Bibliografie tematiche / Water body extraction

Letteratura scientifica selezionata sul tema "Water body extraction"

Autore: Grafiati

Pubblicato: 1 giugno 2024

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Articoli di riviste sul tema "Water body extraction":

Luo, Yuanjiang, Ao Feng, Hongxiang Li, Danyang Li, Xuan Wu, Jie Liao, Chengwu Zhang, Xingqiang Zheng e Haibo Pu. "New deep learning method for efficient extraction of small water from remote sensing images". PLOS ONE 17, n. 8 (5 agosto 2022): e0272317. http://dx.doi.org/10.1371/journal.pone.0272317.

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Abstract (sommario):

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 study, false color processing and a generative adversarial network (GAN) were added to reconstruct remote sensing images and enhance the features of tiny water bodies. In addition, a multi-scale input strategy was designed to reduce the training cost. We input the processed data into a new water body extraction method based on strip pooling for remote sensing images, which is an improvement of DeepLabv3+. Strip pooling was introduced in the DeepLabv3+ network to better extract water bodies with a discrete distribution at long distances using different strip kernels. The experiments and tests show that the proposed method can improve the accuracy of water body extraction and is effective in fine water body extraction. Compared with seven other traditional remote sensing water body extraction methods and deep learning semantic segmentation methods, the prediction accuracy of the proposed method reaches 94.72%. In summary, the proposed method performs water body extraction better than existing methods.

Ye, Chul-Soo. "Water body extraction in SAR image using water body texture index". Korean Journal of Remote Sensing 31, n. 4 (31 agosto 2015): 337–46. http://dx.doi.org/10.7780/kjrs.2015.31.4.6.

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Jiang, Wei, Yuan Ni, Zhiguo Pang, Xiaotao Li, Hongrun Ju, Guojin He, Juan Lv, Kun Yang, June Fu e Xiangdong Qin. "An Effective Water Body Extraction Method with New Water Index for Sentinel-2 Imagery". Water 13, n. 12 (11 giugno 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 areas to conduct a water body extraction performance comparison with the normalized difference water index (NDWI). We found that (1) the contrast value of the SWI was larger than that of the NDWI in terms of various water body types, including purer water, turbid water, salt water, and floating ice, which suggested that the SWI could achieve better enhancement performance for water bodies. An (2) effective water body extraction method was proposed by integrating the SWI and Otsu algorithm, which could accurately extract various water body types with high overall accuracy. The (3) method effectively extracted large water bodies and wide river channels by suppressing shadow noise in urban areas. Our results suggested that the novel method can achieve efficient water body extraction for rapidly and accurately extracting various water bodies from Sentinel-2 data and the novel method has application potential for larger-scale surface water mapping.

Naik, B. Chandrababu, e B. Anuradha. "Extraction of Water-body Area from High-resolution Landsat Imagery". International Journal of Electrical and Computer Engineering (IJECE) 8, n. 6 (1 dicembre 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 Modified Normalized Difference Water Index (MNDWI), were used to detect and extraction of the surface water body from satellite data. Instead of all index methods, the MNDWI was performed better results. The Reservoir water area was extracted using spectral water indexing methods (NDVI, NDWI, MNDWI, and NDMI) in 1989, 1997, 2007, and 2017. The shoreline shrunk in the twenty-eight-year duration of images. The Reservoir Nagarjuna Sagar lost nearly around one-fourth of its surface water area compared to 1989. However, the Reservoir has a critical position in recent years due to changes in surface water and getting higher mud and sand. Maximum water surface area of the Reservoir will lose if such decreasing tendency follows continuously.

Zhang, Yonghong, Huanyu Lu, Guangyi Ma, Huajun Zhao, Donglin Xie, Sutong Geng, Wei Tian e Kenny Thiam Choy Lim Kam Sian. "MU-Net: Embedding MixFormer into Unet to Extract Water Bodies from Remote Sensing Images". Remote Sensing 15, n. 14 (15 luglio 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 in modeling global information. This paper proposes the MU-Net, a hybrid MixFormer architecture, as a novel method for automatically extracting water bodies. First, the MixFormer block is embedded into Unet. The combination of CNN and MixFormer is used to model the local spatial detail information and global contextual information of the image to improve the ability of the network to capture semantic features of the water body. Then, the features generated by the encoder are refined by the attention mechanism module to suppress the interference of image background noise and non-water body features, which further improves the accuracy of water body extraction. The experiments show that our method has higher segmentation accuracy and robust performance compared with the mainstream CNN- and Transformer-based semantic segmentation networks. The proposed MU-Net achieves 90.25% and 76.52% IoU on the GID and LoveDA datasets, respectively. The experimental results also validate the potential of MixFormer in water extraction studies.

Ye, Chul-Soo. "Water body extraction using block-based image partitioning and extension of water body boundaries". Korean Journal of Remote Sensing 32, n. 5 (31 ottobre 2016): 471–82. http://dx.doi.org/10.7780/kjrs.2016.32.5.6.

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Weng, Yijie, Zongmei Li, Guofeng Tang e Yang Wang. "OCNet-Based Water Body Extraction from Remote Sensing Images". Water 15, n. 20 (12 ottobre 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 extraction network to modify the base model of semantic segmentation, and the resulting model achieves excellent performance on water body remote sensing images. Compared with the traditional water body extraction method and the base network, the OCNet modified model has obvious improvement, and is applicable to the extraction of water bodies in true-color remote sensing images such as high-score images and unmanned aerial vehicle remote sensing images. The results show that the model in this study can realize automatic and fast extraction of water bodies from remote sensing images, and the predicted water body image accuracy (ACC) can reach 85%. This study can realize fast and accurate extraction of water bodies, which is of great significance for water resources acquisition and flood disaster prediction.

Zhang, Q., X. Hu e 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 (5 dicembre 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 then fed into a designed multi-scale transformer module to extract global contextual association information of water bodies. Afterwards, the water separability in the new multi-scale features output from the multi-scale transformer module is evaluated separately, and the features at different scales are adaptively weighted and fused according to their water separability. Subsequently, the network adaptively refines the fused features with the aid of a hybrid attention model to generate refined features that can effectively distinguish between water bodies and non-water bodies. Finally, these refined features are input into the prediction head to generate the final water body extraction results. The proposed network integrates the ability of CNN to capture local detail features and the ability of transformer to model global contextual semantic associations in a large range. Therefore, it can more accurately identify water bodies in remote sensing images, and the extracted water body boundaries have high accuracy and continuity. Finally, water body extraction experiments on the public dataset demonstrate the effectiveness of the proposed network. Moreover, the results of comparative experiments also show that compared with existing networks or methods such as U-Net, FCN8s, DeepLabv3+, and MSFA-Net, the proposed network has certain advantages in terms of water body extraction accuracy.

He, S. A., e Xiao Yan Zhu. "Preparation of Zirconia Fiber Body with Extrusion-Extraction Molding". Key Engineering Materials 519 (luglio 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 mechanism transformation from slurry streamlet to fiber body is particle caking, result in water being extracted by aceton and static repulsion force falling as powder surface electronmotive force being reduced, when zirconium slurry extruded into solvent with low dielectric constant. Difference of velocity distribution of slurry passing through spinneret orifice and very small surface tension between slurry and extraction agent cause coarseness occurred on the fiber body surface.

Che, Xianghong, Min Feng, Hao Jiang, Jia Song e 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 surface reflectance (SR) to 250 m to improve the spatial discrimination of water body extraction. The method has been tested at Co Ngoin and Co Bangkog in Qinghai-Tibet plateau. The downscaled SR and the derived water bodies were compared to SR and water body mapped from Landsat-7 ETM+ images were acquired on the same date. Consistency metrics were calculated to measure their agreement and disagreement. The comparisons indicated that the downscaled MODIS SR showed significant improvement over the original 500 m observations when compared with Landsat-7 ETM+ SR, and both commission and omission errors were reduced in the derived 250 m water bodies.

Più fonti

Tesi sul tema "Water body extraction":

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 car il est le premier satellite qui embarque un altimètre de fauchée quasi-nadir. Le calcul des hauteurs d’eau est fait par interférométrie sur les images SAR acquises par SWOT. La détection d’eau dans ces images est donc une étape essentielle du traitement des données SWOT, mais qui peut être difficile, en particulier avec un faible rapport signal sur bruit ou en présence de radiométries inhabituelles. Dans cette thèse, nous cherchons à développer de nouvelles méthodes pour rendre la détection d’eau plus robustes. Pour cela, nous nous intéressons à l’utilisation de données exogènes pour guider la détection, à la combinaison de données multi-temporelles et multi-capteurs et à des approches de débruitage. La première méthode proposée exploite les informations de la base de donnée des rivières utilisée par SWOT pour détecter les rivières fines dans l’image de façon robuste à la fois aux bruit dans l’image, aux erreurs éventuelles de la base de données et aux changements survenus. Cette méthode s’appuie sur un nouveau détecteur de structures linéiques, un algorithme de chemin de moindre coût et une nouvelle méthode de segmentation par CRF qui combine des termes d’attache aux données et de régularisation adaptés au problème. Nous avons également proposé une méthode dérivée des GrabCut qui utilise un polygone a priori contenant un lac pour le détecter sur une image SAR ou une série temporelle. Dans ce cadre, nous avons également étudié le recours à une combinaison multi-temporelle et multi-capteurs (optique et SAR). Enfin, dans le cadre d’une étude préliminaire sur les méthodes de débruitage pour la détection d’eau nous avons étudié les propriétés statistiques de la moyenne géométrique temporelle et proposé une adaptation de la méthode variationnelle MuLoG pour la débruiter
Spaceborne remote sensing provides hydrologists and decision-makers with data that are essential for understanding the water cycle and managing the associated resources and risks. The SWOT satellite, which is a collaboration between the French (CNES) and American (NASA, JPL) space agencies, is scheduled for launch in 2022 and will measure the height of lakes, rivers, and oceans with high spatial resolution. It will complement existing sensors, such as the SAR and optical constellations Sentinel-1 and 2, and in situ measurements. SWOT represents a technological breakthrough as it is the first satellite to carry a near-nadir swath altimeter. The estimation of water levels is done by interferometry on the SAR images acquired by SWOT. Detecting water in these images is therefore an essential step in processing SWOT data, but it can be very difficult, especially with low signal-to-noise ratios, or in the presence of unusual radiometries. In this thesis, we seek to develop new methods to make water detection more robust. To this end, we focus on the use of exogenous data to guide detection, the combination of multi-temporal and multi-sensor data and denoising approaches. The first proposed method exploits information from the river database used by SWOT (derived from GRWL) to detect narrow rivers in the image in a way that is robust to both noise in the image, potential errors in the database, and temporal changes. This method relies on a new linear structure detector, a least-cost path algorithm, and a new Conditional Random Field segmentation method that combines data attachment and regularization terms adapted to the problem. We also proposed a method derived from GrabCut that uses an a priori polygon containing a lake to detect it on a SAR image or a time series of SAR images. Within this framework, we also studied the use of a multi-temporal and multi-sensor combination between Sentinel-1 SAR and Sentinel-2 optical images. Finally, as part of a preliminary study on denoising methods applied to water detection, we studied the statistical properties of the geometric temporal mean and proposed an adaptation of the variational method MuLoG to denoise it

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 largely on the manual selection of different parameters values, i.e. the centre frequency, the number of scales and orientations, and the standard deviations. Previous studies have been conducted on how to determine optimal values. However the results are sometimes inconsistent and even contradictory. Furthermore, the selection of the mask size and tile size used in the convolution process has received little attention, presumably since they are image set dependent. This research attempts to verify specific claims made in previous studies about the influence of the number of scales and orientations, but also to investigate the variation of the filter mask size and tile size for water body extraction from satellite imagery. Optical satellite imagery may contain texture samples that are conceptually the same (belong to the same class), but are structurally different or differ due to changes in illumination, i.e. a texture may appear completely different when the intensity or position of a light source changes. A systematic testing of the effects of varying the parameter values on optical satellite imagery is conducted. Experiments are designed to verify claims made about the influence of varying the scales and orientations within predetermined ranges, but also to show the considerable changes in classification accuracy when varying the filter mask and tile size. Heuristic techniques such as Genetic Algorithms (GA) can be used to find optimum solutions in application domains where an enumeration approach is not feasible. Hence, the effectiveness of a GA to automate the process of determining optimum Gabor filter parameter values for a given image dataset is also investigated. The results of the research can be used to facilitate the selection of Gabor filter parameters for applications that involve multi-textured image segmentation or classification, and specifically to guide the selection of appropriate filter mask and tile sizes for automated analysis of satellite imagery.
Thesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2012.

Capitoli di libri sul tema "Water body extraction":

Jun, Wang, e Xu Kuangdi. "Extraction of Water-Contained Ore Body". In The ECPH Encyclopedia of Mining and Metallurgy, 1–3. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0740-1_228-1.

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Lou, Linjiang, Chen Chen, Xinyuan Gao, Kun Liu, Minmin Li e 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), 247–55. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5735-1_18.

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Hesham, Anas, e 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, 193–96. Cham: 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 e P. Geetha. "Evaluation of Water Body Extraction from Satellite Images Using Open-Source Tools". In Intelligent Systems, Technologies and Applications, 129–40. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6095-4_10.

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Jakovljević, Gordana, e Miro Govedarica. "Water Body Extraction and Flood Risk Assessment Using Lidar and Open Data". In Climate Change Management, 93–111. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03383-5_7.

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Fang, Yiwei, Xin Lyu, Baogen Tong, Shengkai Gao, Xin Li, Xinyuan Wang e Zhennan Xu. "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), 257–69. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0923-0_26.

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Chandrababu Naik, B., Bairam Ravi Kumar, K. Vasu Babu, K. Purushotham Prasad e 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, 263–70. Cham: 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 e Kun Xue. "Comparative Study of Water-Body Information Extraction Methods Based on Electronic Sensing Image". In Advances in Mechanical and Electronic Engineering, 331–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31528-2_52.

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Goel, Lavika, Daya Gupta e V. K. Panchal. "Biogeography and Plate Tectonics Based Optimization for Water Body Extraction in Satellite Images". In Advances in Intelligent and Soft Computing, 1–13. New Delhi: Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0491-6_1.

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Wei, Zhang, Wang Guanghui, Qi Jianwei e Zhang Tao. "Application Research on Water Body Extraction of Gaofen-3 Polarimetric SAR Based on Deep Learning". In Lecture Notes in Electrical Engineering, 274–83. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8202-6_24.

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Atti di convegni sul tema "Water body extraction":

Yuan Tian, Xiuwan Chen, Peng Luo e 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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Aahlen, Julia. "AUTOMATIC WATER BODY EXTRACTION FROM REMOTE SENSING IMAGES USING ENTROPY". In 15th International Multidisciplinary Scientific GeoConference SGEM2015. Stef92 Technology, 2011. http://dx.doi.org/10.5593/sgem2015/b21/s8.064.

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Arreola Esquivel, Mario Martin, Maricela Delgadillo, Carina Toxqui e Alfonso Padilla. "Index-based methods for water body extraction in satellite data". In Applications of Digital Image Processing XLII, a cura di Andrew G. Tescher e Touradj Ebrahimi. SPIE, 2019. http://dx.doi.org/10.1117/12.2529756.

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Wang, Yong, Yaqi Li e Dingsheng Wang. "Extraction of small water body information based on Res2Net-Unet". In 2023 17th International Conference on Ubiquitous Information Management and Communication (IMCOM). IEEE, 2023. http://dx.doi.org/10.1109/imcom56909.2023.10035605.

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R, Nagaraj, e Lakshmi Sutha Kumar. "Performance Analysis of Machine Learning Techniques for Water body Extraction". In 2021 IEEE Bombay Section Signature Conference (IBSSC). IEEE, 2021. http://dx.doi.org/10.1109/ibssc53889.2021.9673372.

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Zhao, Lijun, Wei Zhang e Ping Tang. "Application potential of GF-4 satellite images for water body extraction". In Remote Sensing of the Open and Coastal Ocean and Inland Waters, a cura di Robert J. Frouin e Hiroshi Murakami. SPIE, 2018. http://dx.doi.org/10.1117/12.2323444.

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Shen, Li, e Changchun Li. "Water body extraction from Landsat ETM+ imagery using adaboost algorithm". In 2010 18th International Conference on Geoinformatics. IEEE, 2010. http://dx.doi.org/10.1109/geoinformatics.2010.5567762.

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li, meilin, Jie Rui, Songkun Yang, li Ma, Shuangjun Chen e keke Jiang. "Method for inland water body extraction fused atrous spatial pyramid pooling". In International Conference on Internet of Things and Machine Learning (IoTML 2022), a cura di Hongzhi Wang e Xiangjie Kong. SPIE, 2023. http://dx.doi.org/10.1117/12.2673522.

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Ding, Zhang, Ni Qi, Fang Dong, Li Jinhui, Yao Wei e Yuan Shenggui. "Application of multispectral remote sensing technology in surface water body extraction". In 2016 International Conference on Audio, Language and Image Processing (ICALIP). IEEE, 2016. http://dx.doi.org/10.1109/icalip.2016.7846565.

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Zhao, Chunzhe, Xueying Li, Rong Xu e Jiang Xiong. "Water Body Extraction for the Landsat TM Imagery of Hulun Lake". In International Symposium on Automation, Information and Computing. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0011927800003612.

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