Pub Date : 2024-05-09DOI: 10.5194/isprs-annals-x-1-2024-213-2024
Mahdieh Shirmohammadi, S. Pirasteh, Jie Shen, Jonathan Li
Abstract. Taftan is a semi-active volcano located in southeast of Iran with a number of craters. The main objective of this study is to investigate whether subsidence or uplift in Taftan peak. A total number of 58 images of Sentinel 1-A acquired between January 2015 to December 2020 in the ascending orbit mode and 102 images of both Sentinel 1-A, Sentinel 1-B acquired between October 2014 to June 2020 in the descending orbit mode were pre-processed for this purpose. The interferograms with the permanent scatterer interferometry (PSI) method were created using SARPROZ and StaMPS software in which atmospheric corrections were made automatically and following that get surface displacement of Taftan volcano. The results of the Line Of Sight (LOS) displacement corresponding to the uplift was observed to be 0.5 mm to 1 mm yr-1 for the ascending orbit and 1 mm yr-1 for the descending orbit. Because of GPS station lack close to Taftan volcano, the GPS measurements of one station located in the study area (Saravan station) was used to check the accuracy of PSI method, the GPS station of SARAVAN has been located inside of town and it is appropriate to use and analyze PSI technique in this station. As a result, it was found that the PSI method is in good agreement with the GPS data.
{"title":"Calculating of Taftan Volcano Displacement Using PSI Technique and Sentinel 1 Images","authors":"Mahdieh Shirmohammadi, S. Pirasteh, Jie Shen, Jonathan Li","doi":"10.5194/isprs-annals-x-1-2024-213-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-1-2024-213-2024","url":null,"abstract":"Abstract. Taftan is a semi-active volcano located in southeast of Iran with a number of craters. The main objective of this study is to investigate whether subsidence or uplift in Taftan peak. A total number of 58 images of Sentinel 1-A acquired between January 2015 to December 2020 in the ascending orbit mode and 102 images of both Sentinel 1-A, Sentinel 1-B acquired between October 2014 to June 2020 in the descending orbit mode were pre-processed for this purpose. The interferograms with the permanent scatterer interferometry (PSI) method were created using SARPROZ and StaMPS software in which atmospheric corrections were made automatically and following that get surface displacement of Taftan volcano. The results of the Line Of Sight (LOS) displacement corresponding to the uplift was observed to be 0.5 mm to 1 mm yr-1 for the ascending orbit and 1 mm yr-1 for the descending orbit. Because of GPS station lack close to Taftan volcano, the GPS measurements of one station located in the study area (Saravan station) was used to check the accuracy of PSI method, the GPS station of SARAVAN has been located inside of town and it is appropriate to use and analyze PSI technique in this station. As a result, it was found that the PSI method is in good agreement with the GPS data.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140996251","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}
Abstract. Global high-precision and high timeliness land cover data is a fundamental and strategic resource for global strategic interest maintenance, global environmental change research, and sustainable development planning. However, due to difficulties in obtaining control and reference information from overseas, a single data source cannot effectively cover, and surface coverage classification faces significant challenges in information extraction. Based on this, this article proposes an intelligent interpretation method for typical elements based on multimodal fusion, starting from the characteristics of domestic remote sensing images. It also develops an optical SAR data conversion and complementarity strategy based on convolutional translation networks, as well as a typical element extraction algorithm. This solves the problems of sparse remote sensing images, limited effective observations, and difficult information recognition, thereby achieving automation of typical element information under dense observation time series High precision extraction and analysis.
摘要全球高精度、高时效的土地覆被数据是全球战略利益维护、全球环境变化研究和可持续发展规划的基础性、战略性资源。然而，由于难以从国外获取对照和参考信息，单一数据源无法有效覆盖，地表覆盖分类在信息提取方面面临巨大挑战。基于此，本文从国内遥感影像的特点出发，提出了基于多模态融合的典型要素智能解译方法。同时开发了基于卷积翻译网络的光学 SAR 数据转换与互补策略，以及典型要素提取算法。这解决了遥感影像稀疏、有效观测有限、信息识别困难等问题，从而实现了密集观测时间序列下典型要素信息的自动化高精度提取和分析。
{"title":"Land Cover Classification Based on Multimodal Remote Sensing Fusion","authors":"Wei Chen, Jiage Chen, Yuewu Wan, Xining Liu, Mengya Cai, Jingguo Xu, Hongbo Cui, Mengdie Duan","doi":"10.5194/isprs-annals-x-1-2024-35-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-1-2024-35-2024","url":null,"abstract":"Abstract. Global high-precision and high timeliness land cover data is a fundamental and strategic resource for global strategic interest maintenance, global environmental change research, and sustainable development planning. However, due to difficulties in obtaining control and reference information from overseas, a single data source cannot effectively cover, and surface coverage classification faces significant challenges in information extraction. Based on this, this article proposes an intelligent interpretation method for typical elements based on multimodal fusion, starting from the characteristics of domestic remote sensing images. It also develops an optical SAR data conversion and complementarity strategy based on convolutional translation networks, as well as a typical element extraction algorithm. This solves the problems of sparse remote sensing images, limited effective observations, and difficult information recognition, thereby achieving automation of typical element information under dense observation time series High precision extraction and analysis.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140996981","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 : 2024-05-09DOI: 10.5194/isprs-annals-x-1-2024-41-2024
Fiona C. Collins, F. Noichl, Martin Slepicka, Gerda Cones, André Borrmann
Abstract. Point clouds, image data, and corresponding processing algorithms are intensively investigated to create and enrich Building Information Models (BIM) with as-is information and maintain their value across the building lifecycle. Point clouds can be captured using LiDAR and enriched with color information from images. Complementary to such dual-sensor systems, thermography captures the infrared light spectrum, giving insight into the temperature distribution on an object’s surface and allowing a diagnosis of the as-is energetic health of buildings beyond what humans can see. Although the three sensor modes are commonly used in pair-wise combinations, only a few systems leveraging the power of tri-modal sensor fusion have been proposed. This paper introduces a sensor system comprising LiDAR, RGB, and a radiometric thermal infrared sensor that can capture a 360-degree range through bi-axial rotation. The resulting tri-modal data is fused to a thermo-color point cloud from which temperature values are derived for a standard indoor building setting. Qualitative data analysis shows the potential for unlocking further object semantics in a state-of-the-art Scan-to-BIM pipeline. Furthermore, an outlook is provided on the cross-modal usage of semantic segmentation for automatic, accurate temperature calculations.
{"title":"360-Degree Tri-Modal Scanning: Engineering a Modular Multi-Sensor Platform for Semantic Enrichment of BIM Models","authors":"Fiona C. Collins, F. Noichl, Martin Slepicka, Gerda Cones, André Borrmann","doi":"10.5194/isprs-annals-x-1-2024-41-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-1-2024-41-2024","url":null,"abstract":"Abstract. Point clouds, image data, and corresponding processing algorithms are intensively investigated to create and enrich Building Information Models (BIM) with as-is information and maintain their value across the building lifecycle. Point clouds can be captured using LiDAR and enriched with color information from images. Complementary to such dual-sensor systems, thermography captures the infrared light spectrum, giving insight into the temperature distribution on an object’s surface and allowing a diagnosis of the as-is energetic health of buildings beyond what humans can see. Although the three sensor modes are commonly used in pair-wise combinations, only a few systems leveraging the power of tri-modal sensor fusion have been proposed. This paper introduces a sensor system comprising LiDAR, RGB, and a radiometric thermal infrared sensor that can capture a 360-degree range through bi-axial rotation. The resulting tri-modal data is fused to a thermo-color point cloud from which temperature values are derived for a standard indoor building setting. Qualitative data analysis shows the potential for unlocking further object semantics in a state-of-the-art Scan-to-BIM pipeline. Furthermore, an outlook is provided on the cross-modal usage of semantic segmentation for automatic, accurate temperature calculations.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 42","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140995131","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 : 2024-05-09DOI: 10.5194/isprs-annals-x-1-2024-249-2024
Yiran Wang
Abstract. The grotto temple, carved into cliffs and widely distributed, is a significant cultural heritage in China. However, it faces severe damage and collapse threats due to natural disaster risks in its environment. Nearly seventy percent of grotto temples are located in regions prone to earthquakes and water hazards, leading to varying degrees of damage to cultural artifacts. Therefore, preventive measures are necessary to reduce the impact of natural disasters on grotto temples. A knowledge graph, a structured semantic knowledge base describing concepts and their relationships in the physical world, plays a crucial role in knowledge organization and content representation. Entity relationships are the core of knowledge, serving as both foundational data and a key task in constructing knowledge graphs and processing unstructured text. In the field of grotto temple disease monitoring, while data continues to grow, research on the correlation between textual data remains underexplored. This paper adopts the BiLSTM-CRF method to extract entity relationships, matching them with the grotto temple monitoring knowledge graph. Finally, the Neo4j software is utilized to program and display the knowledge graph, aiming to enhance the efficiency of natural disaster risk management and cultural heritage protection for grotto temples.
{"title":"Research on Entity Relationships in the Knowledge Graph of Disease Monitoring in Grotto Temples","authors":"Yiran Wang","doi":"10.5194/isprs-annals-x-1-2024-249-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-1-2024-249-2024","url":null,"abstract":"Abstract. The grotto temple, carved into cliffs and widely distributed, is a significant cultural heritage in China. However, it faces severe damage and collapse threats due to natural disaster risks in its environment. Nearly seventy percent of grotto temples are located in regions prone to earthquakes and water hazards, leading to varying degrees of damage to cultural artifacts. Therefore, preventive measures are necessary to reduce the impact of natural disasters on grotto temples. A knowledge graph, a structured semantic knowledge base describing concepts and their relationships in the physical world, plays a crucial role in knowledge organization and content representation. Entity relationships are the core of knowledge, serving as both foundational data and a key task in constructing knowledge graphs and processing unstructured text. In the field of grotto temple disease monitoring, while data continues to grow, research on the correlation between textual data remains underexplored. This paper adopts the BiLSTM-CRF method to extract entity relationships, matching them with the grotto temple monitoring knowledge graph. Finally, the Neo4j software is utilized to program and display the knowledge graph, aiming to enhance the efficiency of natural disaster risk management and cultural heritage protection for grotto temples.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140995402","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 : 2024-05-09DOI: 10.5194/isprs-annals-x-1-2024-67-2024
Wen Fan, Jiaojiao Tian, Jonas Troles, Martin Döllerer, Mengistie Kindu, T. Knoke
Abstract. Accurate segmentation of individual tree crowns (ITC) segmentation is essential for investigating tree-level based growth trends and assessing tree vitality. ITC segmentation using remote sensing data faces challenges due to crown heterogeneity, overlapping crowns and data quality. Currently, both classical and deep learning methods have been employed for crown detection and segmentation. However, the effectiveness of deep learning based approaches is limited by the need for high-quality annotated datasets. Benefiting from the BaKIM project, a high-quality annotated dataset can be provided and tested with a Mask Region-based Convolutional Neural Network (Mask R-CNN). In addition, we have used the deep learning based approach to detect the tree locations thus refining the previous Marker controlled Watershed Transformation (MCWST) segmentation approach. The experimental results show that the Mask R-CNN model exhibits better model performance and less time cost compared to the MCWST algorithm for ITC segmentation. In summary, the proposed framework can achieve robust and fast ITC segmentation, which has the potential to support various forest applications such as tree vitality estimation.
{"title":"Comparing Deep Learning and MCWST Approaches for Individual Tree Crown Segmentation","authors":"Wen Fan, Jiaojiao Tian, Jonas Troles, Martin Döllerer, Mengistie Kindu, T. Knoke","doi":"10.5194/isprs-annals-x-1-2024-67-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-1-2024-67-2024","url":null,"abstract":"Abstract. Accurate segmentation of individual tree crowns (ITC) segmentation is essential for investigating tree-level based growth trends and assessing tree vitality. ITC segmentation using remote sensing data faces challenges due to crown heterogeneity, overlapping crowns and data quality. Currently, both classical and deep learning methods have been employed for crown detection and segmentation. However, the effectiveness of deep learning based approaches is limited by the need for high-quality annotated datasets. Benefiting from the BaKIM project, a high-quality annotated dataset can be provided and tested with a Mask Region-based Convolutional Neural Network (Mask R-CNN). In addition, we have used the deep learning based approach to detect the tree locations thus refining the previous Marker controlled Watershed Transformation (MCWST) segmentation approach. The experimental results show that the Mask R-CNN model exhibits better model performance and less time cost compared to the MCWST algorithm for ITC segmentation. In summary, the proposed framework can achieve robust and fast ITC segmentation, which has the potential to support various forest applications such as tree vitality estimation.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140997551","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 : 2024-05-09DOI: 10.5194/isprs-annals-x-1-2024-283-2024
Yandi Yang, N. El-Sheimy
Abstract. Accurate LiDAR odometry results contribute directly to high-quality point cloud maps. However, traditional LiDAR odometry methods drift easily upward, leading to inaccuracies and inconsistencies in the point cloud maps. Considering abundant and reliable ground points in the Mobile Mapping System(MMS), ground points can be extracted, and constraints can be built to eliminate pose drifts. However, existing LiDAR-based odometry methods either do not use ground point cloud constraints or consider the ground plane as an infinite plane (i.e., single ground constraint), making pose estimation prone to errors. Therefore, this paper is dedicated to developing a Multiple Ground Constrained LiDAR Odometry(M-GCLO) method, which extracts multiple grounds and optimizes those plane parameters for better accuracy and robustness. M-GCLO includes three modules. Firstly, the original point clouds will be classified into the ground and non-ground points. Ground points are voxelized, and multiple ground planes are extracted, parameterized, and optimized to constrain the pose errors. All the non-ground point clouds are used for point-to-distribution matching by maintaining an NDT voxel map. Secondly, a novel method for weighting the residuals is proposed by considering the uncertainties of each point in a scan. Finally, the jacobians and residuals are given along with the weightings for estimating LiDAR states. Experimental results in KITTI and M2DGR datasets show that M-GCLO outperforms state-of-the-art LiDAR odometry methods in large-scale outdoor and indoor scenarios.
{"title":"M-GCLO: Multiple Ground Constrained LiDAR Odometry","authors":"Yandi Yang, N. El-Sheimy","doi":"10.5194/isprs-annals-x-1-2024-283-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-1-2024-283-2024","url":null,"abstract":"Abstract. Accurate LiDAR odometry results contribute directly to high-quality point cloud maps. However, traditional LiDAR odometry methods drift easily upward, leading to inaccuracies and inconsistencies in the point cloud maps. Considering abundant and reliable ground points in the Mobile Mapping System(MMS), ground points can be extracted, and constraints can be built to eliminate pose drifts. However, existing LiDAR-based odometry methods either do not use ground point cloud constraints or consider the ground plane as an infinite plane (i.e., single ground constraint), making pose estimation prone to errors. Therefore, this paper is dedicated to developing a Multiple Ground Constrained LiDAR Odometry(M-GCLO) method, which extracts multiple grounds and optimizes those plane parameters for better accuracy and robustness. M-GCLO includes three modules. Firstly, the original point clouds will be classified into the ground and non-ground points. Ground points are voxelized, and multiple ground planes are extracted, parameterized, and optimized to constrain the pose errors. All the non-ground point clouds are used for point-to-distribution matching by maintaining an NDT voxel map. Secondly, a novel method for weighting the residuals is proposed by considering the uncertainties of each point in a scan. Finally, the jacobians and residuals are given along with the weightings for estimating LiDAR states. Experimental results in KITTI and M2DGR datasets show that M-GCLO outperforms state-of-the-art LiDAR odometry methods in large-scale outdoor and indoor scenarios.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140997932","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}
Abstract. Training LiDAR point clouds object detectors requires a significant amount of annotated data, which is time-consuming and effort-demanding. Although weakly supervised 3D LiDAR-based methods have been proposed to reduce the annotation cost, their performance could be further improved. In this work, we propose a weakly supervised LiDAR-based point clouds vehicle detector that does not require any labels for the proposal generation stage and needs only a few labels for the refinement stage. It comprises two primary modules. The first is an unsupervised proposal generation module based on the geometry of point clouds. The second is the pseudo-label refinement module. We validate our method on two point clouds based object detection datasets, namely KITTI and ONCE, and compare it with various existing weakly supervised point clouds object detection methods. The experimental results demonstrate the method’s effectiveness with a small amount of labeled LiDAR point clouds.
{"title":"A Weakly Supervised Vehicle Detection Method from LiDAR Point Clouds","authors":"Yiyuan Li, Yuhang Lu, Xun Huang, Siqi Shen, Cheng Wang, Chenglu Wen","doi":"10.5194/isprs-annals-x-1-2024-123-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-1-2024-123-2024","url":null,"abstract":"Abstract. Training LiDAR point clouds object detectors requires a significant amount of annotated data, which is time-consuming and effort-demanding. Although weakly supervised 3D LiDAR-based methods have been proposed to reduce the annotation cost, their performance could be further improved. In this work, we propose a weakly supervised LiDAR-based point clouds vehicle detector that does not require any labels for the proposal generation stage and needs only a few labels for the refinement stage. It comprises two primary modules. The first is an unsupervised proposal generation module based on the geometry of point clouds. The second is the pseudo-label refinement module. We validate our method on two point clouds based object detection datasets, namely KITTI and ONCE, and compare it with various existing weakly supervised point clouds object detection methods. The experimental results demonstrate the method’s effectiveness with a small amount of labeled LiDAR point clouds.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 35","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140997226","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}
Abstract. Cloud detection is a necessary step before the application of remote sensing images. However, most methods focus on cloud detection in daytime remote sensing images. The ignored nighttime remote sensing images play more and more important role in many fields such as urban monitoring, population estimation and disaster assessment. The radiation intensity similarity between artificial lights and clouds is higher in nighttime remote sensing images than in daytime remote sensing images, which makes it difficult to distinguish artificial lights from clouds. Therefore, this paper proposes a deep learning-based method (MFFCD-Net) to detect clouds for day and nighttime remote sensing images. MFFCD-Net is designed based on the encoder-decoder structure. The encoder adopts Resnet-50 as the backbone network for better feature extraction, and a dilated residual up-sampling module (DR-UP) is designed in the decoder for up-sampling feature maps while enlarging the receptive field. A multi-scale feature extraction fusion module (MFEF) is designed to enhance the ability of the MFFCD-Net to distinguish regular textures of artificial lights and random textures of clouds. An Global Feature Recovery Fusion Module (GFRF Module) is designed to select and fuse the feature in the encoding stage and the feature in the decoding stage, thus to achieve better cloud detection accuracy. This is the first time that a deep learning-based method is designed for cloud detection both in day and nighttime remote sensing images. The experimental results on Suomi-NPP VIIRS DNB images show that MFFCD-Net achieves higher accuracy than baseline methods both in day and nighttime remote sensing images. Results on daytime remote sensing images indicate that MFFCD-Net can obtain better balance on commission and omission rates than baseline methods (92.3% versus 90.5% on F1-score). Although artificial lights introduced strong interference in cloud detection in nighttime remote sensing images, the accuracy values of MFFCD-Net on OA, Precision, Recall, and F1-score are still higher than 90%. This demonstrates that MFFCD-Net can better distinguish artificial lights from clouds than baseline methods in nighttime remote sensing images. The effectiveness of MFFCD-Net proves that it is very promising for cloud detection both in day and nighttime remote sensing images.
{"title":"A Multi-scale features-based cloud detection method for Suomi-NPP VIIRS day and night imagery","authors":"Jun Li, Chengjie Hu, Qinghong Sheng, Jiawei Xu, Chongrui Zhu, Weili Zhang","doi":"10.5194/isprs-annals-x-1-2024-115-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-1-2024-115-2024","url":null,"abstract":"Abstract. Cloud detection is a necessary step before the application of remote sensing images. However, most methods focus on cloud detection in daytime remote sensing images. The ignored nighttime remote sensing images play more and more important role in many fields such as urban monitoring, population estimation and disaster assessment. The radiation intensity similarity between artificial lights and clouds is higher in nighttime remote sensing images than in daytime remote sensing images, which makes it difficult to distinguish artificial lights from clouds. Therefore, this paper proposes a deep learning-based method (MFFCD-Net) to detect clouds for day and nighttime remote sensing images. MFFCD-Net is designed based on the encoder-decoder structure. The encoder adopts Resnet-50 as the backbone network for better feature extraction, and a dilated residual up-sampling module (DR-UP) is designed in the decoder for up-sampling feature maps while enlarging the receptive field. A multi-scale feature extraction fusion module (MFEF) is designed to enhance the ability of the MFFCD-Net to distinguish regular textures of artificial lights and random textures of clouds. An Global Feature Recovery Fusion Module (GFRF Module) is designed to select and fuse the feature in the encoding stage and the feature in the decoding stage, thus to achieve better cloud detection accuracy. This is the first time that a deep learning-based method is designed for cloud detection both in day and nighttime remote sensing images. The experimental results on Suomi-NPP VIIRS DNB images show that MFFCD-Net achieves higher accuracy than baseline methods both in day and nighttime remote sensing images. Results on daytime remote sensing images indicate that MFFCD-Net can obtain better balance on commission and omission rates than baseline methods (92.3% versus 90.5% on F1-score). Although artificial lights introduced strong interference in cloud detection in nighttime remote sensing images, the accuracy values of MFFCD-Net on OA, Precision, Recall, and F1-score are still higher than 90%. This demonstrates that MFFCD-Net can better distinguish artificial lights from clouds than baseline methods in nighttime remote sensing images. The effectiveness of MFFCD-Net proves that it is very promising for cloud detection both in day and nighttime remote sensing images.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140996740","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 : 2024-05-09DOI: 10.5194/isprs-annals-x-1-2024-177-2024
F. Noichl, Maximilian Stuecke, Clemens Thielen, André Borrmann
Abstract. The preparation of laser scanning missions is important for efficiency and data quality. Furthermore, it is a prerequisite for automated data acquisition, which has numerous applications in the built environment, including autonomous inspections and monitoring of construction progress and quality criteria. The scene and potential scanning locations can be discretized to facilitate the analysis of visibility and quality aspects. The remaining mathematical problem to generate an economic scan strategy is the Viewpoint Planning Problem (VPP), which asks for a minimum number of scanning locations within the given scene to cover the scene under pre-defined requirements. Solutions for this problem are most commonly found using heuristics. While these efficient methods scale well, they cannot generally return globally optimal solutions. This paper investigates the VPP based on a problem description that considers quality-constrained visibility in 3D scenes and suitable overlaps between individual viewpoints for targetless registration of acquired point clouds. The methodology includes the introduction of a preprocessing method designed to simplify the input data without losing information about the problem. The paper details various solution methods for the VPP, encompassing conventional heuristics and a mixed-integer linear programming formulation, which is solved using Benders decomposition. Experiments are carried out on two case study datasets, varying in specifications and sizes, to evaluate these methods. The results show the actual quality of the obtained solutions and their deviation from optimality (in terms of the estimated optimality gap) for instances where exact solutions can not be achieved.
{"title":"Assessing and Improving Automated Viewpoint Planning for Static Laser Scanning Using Optimization Methods","authors":"F. Noichl, Maximilian Stuecke, Clemens Thielen, André Borrmann","doi":"10.5194/isprs-annals-x-1-2024-177-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-1-2024-177-2024","url":null,"abstract":"Abstract. The preparation of laser scanning missions is important for efficiency and data quality. Furthermore, it is a prerequisite for automated data acquisition, which has numerous applications in the built environment, including autonomous inspections and monitoring of construction progress and quality criteria. The scene and potential scanning locations can be discretized to facilitate the analysis of visibility and quality aspects. The remaining mathematical problem to generate an economic scan strategy is the Viewpoint Planning Problem (VPP), which asks for a minimum number of scanning locations within the given scene to cover the scene under pre-defined requirements. Solutions for this problem are most commonly found using heuristics. While these efficient methods scale well, they cannot generally return globally optimal solutions. This paper investigates the VPP based on a problem description that considers quality-constrained visibility in 3D scenes and suitable overlaps between individual viewpoints for targetless registration of acquired point clouds. The methodology includes the introduction of a preprocessing method designed to simplify the input data without losing information about the problem. The paper details various solution methods for the VPP, encompassing conventional heuristics and a mixed-integer linear programming formulation, which is solved using Benders decomposition. Experiments are carried out on two case study datasets, varying in specifications and sizes, to evaluate these methods. The results show the actual quality of the obtained solutions and their deviation from optimality (in terms of the estimated optimality gap) for instances where exact solutions can not be achieved.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140996823","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 : 2024-05-09DOI: 10.5194/isprs-annals-x-1-2024-27-2024
Haoliang Chen, Yi Lin
Abstract. To explore how trees optimize their structure, we developed a method based on Pareto optimality theory. This method consists of the following operations. Firstly, we utilize Quantitative Structure Models for Single Trees from Laser Scanner Data (TreeQSM) to extract tree structures from point clouds acquired through Light Detection and Ranging (LiDAR). Subsequently, we utilize a graph-theoretical model to characterize the natural tree structure networks and implement a greedy algorithm to generate Pareto optimal tree structure networks. Finally, based on the Pareto optimality theory, we explore whether tree structures are multi-objective optimized. This paper demonstrates that tree structures lie along the Pareto front between minimizing "transport distance" and minimizing "total length". The growth pattern of trees, which produces multi-objective optimized structures, is likely an intrinsic mechanism in the generation of tree structure networks. The location of tree structures along the Pareto front varies under different environmental conditions, reflecting their diverse survival strategies.
{"title":"Exploring the Scientific Mechanism of Tree Structure Network based on LiDAR Point Cloud Data","authors":"Haoliang Chen, Yi Lin","doi":"10.5194/isprs-annals-x-1-2024-27-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-1-2024-27-2024","url":null,"abstract":"Abstract. To explore how trees optimize their structure, we developed a method based on Pareto optimality theory. This method consists of the following operations. Firstly, we utilize Quantitative Structure Models for Single Trees from Laser Scanner Data (TreeQSM) to extract tree structures from point clouds acquired through Light Detection and Ranging (LiDAR). Subsequently, we utilize a graph-theoretical model to characterize the natural tree structure networks and implement a greedy algorithm to generate Pareto optimal tree structure networks. Finally, based on the Pareto optimality theory, we explore whether tree structures are multi-objective optimized. This paper demonstrates that tree structures lie along the Pareto front between minimizing \"transport distance\" and minimizing \"total length\". The growth pattern of trees, which produces multi-objective optimized structures, is likely an intrinsic mechanism in the generation of tree structure networks. The location of tree structures along the Pareto front varies under different environmental conditions, reflecting their diverse survival strategies.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140994443","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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