Добірка наукової літератури з теми "Water body extraction"
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Статті в журналах з теми "Water body extraction"
Luo, Yuanjiang, Ao Feng, Hongxiang Li, Danyang Li, Xuan Wu, Jie Liao, Chengwu Zhang, Xingqiang Zheng, and Haibo Pu. "New deep learning method for efficient extraction of small water from remote sensing images." PLOS ONE 17, no. 8 (August 5, 2022): e0272317. http://dx.doi.org/10.1371/journal.pone.0272317.
Повний текст джерелаYe, Chul-Soo. "Water body extraction in SAR image using water body texture index." Korean Journal of Remote Sensing 31, no. 4 (August 31, 2015): 337–46. http://dx.doi.org/10.7780/kjrs.2015.31.4.6.
Повний текст джерелаJiang, Wei, Yuan Ni, Zhiguo Pang, Xiaotao Li, Hongrun Ju, Guojin He, Juan Lv, Kun Yang, June Fu, and Xiangdong Qin. "An Effective Water Body Extraction Method with New Water Index for Sentinel-2 Imagery." Water 13, no. 12 (June 11, 2021): 1647. http://dx.doi.org/10.3390/w13121647.
Повний текст джерелаNaik, 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 (December 1, 2018): 4111. http://dx.doi.org/10.11591/ijece.v8i6.pp4111-4119.
Повний текст джерелаZhang, Yonghong, Huanyu Lu, Guangyi Ma, Huajun Zhao, Donglin Xie, Sutong Geng, Wei Tian, and Kenny Thiam Choy Lim Kam Sian. "MU-Net: Embedding MixFormer into Unet to Extract Water Bodies from Remote Sensing Images." Remote Sensing 15, no. 14 (July 15, 2023): 3559. http://dx.doi.org/10.3390/rs15143559.
Повний текст джерелаYe, Chul-Soo. "Water body extraction using block-based image partitioning and extension of water body boundaries." Korean Journal of Remote Sensing 32, no. 5 (October 31, 2016): 471–82. http://dx.doi.org/10.7780/kjrs.2016.32.5.6.
Повний текст джерелаWeng, Yijie, Zongmei Li, Guofeng Tang, and Yang Wang. "OCNet-Based Water Body Extraction from Remote Sensing Images." Water 15, no. 20 (October 12, 2023): 3557. http://dx.doi.org/10.3390/w15203557.
Повний текст джерелаZhang, 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.
Повний текст джерелаHe, 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.
Повний текст джерелаChe, 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.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаSpaceborne 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.
Повний текст джерелаThesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2012.
Частини книг з теми "Water body extraction"
Jun, Wang, and 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.
Повний текст джерелаLou, 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), 247–55. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5735-1_18.
Повний текст джерелаHesham, 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, 193–96. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-43218-7_45.
Повний текст джерелаRithin 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, 129–40. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6095-4_10.
Повний текст джерелаJakovljević, Gordana, and 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.
Повний текст джерелаFang, Yiwei, Xin Lyu, Baogen Tong, Shengkai Gao, Xin Li, Xinyuan Wang, and 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.
Повний текст джерелаChandrababu 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, 263–70. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47942-7_23.
Повний текст джерелаLi, 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, 331–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31528-2_52.
Повний текст джерелаGoel, Lavika, Daya Gupta, and 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.
Повний текст джерелаWei, Zhang, Wang Guanghui, Qi Jianwei, and 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.
Повний текст джерелаТези доповідей конференцій з теми "Water body extraction"
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.
Повний текст джерелаAahlen, 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.
Повний текст джерелаArreola Esquivel, Mario Martin, Maricela Delgadillo, Carina Toxqui, and Alfonso Padilla. "Index-based methods for water body extraction in satellite data." In Applications of Digital Image Processing XLII, edited by Andrew G. Tescher and Touradj Ebrahimi. SPIE, 2019. http://dx.doi.org/10.1117/12.2529756.
Повний текст джерелаWang, Yong, Yaqi Li, and 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.
Повний текст джерелаR, Nagaraj, and 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.
Повний текст джерелаZhao, Lijun, Wei Zhang, and 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, edited by Robert J. Frouin and Hiroshi Murakami. SPIE, 2018. http://dx.doi.org/10.1117/12.2323444.
Повний текст джерелаShen, Li, and 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.
Повний текст джерелаli, meilin, Jie Rui, Songkun Yang, li Ma, Shuangjun Chen, and 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), edited by Hongzhi Wang and Xiangjie Kong. SPIE, 2023. http://dx.doi.org/10.1117/12.2673522.
Повний текст джерелаDing, Zhang, Ni Qi, Fang Dong, Li Jinhui, Yao Wei, and 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.
Повний текст джерелаZhao, Chunzhe, Xueying Li, Rong Xu, and 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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