Pub Date : 2022-07-17DOI: 10.1109/IGARSS46834.2022.9883592
M. Celesti, M. Rast, J. Adams, V. Boccia, F. Gascon, C. Isola, J. Nieke
The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) will provide high-quality, global, operational hyperspectral observations in support of European Union and related policies for the management of natural resources, assets and benefits. In this contribution, the main outcomes of the activities carried out in Phase A/B1 and B2, as well as the planned activities for Phase C/D/E will be presented, covering the scientific support studies, the technical developments and the user community preparatory activities. The ongoing international collaboration towards increasing synergies of current and future Imaging Spectroscopy missions in space will be reported as well.
{"title":"The Copernicus Hyperspectral Imaging Mission for the Environment (Chime): Status and Planning","authors":"M. Celesti, M. Rast, J. Adams, V. Boccia, F. Gascon, C. Isola, J. Nieke","doi":"10.1109/IGARSS46834.2022.9883592","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9883592","url":null,"abstract":"The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) will provide high-quality, global, operational hyperspectral observations in support of European Union and related policies for the management of natural resources, assets and benefits. In this contribution, the main outcomes of the activities carried out in Phase A/B1 and B2, as well as the planned activities for Phase C/D/E will be presented, covering the scientific support studies, the technical developments and the user community preparatory activities. The ongoing international collaboration towards increasing synergies of current and future Imaging Spectroscopy missions in space will be reported as well.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115348855","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 : 2022-07-17DOI: 10.1109/IGARSS46834.2022.9883668
Wei-Bo Qin, Feng Wang
Synthetic aperture radar (SAR) image classification with deep learning methods has achieved high accuracy on a variety of scenes. Despite the excellent performance of new methods, the phenomenon that small perturbations in data might lead to a sharp change in the result, raises attention to these black architectures. Increasing number of adversarial attacks on convolutional neural network (CNN) have been proposed, while these methods construct their adversarial examples with the aid of corresponding classifiers. Such condition cannot be realized in actual confrontation. Therefore, we introduce a universal adversarial attack on CNN-SAR image classification. In essence, this method focuses on distinguishing target distribution by feature dictionary modeling, excluding prior knowledge of any classifier. Experiments on simulated data of plane models indicate that this proposed method works well at various typical CNNs.
{"title":"A Universal Adversarial Attack on CNN-SAR Image Classification by Feature Dictionary Modeling","authors":"Wei-Bo Qin, Feng Wang","doi":"10.1109/IGARSS46834.2022.9883668","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9883668","url":null,"abstract":"Synthetic aperture radar (SAR) image classification with deep learning methods has achieved high accuracy on a variety of scenes. Despite the excellent performance of new methods, the phenomenon that small perturbations in data might lead to a sharp change in the result, raises attention to these black architectures. Increasing number of adversarial attacks on convolutional neural network (CNN) have been proposed, while these methods construct their adversarial examples with the aid of corresponding classifiers. Such condition cannot be realized in actual confrontation. Therefore, we introduce a universal adversarial attack on CNN-SAR image classification. In essence, this method focuses on distinguishing target distribution by feature dictionary modeling, excluding prior knowledge of any classifier. Experiments on simulated data of plane models indicate that this proposed method works well at various typical CNNs.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115421555","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 : 2022-07-17DOI: 10.1109/IGARSS46834.2022.9884242
R. Akbar, Samuel Prager, Agnelo R. Silva, K. Bakian-Dogaheh, Archana Kannan, E. Hodges, Asem Melebari, D. Entekhabi, M. Moghaddam
A ground-based distributed sensing network is described in this work that leverages elements of wireless sensor networks (WSN) and uncrewed areal vehicles (UAVs) with software-defined radar payloads. Hardware and software advancements are made towards combining the operations of WSNs and UAVs for dynamic spatiotemporal monitoring of surface to subsurface soil moisture at kilometer scales. The multi-agent and distributed sensing approach demonstrates coordination, collaboration, and parallel operation of discrete assets for optimal soil moisture monitoring. Results from the first field experiment showing this coordinated operation are reported.
{"title":"Field Demonstrations of Spctor: Sensing Policy Controller and Optimizer","authors":"R. Akbar, Samuel Prager, Agnelo R. Silva, K. Bakian-Dogaheh, Archana Kannan, E. Hodges, Asem Melebari, D. Entekhabi, M. Moghaddam","doi":"10.1109/IGARSS46834.2022.9884242","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884242","url":null,"abstract":"A ground-based distributed sensing network is described in this work that leverages elements of wireless sensor networks (WSN) and uncrewed areal vehicles (UAVs) with software-defined radar payloads. Hardware and software advancements are made towards combining the operations of WSNs and UAVs for dynamic spatiotemporal monitoring of surface to subsurface soil moisture at kilometer scales. The multi-agent and distributed sensing approach demonstrates coordination, collaboration, and parallel operation of discrete assets for optimal soil moisture monitoring. Results from the first field experiment showing this coordinated operation are reported.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115586018","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 : 2022-07-17DOI: 10.1109/IGARSS46834.2022.9884065
H. Tran, A. Chauhan, Ramesh P. Singh
The main agricultural crop in Vietnam is rice. After crop harvesting, the residue is burned by the farmers in the south and north of Vietnam to prepare for the next crop. In this paper, we have analyzed satellite and ground data to study the influence of crop residue burning on aerosol parameters and air quality. Our results show pronounced changes in aerosol properties and air quality, which have adverse impacts on human health and long-term impacts on climate.
{"title":"Influence of Crop Burning on Air Pollution in Vietnam","authors":"H. Tran, A. Chauhan, Ramesh P. Singh","doi":"10.1109/IGARSS46834.2022.9884065","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884065","url":null,"abstract":"The main agricultural crop in Vietnam is rice. After crop harvesting, the residue is burned by the farmers in the south and north of Vietnam to prepare for the next crop. In this paper, we have analyzed satellite and ground data to study the influence of crop residue burning on aerosol parameters and air quality. Our results show pronounced changes in aerosol properties and air quality, which have adverse impacts on human health and long-term impacts on climate.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124410374","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 : 2022-07-17DOI: 10.1109/IGARSS46834.2022.9883642
Jing Yao, D. Hong, Lianru Gao, J. Chanussot
Over the past few decades, a large collection of feature ex-traction and classification algorithms have been developed for land cover mapping using remote sensing data. Although these methods have shown the gradually-increasing performance, their potential inevitably meets the bottleneck due to the lack of high-quality and diversified remote sensing bench-mark datasets, particularly for the multimodal cases. Accordingly, this, to a larger extent, limits the development of the corresponding methodologies and the practical application of land cover classification. To this end, we aim in this pa-per to introduce and build several multimodal remote sensing benchmark datasets for land cover classification. Further-more, two new multimodal land cover classification bench-mark datasets, i.e., Berlin and Augsburg, are openly available. Experiments are conducted on the two datasets for evaluating the performance of several multimodal feature learning and classification methods.
{"title":"Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification","authors":"Jing Yao, D. Hong, Lianru Gao, J. Chanussot","doi":"10.1109/IGARSS46834.2022.9883642","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9883642","url":null,"abstract":"Over the past few decades, a large collection of feature ex-traction and classification algorithms have been developed for land cover mapping using remote sensing data. Although these methods have shown the gradually-increasing performance, their potential inevitably meets the bottleneck due to the lack of high-quality and diversified remote sensing bench-mark datasets, particularly for the multimodal cases. Accordingly, this, to a larger extent, limits the development of the corresponding methodologies and the practical application of land cover classification. To this end, we aim in this pa-per to introduce and build several multimodal remote sensing benchmark datasets for land cover classification. Further-more, two new multimodal land cover classification bench-mark datasets, i.e., Berlin and Augsburg, are openly available. Experiments are conducted on the two datasets for evaluating the performance of several multimodal feature learning and classification methods.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116684630","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 : 2022-07-17DOI: 10.1109/IGARSS46834.2022.9884407
Nan Zhang, Hao Xu, Youmeng Liu, Tian Tian, J. Tian
Aircraft is easily covered by clouds in optical remote sensing images. It is a challenge to detect the aircraft and recognize its sub-categories in this situation. However, the methods proposed by the current research are mainly applied to high-quality images, which do not perform well on cloudy images. In this paper, an adaptive feature aggregation network called AFA-Net is proposed to solve this problem. We design a mixed self-attention module that adaptively focuses on the uncovered parts of the aircraft and its neighborhood from space and channel in feature maps. Experiments were done on the Optical Image Aircraft Detection and Recognition Data Set of the 3rd Tianzhibei Challenge. Compared with the most advanced object detection algorithms, the proposed approach achieves state-of-the-art performance.
{"title":"AFA-NET: Adaptive Feature Aggregation Network for Aircraft Fine-Grained Detection in Cloudy Remote Sensing Images","authors":"Nan Zhang, Hao Xu, Youmeng Liu, Tian Tian, J. Tian","doi":"10.1109/IGARSS46834.2022.9884407","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884407","url":null,"abstract":"Aircraft is easily covered by clouds in optical remote sensing images. It is a challenge to detect the aircraft and recognize its sub-categories in this situation. However, the methods proposed by the current research are mainly applied to high-quality images, which do not perform well on cloudy images. In this paper, an adaptive feature aggregation network called AFA-Net is proposed to solve this problem. We design a mixed self-attention module that adaptively focuses on the uncovered parts of the aircraft and its neighborhood from space and channel in feature maps. Experiments were done on the Optical Image Aircraft Detection and Recognition Data Set of the 3rd Tianzhibei Challenge. Compared with the most advanced object detection algorithms, the proposed approach achieves state-of-the-art performance.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116769577","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 : 2022-07-17DOI: 10.1109/IGARSS46834.2022.9884657
Yuye Zhang, A. Zhang, Genyun Sun, Hang Fu, Yanjuan Yao
Impervious surface (IS) and urban heat island (UHI) effects are always the research hotspots. However, the existing researches either ignore the impacts of IS material on UHI or fail to monitor the seasonal temporal variations of UHI. To this end, we explore the impacts of impervious surface materials on land surface temperature (LST) by analyzing their correlation and seasonal temporal variations. The results show that the mean LST for different impervious surface materials is statistically different from each other. Additionally, the contribution of IS to LST is affected by the material. Finally, the effect of impervious surface materials on LST has seasonal differences. These findings may help decision-makers develop more effective strategies to alleviate the urban heat island phenomenon.
{"title":"Exploring the Influence and Time Variation of Impervious Surface Materials on Urban Surface Heat Island","authors":"Yuye Zhang, A. Zhang, Genyun Sun, Hang Fu, Yanjuan Yao","doi":"10.1109/IGARSS46834.2022.9884657","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884657","url":null,"abstract":"Impervious surface (IS) and urban heat island (UHI) effects are always the research hotspots. However, the existing researches either ignore the impacts of IS material on UHI or fail to monitor the seasonal temporal variations of UHI. To this end, we explore the impacts of impervious surface materials on land surface temperature (LST) by analyzing their correlation and seasonal temporal variations. The results show that the mean LST for different impervious surface materials is statistically different from each other. Additionally, the contribution of IS to LST is affected by the material. Finally, the effect of impervious surface materials on LST has seasonal differences. These findings may help decision-makers develop more effective strategies to alleviate the urban heat island phenomenon.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116933912","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 : 2022-07-17DOI: 10.1109/IGARSS46834.2022.9883903
Chenyang Zhang, Jianhui Zhao, Lin Min, Ning Li
Soil moisture is an important parameter affecting environmental processes such as hydrology, ecology and climate. Microwave remote sensing is an effective means of surface soil moisture measurement. Aiming at the influence of vegetation cover in the process of surface soil moisture inversion of winter wheat farmland by microwave remote sensing, a cooperative inversion method using multi-source remote sensing data is proposed in this paper. Thirty-three feature parameters are extracted from Radarsat-2 full polarization SAR data and Sentinel-2 optical data, and ten parameters with high correlation with soil moisture are selected to participate in soil moisture inversion by Pearson correlation analysis. Combined with the ground sampling data, four machine learning models, including Random Forest, Generalized Regression Neural Network, Radial Basis Function and Extreme Learning Machine, are used for quantitative inversion of soil moisture to reduce the impact of vegetation and improve the inversion accuracy. The experimental results show that the Random Forest model is the optimal. The average of determination coefficient is 0.63959, and the average of root mean square error is 0.0317 cm3 / cm3, which provides a reference for the inversion of soil moisture in farmland using multi-source remote sensing data.
{"title":"Cooperative Inversion of Winter Wheat Covered Surface Soil Moisture by Multi-Source Remote Sensing","authors":"Chenyang Zhang, Jianhui Zhao, Lin Min, Ning Li","doi":"10.1109/IGARSS46834.2022.9883903","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9883903","url":null,"abstract":"Soil moisture is an important parameter affecting environmental processes such as hydrology, ecology and climate. Microwave remote sensing is an effective means of surface soil moisture measurement. Aiming at the influence of vegetation cover in the process of surface soil moisture inversion of winter wheat farmland by microwave remote sensing, a cooperative inversion method using multi-source remote sensing data is proposed in this paper. Thirty-three feature parameters are extracted from Radarsat-2 full polarization SAR data and Sentinel-2 optical data, and ten parameters with high correlation with soil moisture are selected to participate in soil moisture inversion by Pearson correlation analysis. Combined with the ground sampling data, four machine learning models, including Random Forest, Generalized Regression Neural Network, Radial Basis Function and Extreme Learning Machine, are used for quantitative inversion of soil moisture to reduce the impact of vegetation and improve the inversion accuracy. The experimental results show that the Random Forest model is the optimal. The average of determination coefficient is 0.63959, and the average of root mean square error is 0.0317 cm3 / cm3, which provides a reference for the inversion of soil moisture in farmland using multi-source remote sensing data.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116934595","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 : 2022-07-17DOI: 10.1109/IGARSS46834.2022.9884283
K. Prabhakar, Veera Harikrishna Nukala, J. Gubbi, Arpan Pal, B. P.
Fusing SAR and multi-spectral images to generate a precise land cover map in a weakly supervised setting is a challenging yet essential problem. The inaccurate, noisy, and inexact ground truth labels pose difficulty training any machine learning models. In this paper, we make a fundamental and pivotal contribution towards improving the ground truth label quality using domain knowledge. We present a simple yet effective mechanism to refine the low-resolution noisy ground truth labels. The proposed approach is trained and tested on a publicly available DFC2020 dataset. Through experiments, we show the effectiveness of our method by training a deep learning model on the refined labels that outperform even the models trained with clean ground truth.
{"title":"Improving SAR and Optical Image Fusion for Lulc Classification with Domain Knowledge","authors":"K. Prabhakar, Veera Harikrishna Nukala, J. Gubbi, Arpan Pal, B. P.","doi":"10.1109/IGARSS46834.2022.9884283","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884283","url":null,"abstract":"Fusing SAR and multi-spectral images to generate a precise land cover map in a weakly supervised setting is a challenging yet essential problem. The inaccurate, noisy, and inexact ground truth labels pose difficulty training any machine learning models. In this paper, we make a fundamental and pivotal contribution towards improving the ground truth label quality using domain knowledge. We present a simple yet effective mechanism to refine the low-resolution noisy ground truth labels. The proposed approach is trained and tested on a publicly available DFC2020 dataset. Through experiments, we show the effectiveness of our method by training a deep learning model on the refined labels that outperform even the models trained with clean ground truth.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116962411","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 : 2022-07-17DOI: 10.1109/IGARSS46834.2022.9884240
Alfonso López Ruiz, J. Jurado, C. Ogáyar, F. Feito-Higueruela
This work describes an efficient approach for generating large 3D thermal point clouds considering the occlusion of camera viewpoints. For that purpose, RGB and thermal imagery are first corrected and fused with an intensity correlation-based algorithm. Then, absolute temperature values are obtained from the normalized data. Finally, thermal imagery is mapped on the point cloud using the Graphics Processing Unit (GPU) hardware. The proposed occlusion-aware mapping algorithm is massively parallelized using OpenGL's compute shaders. Our solution allows generating dense thermal point clouds in a lower response time compared with other notable soft-ware solutions (e.g., Agisoft Metashape or Pix4Dmapper) that yield results with a significantly lower point density.
{"title":"GPU-based Mapping of Thermal Imagery for Generating 3D Occlusion-Aware Point Clouds","authors":"Alfonso López Ruiz, J. Jurado, C. Ogáyar, F. Feito-Higueruela","doi":"10.1109/IGARSS46834.2022.9884240","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884240","url":null,"abstract":"This work describes an efficient approach for generating large 3D thermal point clouds considering the occlusion of camera viewpoints. For that purpose, RGB and thermal imagery are first corrected and fused with an intensity correlation-based algorithm. Then, absolute temperature values are obtained from the normalized data. Finally, thermal imagery is mapped on the point cloud using the Graphics Processing Unit (GPU) hardware. The proposed occlusion-aware mapping algorithm is massively parallelized using OpenGL's compute shaders. Our solution allows generating dense thermal point clouds in a lower response time compared with other notable soft-ware solutions (e.g., Agisoft Metashape or Pix4Dmapper) that yield results with a significantly lower point density.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117062365","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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