Thematische Bibliographien / Water body extraction

Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Buchteile
  4. Konferenzberichte
  5. Berichte der Organisationen

Auswahl der wissenschaftlichen Literatur zum Thema „Water body extraction“

Autor: Grafiati
Veröffentlicht am 1. Juni 2024
Zuletzt aktualisiert am 31. Juli 2025

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Zeitschriftenartikel zum Thema "Water body extraction"

1

Luo, Yuanjiang, Ao Feng, Hongxiang Li, et al. "New deep learning method for efficient extraction of small water from remote sensing images." PLOS ONE 17, no. 8 (2022): e0272317. http://dx.doi.org/10.1371/journal.pone.0272317.

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Extracting water bodies from remote sensing images is important in many fields, such as in water resources information acquisition and analysis. Conventional methods of water body extraction enhance the differences between water bodies and other interfering water bodies to improve the accuracy of water body boundary extraction. Multiple methods must be used alternately to extract water body boundaries more accurately. Water body extraction methods combined with neural networks struggle to improve the extraction accuracy of fine water bodies while ensuring an overall extraction effect. In this
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Ye, Chul-Soo. "Water body extraction in SAR image using water body texture index." Korean Journal of Remote Sensing 31, no. 4 (2015): 337–46. http://dx.doi.org/10.7780/kjrs.2015.31.4.6.

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3

Na, Zihao, Zhonghua Guo, and Yang Zhu. "Soil Moisture Monitoring Based on Deformable Convolution Unit Net Algorithm Combined with Water Area Changes." Electronics 14, no. 5 (2025): 1011. https://doi.org/10.3390/electronics14051011.

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In response to the issue that existing soil moisture monitoring methods are significantly affected by surface roughness and the complex environment around water bodies, leading to a need for improvement in the accuracy of soil moisture inversion, a soil moisture detection algorithm based on a DCU-Net (Deformable Conv Unit-Net) water body extraction model is proposed, using the Ningxia region as the study area. The algorithm introduces the DCU (Deformable Conv Unit) module, which addresses the problem of extracting small water bodies at large scales with low resolution; reduces the probability
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Jiang, Wei, Yuan Ni, Zhiguo Pang, et al. "An Effective Water Body Extraction Method with New Water Index for Sentinel-2 Imagery." Water 13, no. 12 (2021): 1647. http://dx.doi.org/10.3390/w13121647.

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Surface water bodies, such as rivers, lakes, and reservoirs, play an irreplaceable role in global ecosystems and climate systems. Sentinel-2 imagery provides new high-resolution satellite remote sensing data. Based on the analysis of the spectral characteristics of the Sentinel-2 satellite, a novel water index called the Sentinel-2 water index (SWI) that is based on the vegetation-sensitive red-edge band (Band 5) and shortwave infrared (Band 11) bands was developed. Four representative water body types, namely, Taihu Lake, Yangtze River, Chaka Salt Lake, and Chain Lake, were selected as study
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Zhang, Yonghong, Huanyu Lu, Guangyi Ma, et al. "MU-Net: Embedding MixFormer into Unet to Extract Water Bodies from Remote Sensing Images." Remote Sensing 15, no. 14 (2023): 3559. http://dx.doi.org/10.3390/rs15143559.

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Water bodies extraction is important in water resource utilization and flood prevention and mitigation. Remote sensing images contain rich information, but due to the complex spatial background features and noise interference, problems such as inaccurate tributary extraction and inaccurate segmentation occur when extracting water bodies. Recently, using a convolutional neural network (CNN) to extract water bodies is gradually becoming popular. However, the local property of CNN limits the extraction of global information, while Transformer, using a self-attention mechanism, has great potential
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Weng, Yijie, Zongmei Li, Guofeng Tang, and Yang Wang. "OCNet-Based Water Body Extraction from Remote Sensing Images." Water 15, no. 20 (2023): 3557. http://dx.doi.org/10.3390/w15203557.

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Water body extraction techniques from remotely sensed images are crucial in water resources distribution studies, climate change studies and other work. The traditional remote sensing water body extraction has the problems of low accuracy and being time-consuming and laborious, and the water body recognition technique based on deep learning is more efficient and accurate than the traditional threshold method; however, there is the problem that the basic model of semantic segmentation is not well-adapted to complex remote sensing images. Based on this, this study adopts an OCNet feature extract
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Zhang, Q., X. Hu, and Y. Xiao. "A NOVEL HYBRID MODEL BASED ON CNN AND MULTI-SCALE TRANSFORMER FOR EXTRACTING WATER BODIES FROM HIGH RESOLUTION REMOTE SENSING IMAGES." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1/W1-2023 (December 5, 2023): 889–94. http://dx.doi.org/10.5194/isprs-annals-x-1-w1-2023-889-2023.

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Abstract. Extracting water bodies from high-resolution remote sensing images has always been a challenging and hot task in the field of remote sensing. Considering that the accuracy and reliability of water body extraction still have some room for improvement, this paper proposes a hybrid network model based on CNN and multi-scale transformer for water body extraction from high-resolution remote sensing images. Specifically, the proposed network first uses a CNN model to extract a series of multi-scale features from shallow to deep from remote sensing images. These multi-scale features are the
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Naik, B. Chandrababu, and B. Anuradha. "Extraction of Water-body Area from High-resolution Landsat Imagery." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4111. http://dx.doi.org/10.11591/ijece.v8i6.pp4111-4119.

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Extraction of water bodies from satellite imagery has been broadly explored in the current decade. So many techniques were involved in detecting of the surface water bodies from satellite data. To detect and extracting of surface water body changes in Nagarjuna Sagar Reservoir, Andhra Pradesh from the period 1989 to 2017, were calculated using Landsat-5 TM, and Landsat-8 OLI data. Unsupervised classification and spectral water indexing methods, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), and
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Naik, B. Chandrababu, and B. Anuradha. "Extraction of Water-body Area from High-resolution Landsat Imagery." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4111–19. https://doi.org/10.11591/ijece.v8i6.pp4111-4119.

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Extraction of water bodies from satellite imagery has been broadly explored in the current decade. So many techniques were involved in detecting of the surface water bodies from satellite data. To detect and extracting of surface water body changes in Nagarjuna Sagar Reservoir, Andhra Pradesh from the period 1989 to 2017, were calculated using Landsat-5 TM, and Landsat-8 OLI data. Unsupervised classification and spectral water indexing methods, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), and
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Ye, Chul-Soo. "Water body extraction using block-based image partitioning and extension of water body boundaries." Korean Journal of Remote Sensing 32, no. 5 (2016): 471–82. http://dx.doi.org/10.7780/kjrs.2016.32.5.6.

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Dissertationen zum Thema "Water body extraction"

1

Gasnier, Nicolas. "Use of multi-temporal and multi-sensor data for continental water body extraction in the context of the SWOT mission." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT002.

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La télédétection spatiale fournit aux hydrologues et aux décideurs des données indispensables à la compréhension du cycle de l’eau et à la gestion des ressources et risques associés. Le satellite SWOT, qui est une collaboration entre les agences spatiales françaises (CNES) et américaine (NASA, JPL), et dont le lancement est prévu en 2022 vise à mesurer la hauteur des lacs, rivières et océans avec une grande résolution spatiale. Il complétera ainsi les capteurs existants, comme les constellations SAR et optique Sentinel-1 et 2 et les relevés in situ. SWOT représente une rupture technologique ca
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Pillay, Maldean. "Gabor filter parameter optimization for multi-textured images : a case study on water body extraction from satellite imagery." Thesis, 2012. http://hdl.handle.net/10413/11070.

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The analysis and identification of texture is a key area in image processing and computer vision. One of the most prominent texture analysis algorithms is the Gabor Filter. These filters are used by convolving an image with a family of self similar filters or wavelets through the selection of a suitable number of scales and orientations, which are responsible for aiding in the identification of textures of differing coarseness and directions respectively. While extensively used in a variety of applications, including, biometrics such as iris and facial recognition, their effectiveness depend l
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Buchteile zum Thema "Water body extraction"

1

Jun, Wang, and Xu Kuangdi. "Extraction of Water-Contained Ore Body." In The ECPH Encyclopedia of Mining and Metallurgy. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0740-1_228-1.

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Jun, Wang. "Extraction of Water-Contained Ore Body." In The ECPH Encyclopedia of Mining and Metallurgy. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-2086-0_228.

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Prasad, K., E. Stephen Neal Joshua, and Osvaldo Gervasi. "Algorithms for Water Body Extraction from Remote Sensing Data." In Water Informatics. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1518-3_8.

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Lou, Linjiang, Chen Chen, Xinyuan Gao, Kun Liu, Minmin Li, and Yajie Fu. "Comparative Research on Water Body Extraction Methods Based on SPOT Data." In Proceedings of the 7th China High Resolution Earth Observation Conference (CHREOC 2020). Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5735-1_18.

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Hesham, Anas, and Dursun Zafer Seker. "Investigating Accurate Water Body Extraction from Satellite Imagery Using Convolutional Neural Network with Water Indices." In Recent Research on Geotechnical Engineering, Remote Sensing, Geophysics and Earthquake Seismology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-43218-7_45.

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Rithin Paul Reddy, K., Suda Sai Srija, R. Karthi, and P. Geetha. "Evaluation of Water Body Extraction from Satellite Images Using Open-Source Tools." In Intelligent Systems, Technologies and Applications. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6095-4_10.

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Fang, Yiwei, Xin Lyu, Baogen Tong, et al. "PSAGNet: A Water Body Extraction Method for High Resolution Remote Sensing Images." In Proceedings of International Conference on Image, Vision and Intelligent Systems 2022 (ICIVIS 2022). Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0923-0_26.

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Jakovljević, Gordana, and Miro Govedarica. "Water Body Extraction and Flood Risk Assessment Using Lidar and Open Data." In Climate Change Management. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03383-5_7.

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Chandrababu Naik, B., Bairam Ravi Kumar, K. Vasu Babu, K. Purushotham Prasad, and K. Sai Venu Prathap. "Surface Water Body Extraction for Landsat-8 (OLI) Imagery Using Water-Indices Methods and SCM Techniques." In Signals and Communication Technology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47942-7_23.

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Li, Xiumei, Xianbin Liu, Lina Liu, and Kun Xue. "Comparative Study of Water-Body Information Extraction Methods Based on Electronic Sensing Image." In Advances in Mechanical and Electronic Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31528-2_52.

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Konferenzberichte zum Thema "Water body extraction"

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Zhang, Tianyi, Weibin Li, Xihui Feng, et al. "Super-Resolution Water Body Extraction Based on MF-SegFormer." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10640498.

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Yang, Xinyi, Wenli Huang, and Shujie Chen. "Extraction of Water Coverage Based on a Combined Multi-Source Water Body Distribution Dataset." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642751.

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Qin, Chen-Hao, Wei-Bin Li, Tian-Yi Zhang, Wen-Bo Ji, Xi-Hui Feng, and Yi Ren. "Improved DeepLabv3+ Based Flood Water Body Extraction Model for SAR Imagery." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10640606.

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Kumari, Sushma, Jiantao Wu, David Ayala-Cabrera, and Soumyabrata Dev. "Effective Water Body Extraction from Hyperspectral Data: A Focus on Unsupervised Band Indices." In 2024 IEEE 9th International Conference on Computational Intelligence and Applications (ICCIA). IEEE, 2024. http://dx.doi.org/10.1109/iccia62557.2024.10719176.

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Wang, Jianyu, Ye Tian, Lei Chen, Ning Wang, Ningning Zhang, and Siao Liu. "A Water Body Extraction Network Based on Fusing SAR Images and DEM Data." In 2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP). IEEE, 2024. https://doi.org/10.1109/icsidp62679.2024.10868508.

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Zhou, Jing-Song, and Si-Bao Chen. "Dynamic patch refinement network for water body extraction and ultra-high resolution segmentation." In International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2025), edited by Haiquan Zhao and Xinhua Tang. SPIE, 2025. https://doi.org/10.1117/12.3070674.

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Liu, Ting, Mengke Yuan, Chaoran Lu, et al. "Water Body Extraction from SAR and Multi-Source Data Using Siamese Network-Based Segmentation." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10641935.

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Xing, Jun, Binge Cui, Peng Li, Xiaohui Wang, and Lijuan Song. "Water body extraction in Nansi Lake Basin by integrating surface object types and spectral features." In Sixth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2024), edited by Zhiliang Qin, Jun Chen, and Huaichun Wu. SPIE, 2025. https://doi.org/10.1117/12.3057516.

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Ji, Wenbo, Weibin Li, Xihui Feng, Tianyi Zhang, Chenhao Qin, and Yi Ren. "Multimodal water body extraction network based on remote sensing satellite images and digital surface model." In International Conference on Optics, Electronics, and Communication Engineering, edited by Yang Yue. SPIE, 2024. http://dx.doi.org/10.1117/12.3048285.

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Yuan Tian, Xiuwan Chen, Peng Luo, and Yubin Xu. "Beijiang water body information extraction based on ENVISAT-ASAR." In 2012 Second International Workshop on Earth Observation and Remote Sensing Applications (EORSA). IEEE, 2012. http://dx.doi.org/10.1109/eorsa.2012.6261181.

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Berichte der Organisationen zum Thema "Water body extraction"

1

Jia, Dan, Yong-Yi Wang, Dave Warman, and Alex Wang. PR350-224502-R02 Managing Longitudinal Stresses from Uneven Support and Settlement. Pipeline Research Council International, Inc. (PRCI), 2025. https://doi.org/10.55274/r0000107.

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High longitudinal stresses in pipelines can be a major contributor to failures of girth welds and circumferentially oriented flaws. These high stresses are frequently associated with construction and maintenance activities, such as excavation and backfill in new construction, pipe replacement, and integrity digs. There are currently no industry-wide guidelines for the management of construction- and maintenance-related stresses. The objective of this project is to develop such guidelines by extracting and streamlining the best practices from pipeline operators and other sources. Evidence colle
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