentinel-2 and AErosol RObotic NETwork (AERONET ) data. The surface reflectance for AOD retrieval was estimated from the image that had minimal aerosol contamination in a temporal window\u0000 determined by AERONET data. Validation of the Sentinel-2 AOD retrievals was conducted against four Aerosol Robotic Network (AERONET ) sites located in Beijing. The results show that the Sentinel-2 AOD retrievals are highly consistent with the AERONET AOD measurements (R = 0.942), with 85.56%\u0000 falling within the expected error. The mean absolute error and the root-mean-square error are 0.0688 and 0.0882, respectively. In addition, the AOD distribution map obtained by this algorithm well reflects the fine-spatial-resolution changes in AOD distribution. These results suggest that\u0000 the improved high-resolution AOD retrieval algorithm is robust and has the potential advantage of retrieving high-resolution AOD over urban areas.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"15 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":"125580686","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}
Pub Date : 2023-06-01DOI: 10.14358/pers.22-00108r2
Zhikang Lin, Wei Liu, Yulong Wang, Yan Xu, C. Niu
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.
{"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}
Pub Date : 2023-05-01DOI: 10.14358/pers.22-00107r2
Yuanyang Lin, Jing He, Gang Liu, Biao Mou, Bing Wang, Rao Fu
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 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 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: 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 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.
{"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}
Pub Date : 2023-05-01DOI: 10.14358/pers.22-00113r2
Oussama Mezouar, F. Meskine, I. Boukerch
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 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 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. 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.
{"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}
Pub Date : 2023-05-01DOI: 10.14358/pers.22-00112r2
Daiwei Zhang, Bo Xu, Han Hu, Qing Zhu, Qiang Wang, X. Ge, Min Chen, Yan Zhou
Line features are of great importance for the registration of the Vehicle-Borne Mobile Mapping System that contains both lidar and multiple-lens panoramic cameras. In this work, a spherical straight- line model is proposed to detect the unified line features in the panoramic imaging surface based on the Spherical Hough Transform. The local topological constraints and gradient image voting are also combined to register the line features between panoramic images and lidar point clouds within the Hough parameter space. Experimental results show that the proposed method can accurately extract the long strip targets on the panoramic images and avoid spurious or broken line-segments. Meanwhile, the line matching precision between point clouds and panoramic images are also improved.
{"title":"Spherical Hough Transform for Robust Line Detection Toward a 2D–3D Integrated Mobile Mapping System","authors":"Daiwei Zhang, Bo Xu, Han Hu, Qing Zhu, Qiang Wang, X. Ge, Min Chen, Yan Zhou","doi":"10.14358/pers.22-00112r2","DOIUrl":"https://doi.org/10.14358/pers.22-00112r2","url":null,"abstract":"Line features are of great importance for the registration of the Vehicle-Borne Mobile Mapping System that contains both lidar and multiple-lens panoramic cameras. In this work, a spherical straight- line model is proposed to detect the unified line features in the panoramic imaging\u0000 surface based on the Spherical Hough Transform. The local topological constraints and gradient image voting are also combined to register the line features between panoramic images and lidar point clouds within the Hough parameter space. Experimental results show that the proposed method can\u0000 accurately extract the long strip targets on the panoramic images and avoid spurious or broken line-segments. Meanwhile, the line matching precision between point clouds and panoramic images are also improved.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"31 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":"122004519","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}
{"title":"GIS Tips &Tricks","authors":"C. Lopez, Alma M. Karlin","doi":"10.14358/pers.89.5.265","DOIUrl":"https://doi.org/10.14358/pers.89.5.265","url":null,"abstract":"","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"20 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":"123678235","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}
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