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Thematische Bibliographien / Water body extraction / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Water body extraction“

Autor: Grafiati

Veröffentlicht am 1. Juni 2024

Zuletzt aktualisiert am 31. Juli 2025

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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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He, S. A., and Xiao Yan Zhu. "Preparation of Zirconia Fiber Body with Extrusion-Extraction Molding." Key Engineering Materials 519 (July 2012): 291–96. http://dx.doi.org/10.4028/www.scientific.net/kem.519.291.

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Zirconia fiber body was prepared with Extrude-extracting, using zirconium slurry made of partial stabilized Zirconia ultra-fine powder. The result shows that，acetone is the first choice as extraction agent because of its notable effect of water extraction on zirconium slurry. The Zirconia fiber body, which length is over 2 centimeters and solid content is more than 98% ( weight percent ), can be prepared while the range of solid fraction in slurry is in 36 vol％～49vol％, with addition less than 1% ammonium polyacrylic acid, the extrusion force is range in 1641.5 Pa～6566.2 Pa. The solidfication m

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Cheng, Xi, Qian Zhu, Yujian Song, et al. "Precise City-Scale Urban Water Body Semantic Segmentation and Open-Source Sampleset Construction Based on Very High-Resolution Remote Sensing: A Case Study in Chengdu." Remote Sensing 16, no. 20 (2024): 3873. http://dx.doi.org/10.3390/rs16203873.

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Addressing the challenges related to urban water bodies is essential for advancing urban planning and development. Therefore, obtaining precise and timely information regarding urban water bodies is of paramount importance. To address issues such as incomplete extraction boundaries, mistaken feature identification, and omission of small water bodies, this study utilized very high-resolution (VHR) satellite images of the Chengdu urban area and its surroundings to create the Chengdu Urban Water Bodies Semantic Segmentation Dataset (CDUWD). Based on the shape characteristics of water bodies, thes

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Lou, Linjiang, Chen Chen, Minmin Li, and Kun Liu. "Comparative Analysis of Water Body Extraction Accuracy Based on Thresholding Method." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4-2024 (October 21, 2024): 331–36. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-2024-331-2024.

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Abstract. With the continuous development of remote sensing technology, various methods for land water body extraction based on satellite remote sensing have emerged. The thresholding method, as a commonly used image segmentation technique, possesses advantages such as high efficiency and wide applicability, making it widely employed in water body extraction research. In this thesis, utilizing SPOT4 imagery, we conducted experimental comparisons of water body extraction using the Iteration thresholding algorithm, Kittler-Illingworth (KI) thresholding algorithm, and Otsu thresholding algorithm.

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Wang, Yanjun, Shaochun Li, Yunhao Lin, and Mengjie Wang. "Lightweight Deep Neural Network Method for Water Body Extraction from High-Resolution Remote Sensing Images with Multisensors." Sensors 21, no. 21 (2021): 7397. http://dx.doi.org/10.3390/s21217397.

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Rapid and accurate extraction of water bodies from high-spatial-resolution remote sensing images is of great value for water resource management, water quality monitoring and natural disaster emergency response. For traditional water body extraction methods, it is difficult to select image texture and features, the shadows of buildings and other ground objects are in the same spectrum as water bodies, the existing deep convolutional neural network is difficult to train, the consumption of computing resources is large, and the methods cannot meet real-time requirements. In this paper, a water b

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Chen, Chao, Liyan Wang, Yanli Chu, and Xinyue He. "The method for water body information extraction in complex environment using GF-1 WFV images." E3S Web of Conferences 213 (2020): 03024. http://dx.doi.org/10.1051/e3sconf/202021303024.

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Water body is one of the most active and important earth resources, and which has a profound impact on the natural system and human society. In order to acquire surface water body information quickly, accurately and efficiently, the method of water body information extraction using remote sensing imagery has attracted the attention of many searchers. On the basis of sorting out relevant research results of water body information extraction using remote sensing imagery, this paper proposed the method of water body information extraction based on the tasseled cap transformation for complex envir

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Song, Jia, and Xiangbing Yan. "The Effect of Negative Samples on the Accuracy of Water Body Extraction Using Deep Learning Networks." Remote Sensing 15, no. 2 (2023): 514. http://dx.doi.org/10.3390/rs15020514.

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Water resources are important strategic resources related to human survival and development. Water body extraction from remote sensing images is a very important research topic for the monitoring of global and regional surface water changes. Deep learning networks are one of the most effective approaches and training data is indispensable for ensuring the network accurately extracts water bodies. The training data for water body extraction includes water body samples and non-water negative samples. Cloud shadows are essential negative samples due to the high similarity between water bodies and

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Cheng, Xuejun, Kuikui Han, Jian Xu, et al. "SPFDNet: Water Extraction Method Based on Spatial Partition and Feature Decoupling." Remote Sensing 16, no. 21 (2024): 3959. http://dx.doi.org/10.3390/rs16213959.

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Extracting water information from remote-sensing images is of great research significance for applications such as water resource protection and flood monitoring. Current water extraction methods aggregated richer multi-level features to enhance the output results. In fact, there is a difference in the requirements for the water body and the water boundary. Indiscriminate multi-feature fusion can lead to perturbation and competition of information between these two types of features during the optimization. Consequently, models cannot accurately locate the internal vacancies within the water b

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Ji, Zhonglin, Yu Zhu, Yaozhong Pan, Xiufang Zhu, and Xuechang Zheng. "Large-Scale Extraction and Mapping of Small Surface Water Bodies Based on Very High-Spatial-Resolution Satellite Images: A Case Study in Beijing, China." Water 14, no. 18 (2022): 2889. http://dx.doi.org/10.3390/w14182889.

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Surface water is a crucial resource and environmental element for human survival and ecosystem stability; therefore, accurate information on the distribution of surface water bodies is essential. Extracting this information on a large scale is commonly implemented using moderate- and low-resolution satellite images. However, the detection and analysis of more detailed surface water structures and small water bodies necessitate the use of very high-resolution (VHR) satellite images. The large-scale application of VHR images for water extraction requires convenient and accurate methods. In this

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Zhang, Hong, Jun Wei Wang, Sheng Zhong Dong, Fang Xu Xu, and Sheng Hou Wang. "The Optimization of Extraction of Cordycepin from Fruiting Body of Cordyceps militaris (L.) Link." Advanced Materials Research 393-395 (November 2011): 1024–28. http://dx.doi.org/10.4028/www.scientific.net/amr.393-395.1024.

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The optimization of extraction of cordycepin from fruiting body of Cordyceps militaris YCC-01 by water extraction, ethanol extraction, ultrasonic extraction, and synergistic extraction is studied in this paper. The optimal conditions, water extraction at 85°C for 2.5h plus ultrasonic extraction at 600W for 35min, were determined through high performance liquid chromatography (HPLC). The dried fruiting body of cordycepin content was 9.559 mg/g by this synergistic extraction method. The yield was 66.2% higher than the control group 85°C water extraction 2.5h and 11.3% higher than the room temper

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Che, Xianghong, Min Feng, Hao Jiang, Jia Song, and Bei Jia. "Downscaling MODIS Surface Reflectance to Improve Water Body Extraction." Advances in Meteorology 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/424291.

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Inland surface water is essential to terrestrial ecosystems and human civilization. Accurate mapping of surface water dynamic is vital for both scientific research and policy-driven applications. MODIS provides twice observation per day, making it perfect for monitoring temporal water dynamic. Although MODIS provides two bands at 250 m resolution, accurately deriving water area always depends on observations from the spectral bands with 500 m resolution, which limits its discrimination ability over small lakes and rivers. The paper presents an automated method for downscaling the 500 m MODIS s

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Ma, Wenqiu, Xiao Liu, and Xinglei Zhao. "Extraction of River Water Bodies Based on ICESat-2 Photon Classification." Remote Sensing 16, no. 16 (2024): 3034. http://dx.doi.org/10.3390/rs16163034.

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The accurate extraction of river water bodies is crucial for the utilization of water resources and understanding climate patterns. Compared with traditional methods of extracting rivers using remote sensing imagery, the launch of satellite-based photon-counting LiDAR (ICESat-2) provides a novel approach for river water body extraction. The use of ICESat-2 ATL03 photon data for inland river water body extraction is relatively underexplored and thus warrants investigation. To extract inland river water bodies accurately, this study proposes a method based on the spatial distribution of ATL03 ph

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Zhang, Zhili, Meng Lu, Shunping Ji, Huafen Yu, and Chenhui Nie. "Rich CNN Features for Water-Body Segmentation from Very High Resolution Aerial and Satellite Imagery." Remote Sensing 13, no. 10 (2021): 1912. http://dx.doi.org/10.3390/rs13101912.

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Extracting water-bodies accurately is a great challenge from very high resolution (VHR) remote sensing imagery. The boundaries of a water body are commonly hard to identify due to the complex spectral mixtures caused by aquatic vegetation, distinct lake/river colors, silts near the bank, shadows from the surrounding tall plants, and so on. The diversity and semantic information of features need to be increased for a better extraction of water-bodies from VHR remote sensing images. In this paper, we address these problems by designing a novel multi-feature extraction and combination module. Thi

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Frioui, Mohamed, Getnet Yimer, Mark Shamtsyan, et al. "Isolation of bioactive beta-glucans from mycelium of <i>Pleurotus ostreatus</i> mushroom." Bioactive Compounds in Health and Disease - Online ISSN: 2574-0334; Print ISSN: 2769-2426 7, no. 12 (2024): 649–58. https://doi.org/10.31989/bchd.v7i12.1511.

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Background: Beta-glucan, a compound found in higher fungi, possesses significant properties important for the human body, including immunomodulatory effects. Developing efficient extraction technology is crucial to ensure a high yield of beta-glucan component while preserving its bioactive properties. Context and purpose of this study: This study aimed to evaluate the extraction efficiency of beta-glucans from Pleurotus ostreatus mycelium powder using microwave-assisted extraction and to compare its performance with traditional extraction methods—specifically water and ethyl alcohol-based extr

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Yue, Hui, Yao Li, Jiaxin Qian, and Ying Liu. "A new accuracy evaluation method for water body extraction." International Journal of Remote Sensing 41, no. 19 (2020): 7311–42. http://dx.doi.org/10.1080/01431161.2020.1755740.

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Su, Zhenfeng, Longwei Xiang, Holger Steffen, et al. "A New and Robust Index for Water Body Extraction from Sentinel-2 Imagery." Remote Sensing 16, no. 15 (2024): 2749. http://dx.doi.org/10.3390/rs16152749.

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Land surface water is a key part in the global ecosystem balance and hydrological cycle. Remote sensing has become an effective tool for its spatio-temporal monitoring. However, remote sensing results exemplified in so-called water indices are subject to several limitations. This paper proposes a new and effective water index called the Sentinel Multi-Band Water Index (SMBWI) to extract water bodies in complex environments from Sentinel-2 satellite imagery. Individual tests explore the effectiveness of the SMBWI in eliminating interference of various special interfering cover features. The Ite

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Liu, Haiyang, Hongda Hu, Xulong Liu, Hao Jiang, Wanxia Liu, and Xiaoling Yin. "A Comparison of Different Water Indices and Band Downscaling Methods for Water Bodies Mapping from Sentinel-2 Imagery at 10-M Resolution." Water 14, no. 17 (2022): 2696. http://dx.doi.org/10.3390/w14172696.

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Satellite-based remote sensing is important for monitoring the spatial distribution of water resources. The water index is currently one of the most widely used water body extraction methods. Based on Sentinel-2 remote sensing image, this study combines area-to-point regression kriging interpolation, bilinear interpolation, and the Gram–Schmidt (GS) pan-sharpening method with the water indices MNDWI, AWEIsh and WI2015 to compare different water body extraction methods. The experimental results showed that all water indices have satisfactory extraction ability, with the kappa coefficient as an

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Wu, Dan, and Shenglan Ye. "Research on Water Extraction from Remote Sensing Images based on Ada Boost Algorithm." Frontiers in Computing and Intelligent Systems 4, no. 1 (2023): 102–4. http://dx.doi.org/10.54097/fcis.v4i1.9478.

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The extraction of water in remote sensing image is a key step in the application of remote sensing image. Aiming at the existing problems in remote sensing image water extraction, a water extraction method based on Ada Boost algorithm is proposed. In this method, multiple thresholds are set for image segmentation, and the results are voted, and finally the water extraction results are obtained. Using the threshold value of 7 algorithms to synthesize, using the Ada Boost algorithm, the advantages and disadvantages of each algorithm are analyzed and compared. The experimental results show that t

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Zhang, Tianyi, Chenhao Qin, Weibin Li, et al. "Water Body Extraction of the Weihe River Basin Based on MF-SegFormer Applied to Landsat8 OLI Data." Remote Sensing 15, no. 19 (2023): 4697. http://dx.doi.org/10.3390/rs15194697.

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In the era of big data, making full use of remote sensing images to automatically extract surface water bodies (WBs) in complex environments is extremely challenging. Due to the weak capability of existing algorithms in extracting small WBs and WB edge information from remote sensing images, we proposed a new method—Multiscale Fusion SegFormer (MF-SegFormer)—for WB extraction in the Weihe River Basin of China using Landsat 8 OLI images. The MF-SegFormer method adopts a cascading approach to fuse features output by the SegFormer encoder at multiple scales. A feature fusion (FF) module is propos

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Zhang, Jing. "Water Body Information Extraction from Remote Sensing Images based on PSPNet." International Journal of Computer Science and Information Technology 2, no. 1 (2024): 319–25. http://dx.doi.org/10.62051/ijcsit.v2n1.33.

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Remote sensing image has the characteristics of real-time, periodicity and wide monitoring range. It can quickly and accurately obtain water area, distribution and other information, which is of great significance to the utilization and development of water resources, agricultural irrigation, flood disaster assessment and so on. Since traditional water information extraction methods only use part of image band information, the accuracy of water information extraction is low and has certain limitations. In recent years, convolutional neural network technology has developed rapidly and achieved

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Jiang, Duomandi, Yunmei Li, Qihang Liu, and Chang Huang. "Evaluating the Sustainable Development Science Satellite 1 (SDGSAT-1) Multi-Spectral Data for River Water Mapping: A Comparative Study with Sentinel-2." Remote Sensing 16, no. 15 (2024): 2716. http://dx.doi.org/10.3390/rs16152716.

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SDGSAT-1, the first scientific satellite dedicated to advancing the United Nations 2030 Agenda for Sustainable Development, brings renewed vigor and opportunities to water resource monitoring and research. This study evaluates the effectiveness of SDGSAT-1 in extracting water bodies in comparison to Sentinel-2 multi-spectral imager (MSI) data. We applied a confidence thresholding method to delineate river water from land, utilizing the Normalized Differential Water Body Index (NDWI), Normalized Difference Water Index (MNDWI), and Shaded Water Body Index (SWI). It was found that the SWI works b

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Guo, Hongxiang, Guojin He, Wei Jiang, Ranyu Yin, Lei Yan, and Wanchun Leng. "A Multi-Scale Water Extraction Convolutional Neural Network (MWEN) Method for GaoFen-1 Remote Sensing Images." ISPRS International Journal of Geo-Information 9, no. 4 (2020): 189. http://dx.doi.org/10.3390/ijgi9040189.

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Automatic water body extraction method is important for monitoring floods, droughts, and water resources. In this study, a new semantic segmentation convolutional neural network named the multi-scale water extraction convolutional neural network (MWEN) is proposed to automatically extract water bodies from GaoFen-1 (GF-1) remote sensing images. Three convolutional neural networks for semantic segmentation (fully convolutional network (FCN), Unet, and Deeplab V3+) are employed to compare with the water bodies extraction performance of MWEN. Visual comparison and five evaluation metrics are used

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Weng, Wen Lu, Hao Min Lo, Shih Jung Chan, and Wen Huang Liu. "Bioactive Component Extraction from Antrodia camphorata Fruiting Body on Artificial Agar Media." Advanced Materials Research 750-752 (August 2013): 1485–88. http://dx.doi.org/10.4028/www.scientific.net/amr.750-752.1485.

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Supercritical fluid extraction (carbon dioxide), water extraction and ethanol extraction are used to extract bioactive components from fruiting bodies of Antrodia Camphorata to find out the optimum extraction condition through operation variable changes. Analysis comparisons tell that the best condition for water and ethanol approaches is 95% ethanol as the solvent, 45 °C as the operation temperature and 24 hours for extraction while supercritical fluid extraction prefers 95 % ethanol as the co-solvent, 250 bar as the working pressure and 45 °C as the operation temperature. There exist more an

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Liu, Min, Jiangping Liu, and Hua Hu. "A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images." Applied Sciences 14, no. 4 (2024): 1344. http://dx.doi.org/10.3390/app14041344.

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Extraction of lake water bodies from remote sensing images provides reliable data support for water resource management, environmental protection, natural disaster early warning, and scientific research, and helps to promote sustainable development, protect the ecological environment and human health. With reference to the classical encoding-decoding semantic segmentation network, we propose the network model R50A3-LWBENet for lake water body extraction from remote sensing images based on ResNet50 and three attention mechanisms. R50A3-LWBENet model uses ResNet50 for feature extraction, also kn

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Chen, Jie, Yankun Wang, Jingzhe Wang, et al. "The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis." Remote Sensing 16, no. 11 (2024): 1984. http://dx.doi.org/10.3390/rs16111984.

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The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comp

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Jiang, Zhiqi, Yijun Wen, Gui Zhang, and Xin Wu. "Water Information Extraction Based on Multi-Model RF Algorithm and Sentinel-2 Image Data." Sustainability 14, no. 7 (2022): 3797. http://dx.doi.org/10.3390/su14073797.

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For the Sentinel-2 multispectral satellite image remote sensing data, due to the rich spatial information, the traditional water body extraction methods cannot meet the needs of practical applications. In this study, a random forest-based RF_16 optimal combination model algorithm is proposed to extract water bodies. The research process uses Sentinel-2 multispectral satellite images and DEM data as the basic data, collected 24 characteristic variable indicators (B2, B3, B4, B8, B11, B12, NDVI, MSAVI, B5, B6, B7, B8A, NDI45, MCARI, REIP, S2REP, IRECI, PSSRa, NDWI, MNDWI, LSWI, DEM, SLOPE, SLOPE

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Talekar, P. R. "Extraction of Water Bodies from High Resolution Remote-Sensing Satellite Imagery Using Deep Learning." International Journal of Advance and Applied Research 5, no. 17 (2024): 112–16. https://doi.org/10.5281/zenodo.12177504.

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Water bodies play an essential role in sustaining life on earth, and their accurate mapping and monitoring are critical for environmental management. The extraction of water bodies from satellite images has been a challenging task due to the presence of various land covers and their surroundings. Accurate and timely mapping of water bodies can help decision makers monitor changes in water availability, detect illegal water use, and develop strategies for water conservation and management. In recent years, deep learning techniques have shown remarkable performance in semantic segmentation tasks

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Qi, Baogui, Yin Zhuang, He Chen, Shan Dong, and Lianlin Li. "Fusion Feature Multi-Scale Pooling for Water Body Extraction from Optical Panchromatic Images." Remote Sensing 11, no. 3 (2019): 245. http://dx.doi.org/10.3390/rs11030245.

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Water body extraction is a hot research topic in remote sensing applications. Using panchromatic optical remote sensing images to extract water bodies is a challenging task, because these images have one level of gray information, variable imaging conditions, and complex scene information. Refined water body extraction from optical panchromatic images often experiences serious under- or over- segmentation problems. In this paper, for producing refined water body extraction results from optical panchromatic images, we propose a fusion feature multi-scale pooling for Markov modeling method. Mark

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Wu, Xiao Dong, Yi Ning Wang, Rui He Wang, Han Han Zhang, Bei Lin Qi, and Ming Zhu. "Research on Horizontal Well Inhibiting Water Coning and Tapping the Potential of Remaining Oil." Applied Mechanics and Materials 527 (February 2014): 57–64. http://dx.doi.org/10.4028/www.scientific.net/amm.527.57.

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The effects of well type, water extraction time and water extraction quantity on the control of bottom water coning are studied by analytical method. The results suggest that a reservoir with low vertical permeability and interlayer above the water oil contact would have good effect of water extraction and cone control. The effect of water extraction with horizontal well is better than vertical well; the earlier the water extraction is applied, the better the effect of water control is obtained; the larger the quantity of water extraction is, the more obvious is the water control effect, and w

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Guo, Zhishun, Lin Wu, Yabo Huang, Zhengwei Guo, Jianhui Zhao, and Ning Li. "Water-Body Segmentation for SAR Images: Past, Current, and Future." Remote Sensing 14, no. 7 (2022): 1752. http://dx.doi.org/10.3390/rs14071752.

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Synthetic Aperture Radar (SAR), as a microwave sensor that can sense a target all day or night under all-weather conditions, is of great significance for detecting water resources, such as coastlines, lakes and rivers. This paper reviews literature published in the past 30 years in the field of water body extraction in SAR images, and makes some proposals that the community working with SAR image waterbody extraction should consider. Firstly, this review focuses on the main ideas and characteristics of traditional water body extraction on SAR images, mainly focusing on traditional Machine Lear

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Woo, Park Cheol, Jeon Jong Ju, Moon Yong Ho, and Eom Il Kyu. "Green Algae Detection Using Water Body Extraction and Color information." Journal of the Institute of Electronics and Information Engineers 56, no. 5 (2019): 43–51. http://dx.doi.org/10.5573/ieie.2019.56.5.43.

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Germán Torrijos. C et al.,, Germán Torrijos C. et al ,. "Water Body Extraction using Mixture Analysis Techniques and Mathematical Morphology." International Journal of Mechanical and Production Engineering Research and Development 10, no. 5 (2020): 789–96. http://dx.doi.org/10.24247/ijmperdoct202079.

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Zhou, Ya'nan, Jiancheng Luo, Zhanfeng Shen, Xiaodong Hu, and Haiping Yang. "Multiscale Water Body Extraction in Urban Environments From Satellite Images." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, no. 10 (2014): 4301–12. http://dx.doi.org/10.1109/jstars.2014.2360436.

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Haibo, Yang, Wang Zongmin, Zhao Hongling, and Guo Yu. "Water Body Extraction Methods Study Based on RS and GIS." Procedia Environmental Sciences 10 (2011): 2619–24. http://dx.doi.org/10.1016/j.proenv.2011.09.407.

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Yu, Long, Zhiyin Wang, Shengwei Tian, Feiyue Ye, Jianli Ding, and Jun Kong. "Convolutional Neural Networks for Water Body Extraction from Landsat Imagery." International Journal of Computational Intelligence and Applications 16, no. 01 (2017): 1750001. http://dx.doi.org/10.1142/s1469026817500018.

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Traditional machine learning methods for water body extraction need complex spectral analysis and feature selection which rely on wealth of prior knowledge. They are time-consuming and hard to satisfy our request for accuracy, automation level and a wide range of application. We present a novel deep learning framework for water body extraction from Landsat imagery considering both its spectral and spatial information. The framework is a hybrid of convolutional neural networks (CNN) and logistic regression (LR) classifier. CNN, one of the deep learning methods, has acquired great achievements o

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Reddy, S. L. K., C. V. Rao, P. R. Kumar, R. V. G. Anjaneyulu, and B. G. Krishna. "A NOVEL METHOD FOR WATER AND WATER CANAL EXTRACTION FROM LANDSAT-8 OLI IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 323–28. http://dx.doi.org/10.5194/isprs-archives-xlii-5-323-2018.

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<p><strong>Abstract.</strong> Constituents of hydrologic network, River and water canals play a key role in Agriculture for cultivation, Industrial activities and urban planning. Remote sensing images can be effectively used for water canal extraction, which significantly improves the accuracy and reduces the cost involved in mapping using conventional means. Using remote sensing data, the water Index (WI), Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) are used in extracting the water bodies. These techniques are aimed at water body detection and need to

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Pang, Mingkun, Hongyu Pan, Hang Zhang, and Tianjun Zhang. "Experimental Investigation of the Effect of Groundwater on the Relative Permeability of Coal Bodies around Gas Extraction Boreholes." International Journal of Environmental Research and Public Health 19, no. 20 (2022): 13609. http://dx.doi.org/10.3390/ijerph192013609.

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Water infiltration in boreholes is a common problem in mine gas pre-extraction, where water infiltration can significantly reduce the efficiency of gas extraction and curtail the life cycle of the borehole. It is important to evaluate the effect of groundwater on the permeability of the coal body around a gas extraction borehole. In order to determine the seepage parameters of the fractured coal body system around the borehole, a water–gas two-phase seepage test was designed to determine the relative seepage parameters of the fractured coal media seepage system. The main conclusion is that the

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Li, Hengkai, Zikun Xu, Yanbing Zhou, Xiaoxing He, and Minghua He. "Flood Monitoring Using Sentinel-1 SAR for Agricultural Disaster Assessment in Poyang Lake Region." Remote Sensing 15, no. 21 (2023): 5247. http://dx.doi.org/10.3390/rs15215247.

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An extensive number of farmlands in the Poyang Lake region of China have been submerged due to the impact of flood disasters, resulting in significant agricultural economic losses. Therefore, it is of great importance to conduct the long-term temporal monitoring of flood-induced water body changes using remote sensing technology. However, the scarcity of optical images and the complex, fragmented terrain are pressing issues in the current water body extraction efforts in southern hilly regions, particularly due to difficulties in distinguishing shadows from numerous mountain and water bodies.

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Xu, Gang, Sizheng Lu, Long Sun, Xiao Liu, Yongke Shan, and Hongwei Jin. "Study on the Coal-Breaking Characteristics and Repair Effects of Water Jets in Gas Extraction Boreholes." Processes 13, no. 3 (2025): 836. https://doi.org/10.3390/pr13030836.

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Water jet repair technology, used for gas extraction boreholes, is an effective way to improve the effect of gas extraction. Nevertheless, the effects water jets have on the repair of gas extraction boreholes are still ambiguous. To investigate the characteristics of coal breaking using water jets in gas extraction boreholes and their repair effectiveness, a mathematical model for water jet coal breaking was established and a method for determining the depth of coal breaking using water jets was proposed. The coal-breaking depth, stress distribution law, and damage distribution law under the i

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Zhao, Zitong, Jin Yang, Mingjia Wang, et al. "The PCA-NDWI Urban Water Extraction Model Based on Hyperspectral Remote Sensing." Water 16, no. 7 (2024): 963. http://dx.doi.org/10.3390/w16070963.

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Accurate extraction of water bodies is the basis of remote sensing monitoring of water environments. Due to the complex types of ground objects around urban water bodies, high spectral and spatial resolution are needed to achieve accurate extraction of water bodies. Addressing the limitation that most spectral index methods used for water body extraction are more suitable for open waters such as oceans and lakes, this study proposes a PCA-NDWI accurate extraction model for urban water bodies based on hyperspectral remote sensing, which combines Principal Component Analysis (PCA) with Normalize

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Yang, Jichang, Yuncong Lu, Zhiqiang Zhang, et al. "A Deep Learning Method Coupling a Channel Attention Mechanism and Weighted Dice Loss Function for Water Extraction in the Yellow River Basin." Water 17, no. 4 (2025): 478. https://doi.org/10.3390/w17040478.

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The extraction of small water bodies in the Yellow River Basin has always been a key issue of concern in the fields of remote sensing technology application, water resource management, environmental science, and geographic information systems. Due to factors such as water bodies, human activities, and cloud cover, water body extraction becomes difficult. In addition, convolutional neural networks are prone to losing small water body feature information during the process of extracting local features, which can cause more imbalance between positive and negative samples of water bodies and non-w

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