The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences最新文献

The 19th 3D GeoInfo Conference: Preface Archives 第 19 届 3D GeoInfo 会议：前言档案

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-07-25 DOI: 10.5194/isprs-archives-xlviii-4-w11-2024-189-2024

L. Díaz-Vilariño, J. Balado

引用次数: 0

Evaluating Learning-based Tie Point Matching for Geometric Processing of Off-Track Satellite Stereo 评估基于学习的绑定点匹配，用于轨道外卫星立体图像的几何处理

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-06-11 DOI: 10.5194/isprs-archives-xlviii-2-2024-393-2024

Shuang Song, Luca Morelli, Xinyi Wu, Rongjun Qin, H. Albanwan, F. Remondino

{"title":"Evaluating Learning-based Tie Point Matching for Geometric Processing of Off-Track Satellite Stereo","authors":"Shuang Song, Luca Morelli, Xinyi Wu, Rongjun Qin, H. Albanwan, F. Remondino","doi":"10.5194/isprs-archives-xlviii-2-2024-393-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-393-2024","url":null,"abstract":"Abstract. Tie-point matching of off-track stereo images is a very challenging task, which can impact bias compensation and digital surface model (DSM) generation. Compared to in-track stereo images, off-track stereo images are more complex primarily due to the radiometric differences caused by sun illumination, sensor responses, atmospheric conditions, and seasonal land cover variations, and secondly due to the longer baseline and larger intersection angle. These challenges significantly limit the use of the vast number of images in satellite archives for automated geometric processing and mapping. Recent advances in deep learning (DL) based matching show promising results against images with diverse illuminations, viewing angles and scales through learning examples. This paper evaluates the potentials of addressing the tie point matching problems in off-track satellite stereo images. Specifically, we focus on stereo pairs that failed or underperformed in classic matching algorithms (i.e., SIFT (scale invariant feature transform)), and evaluate the DL-based tie points matchers by its resulting geometric accuracy in relative orientation, and the generated DSM. The experiments are carried out using 40 off-track satellite stereo pairs from four different regions around the world. We conclude that DL-based methods provide a significant higher success rate in matching challenging multi-temporal stereo pairs, even if their matching accuracy is slightly lower than classic algorithms.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"38 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141355561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Key-Region-Based UAV Visual Navigation 基于关键区域的无人机视觉导航

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-06-11 DOI: 10.5194/isprs-archives-xlviii-2-2024-173-2024

Michael Karnes, Jacob Riffel, Alper Yilmaz

{"title":"Key-Region-Based UAV Visual Navigation","authors":"Michael Karnes, Jacob Riffel, Alper Yilmaz","doi":"10.5194/isprs-archives-xlviii-2-2024-173-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-173-2024","url":null,"abstract":"Abstract. Visual navigation has recently seen significant developments with the rise in autonomous navigation. Keypoint-based mapping and localization has served as a reliable localization method for many applications, but the push to run more applications on less expensive hardware becomes extremely limiting. In this paper, we present a novel approach for visual geolocalization and navigation that improves landmark detection reliability while reducing reference map complexity. Similar to prior techniques, we use the process of point based matching schemes to solve for the image-to-map transform. The critical difference is that we use object detection to identify key-regions instead of keypoints. During an initial flight key-regions are mapped into an identity dictionary with their geolocations and few-shot learning encoded descriptors. Then on subsequent flights, key-regions are detected and matched using the identity dictionary for re-identification. Using the identified vehicles as key-regions, the results show that the proposed key-region based localization produces GPS like localization while maintaining a higher resilience to image noise compared to keypoint-based techniques.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-detail and low-cost underwater inspection of large-scale hydropower dams 大型水电站大坝的高精细、低成本水下检测

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-06-11 DOI: 10.5194/isprs-archives-xlviii-2-2024-115-2024

Michael Grömer, E. Nocerino, A. Calantropio, F. Menna, Ansgar Dreier, Lukas Winiwarter, Gottfried Mandlburger

{"title":"High-detail and low-cost underwater inspection of large-scale hydropower dams","authors":"Michael Grömer, E. Nocerino, A. Calantropio, F. Menna, Ansgar Dreier, Lukas Winiwarter, Gottfried Mandlburger","doi":"10.5194/isprs-archives-xlviii-2-2024-115-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-115-2024","url":null,"abstract":"Abstract. The article presents a practical method that combines low-cost camera systems with remotely operated vehicles (ROVs) to accomplish a comprehensive but economically feasible underwater survey of large hydropower infrastructures. Typically, inspecting reservoirs entails draining them off to allow for visual inspections, which are time-intensive, pose risks to operators' safety and are associated with generation losses. In this regard, ROVs are a much safer and more efficient alternative to traditional methods. The study was conducted at the Pack reservoir in Austria, where a reference framework was set up using terrestrial laser scanning and checkerboard markings for the above-water components. A ROV equipped with a GoPro camera and lighting system for the underwater recordings has been employed. Via a close-range photogrammetric approach, it was possible to generate 3D point clouds of the submerged infrastructure with a survey-grade accuracy level. Various strategies were explored to perform bundle block adjustment (BBA), among these were strategies where ground control points (GCPs) were used, strategies without the use of GCPs but pre-calibrated initial camera parameters and strategies with a combination of using both GCPs and pre-calibrated camera parameters in the BBA. The deployment of an inspection technique using low-cost sensors that can generate highly detailed three-dimensional models of submerged infrastructure areas is presented and discussed, allowing easy detection and localization for maintenance inspection, all while being cost-effective. The paper strengthens the suggestion of best practices that optimize camera settings, considering the effect of electronic image stabilization, suggesting its avoidance, and using advanced calibration methods.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"14 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning assisted exponential waveform decomposition for bathymetric LiDAR 用于测深激光雷达的深度学习辅助指数波形分解

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-06-11 DOI: 10.5194/isprs-archives-xlviii-2-2024-195-2024

Nan Li, M. Truong, Roland Schwarz, M. Pfennigbauer, A. Ullrich

{"title":"Deep learning assisted exponential waveform decomposition for bathymetric LiDAR","authors":"Nan Li, M. Truong, Roland Schwarz, M. Pfennigbauer, A. Ullrich","doi":"10.5194/isprs-archives-xlviii-2-2024-195-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-195-2024","url":null,"abstract":"Abstract. The processing of bathymetric LiDAR waveforms is an important task, as it provides range and radiometric information to determine the precise location of water surface and bottom, and other characteristics like amplitude. The exponential waveform decomposition proved to be an effective algorithm for bathymetric LiDAR waveforms processing, however, it heavily relies on the high-quality initial estimates of the model parameters. This paper proposes to make use of deep learning to obtain the initial values directly from the input received waveforms without any hand-crafted features and prior-knowledges. Additionally, to provide training samples, we presents a method to create the synthetic bathymetric LiDAR waveforms by simulating of the backscatter cross function returned from water bodies. Two networks with different sensitivities of weak signals were trained by these synthetic waveforms, and used to estimate the initial values of the model parameters, a least square optimization follows up to obtain the final waveform decomposition result. This deep learning assisted exponential waveform decomposition method is applied to the real waveforms acquired by RIEGL VQ-840-G. The results show that estimations with the help of deep learning is less influenced by the intermediate peaks backscattered from objects and particles in water, producing a cleaner point cloud with less isolated points below water surface than the original exponential waveform decomposition. Moreover, the proposed sensitive DL-XDC is even able to detect some very weak bottom returns with low SNR.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"38 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141358801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing Coral Structural Connectivity Analysis through Deep Learning and Remote Sensing: A Case Study of South Pacific Tetiaroa Island 通过深度学习和遥感推进珊瑚结构连接性分析：南太平洋泰蒂阿罗阿岛案例研究

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-06-11 DOI: 10.5194/isprs-archives-xlviii-2-2024-471-2024

Yunhan Zhang, J. Qin, Ming Li, Qiyao Han, A. Gruen, Deren Li, J. Zhong

{"title":"Advancing Coral Structural Connectivity Analysis through Deep Learning and Remote Sensing: A Case Study of South Pacific Tetiaroa Island","authors":"Yunhan Zhang, J. Qin, Ming Li, Qiyao Han, A. Gruen, Deren Li, J. Zhong","doi":"10.5194/isprs-archives-xlviii-2-2024-471-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-471-2024","url":null,"abstract":"Abstract. Structural connectivity is an important factor in preserving coral diversity. It maintains the stability and adaptability of coral reef ecosystems by facilitating ecological flow, species migration, and gene exchange between coral communities. However, there has always been a lack of consistent solutions for accurate structural connectivity describing and quantifying, which has hindered the understanding of the complex ecological processes in coral reefs. Based on this, this paper proposes a framework that uses advanced remote sensing and deep learning technologies to assess coral structural connectivity. Specifically, accurate coral patches are firstly identified through image segmentation techniques. And the structural connectivity is quantified by assessing the connectivity patterns between and within these coral patches. Furthermore, Tetiaroa Island in the South Pacific is used as a case study to validate the effectiveness and accuracy of the framework in assessing coral structural connectivity. The experimental results demonstrate that the framework proposed in this paper provides a powerful tool for understanding the internal ecological processes and external spatial patterns of coral reef ecosystems, thereby promoting scientific understanding and effective management of coral reef conservation.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"38 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141358803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Land Movement Detection from UAV Images for a Sustainable World 从无人机图像中探测土地移动，实现可持续发展的世界

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-06-11 DOI: 10.5194/isprs-archives-xlviii-2-2024-335-2024

P. C. Pesántez-Cabrera

引用次数: 0

Convolutional Neural Networks for Road Detection: An Unsupervised Domain Adaptation Approach 用于道路检测的卷积神经网络：一种无监督领域适应方法

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-06-11 DOI: 10.5194/isprs-archives-xlviii-2-2024-65-2024

Gustavo Rota Collegio, A. P. Dal Poz, Antonio Gaudencio Guimarães Filho, Ayman Habib

{"title":"Convolutional Neural Networks for Road Detection: An Unsupervised Domain Adaptation Approach","authors":"Gustavo Rota Collegio, A. P. Dal Poz, Antonio Gaudencio Guimarães Filho, Ayman Habib","doi":"10.5194/isprs-archives-xlviii-2-2024-65-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-65-2024","url":null,"abstract":"Abstract. Due to the frequent road network changes, keeping them updated is fundamental for several purposes. Currently, models based on Deep Learning (DL), specifically, Convolutional Neural Networks (CNNs), such as encoder-decoder type, are state-of-the-art for this purpose. In this context, the high performance in CNNs has two aspects involved: the model needs a large labeled dataset, and the dataset belongs to the same probability distribution. In practical applications, however, this may not hold, since there is a domain shift effect, and it is not customary for the availability of labeled data. To approach these challenges, we propose to adapt the U-Net architecture (encoder-decoder) to the Unsupervised Domain Adaptation (UDA) that does not need labeling data to minimize the domain shift effect. Our results demonstrate that the proposed method contributes to road segmentation, whose model reaches 74.31% (IoU) and 85.04% (F1), against the same model without UDA that reaches 67.36% (IoU) and 80.02% (F1). This implies that the information that comes from the target domain, even unsupervised, contributes to adversarial learning, improving the generalization capacity of the model, enhancing aspects such as better discrimination surrounding classes, and in the geometric delineation of the road network.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"15 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Monitoring Time-Varying Changes of Historic Structures Through Photogrammetry-Driven Digital Twinning 通过摄影测量驱动的数字配对监测历史建筑的时变变化

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-06-11 DOI: 10.5194/isprs-archives-xlviii-2-2024-181-2024

Xiangxiong Kong

引用次数: 0

Lighting Model for Underwater Photogrammetric Captures 水下摄影测量采集的照明模型

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-06-11 DOI: 10.5194/isprs-archives-xlviii-2-2024-153-2024

Nathan Hui, E. Lo, D. Rissolo, F. Kuester

引用次数: 0