Gotowa bibliografia na temat „Water body extraction”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Zobacz listy aktualnych artykułów, książek, rozpraw, streszczeń i innych źródeł naukowych na temat „Water body extraction”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Artykuły w czasopismach na temat "Water body extraction"
Luo, Yuanjiang, Ao Feng, Hongxiang Li, Danyang Li, Xuan Wu, Jie Liao, Chengwu Zhang, Xingqiang Zheng i Haibo Pu. "New deep learning method for efficient extraction of small water from remote sensing images". PLOS ONE 17, nr 8 (5.08.2022): e0272317. http://dx.doi.org/10.1371/journal.pone.0272317.
Pełny tekst źródłaYe, Chul-Soo. "Water body extraction in SAR image using water body texture index". Korean Journal of Remote Sensing 31, nr 4 (31.08.2015): 337–46. http://dx.doi.org/10.7780/kjrs.2015.31.4.6.
Pełny tekst źródłaJiang, Wei, Yuan Ni, Zhiguo Pang, Xiaotao Li, Hongrun Ju, Guojin He, Juan Lv, Kun Yang, June Fu i Xiangdong Qin. "An Effective Water Body Extraction Method with New Water Index for Sentinel-2 Imagery". Water 13, nr 12 (11.06.2021): 1647. http://dx.doi.org/10.3390/w13121647.
Pełny tekst źródłaNaik, B. Chandrababu, i B. Anuradha. "Extraction of Water-body Area from High-resolution Landsat Imagery". International Journal of Electrical and Computer Engineering (IJECE) 8, nr 6 (1.12.2018): 4111. http://dx.doi.org/10.11591/ijece.v8i6.pp4111-4119.
Pełny tekst źródłaZhang, Yonghong, Huanyu Lu, Guangyi Ma, Huajun Zhao, Donglin Xie, Sutong Geng, Wei Tian i Kenny Thiam Choy Lim Kam Sian. "MU-Net: Embedding MixFormer into Unet to Extract Water Bodies from Remote Sensing Images". Remote Sensing 15, nr 14 (15.07.2023): 3559. http://dx.doi.org/10.3390/rs15143559.
Pełny tekst źródłaYe, Chul-Soo. "Water body extraction using block-based image partitioning and extension of water body boundaries". Korean Journal of Remote Sensing 32, nr 5 (31.10.2016): 471–82. http://dx.doi.org/10.7780/kjrs.2016.32.5.6.
Pełny tekst źródłaWeng, Yijie, Zongmei Li, Guofeng Tang i Yang Wang. "OCNet-Based Water Body Extraction from Remote Sensing Images". Water 15, nr 20 (12.10.2023): 3557. http://dx.doi.org/10.3390/w15203557.
Pełny tekst źródłaZhang, Q., X. Hu i 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 (5.12.2023): 889–94. http://dx.doi.org/10.5194/isprs-annals-x-1-w1-2023-889-2023.
Pełny tekst źródłaHe, S. A., i Xiao Yan Zhu. "Preparation of Zirconia Fiber Body with Extrusion-Extraction Molding". Key Engineering Materials 519 (lipiec 2012): 291–96. http://dx.doi.org/10.4028/www.scientific.net/kem.519.291.
Pełny tekst źródłaChe, Xianghong, Min Feng, Hao Jiang, Jia Song i 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.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaSpaceborne 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.
Pełny tekst źródłaThesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2012.
Części książek na temat "Water body extraction"
Jun, Wang, i Xu Kuangdi. "Extraction of Water-Contained Ore Body". W 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.
Pełny tekst źródłaLou, Linjiang, Chen Chen, Xinyuan Gao, Kun Liu, Minmin Li i Yajie Fu. "Comparative Research on Water Body Extraction Methods Based on SPOT Data". W 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.
Pełny tekst źródłaHesham, Anas, i Dursun Zafer Seker. "Investigating Accurate Water Body Extraction from Satellite Imagery Using Convolutional Neural Network with Water Indices". W 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.
Pełny tekst źródłaRithin Paul Reddy, K., Suda Sai Srija, R. Karthi i P. Geetha. "Evaluation of Water Body Extraction from Satellite Images Using Open-Source Tools". W Intelligent Systems, Technologies and Applications, 129–40. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6095-4_10.
Pełny tekst źródłaJakovljević, Gordana, i Miro Govedarica. "Water Body Extraction and Flood Risk Assessment Using Lidar and Open Data". W Climate Change Management, 93–111. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03383-5_7.
Pełny tekst źródłaFang, Yiwei, Xin Lyu, Baogen Tong, Shengkai Gao, Xin Li, Xinyuan Wang i Zhennan Xu. "PSAGNet: A Water Body Extraction Method for High Resolution Remote Sensing Images". W 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.
Pełny tekst źródłaChandrababu Naik, B., Bairam Ravi Kumar, K. Vasu Babu, K. Purushotham Prasad i K. Sai Venu Prathap. "Surface Water Body Extraction for Landsat-8 (OLI) Imagery Using Water-Indices Methods and SCM Techniques". W Signals and Communication Technology, 263–70. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47942-7_23.
Pełny tekst źródłaLi, Xiumei, Xianbin Liu, Lina Liu i Kun Xue. "Comparative Study of Water-Body Information Extraction Methods Based on Electronic Sensing Image". W 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.
Pełny tekst źródłaGoel, Lavika, Daya Gupta i V. K. Panchal. "Biogeography and Plate Tectonics Based Optimization for Water Body Extraction in Satellite Images". W 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.
Pełny tekst źródłaWei, Zhang, Wang Guanghui, Qi Jianwei i Zhang Tao. "Application Research on Water Body Extraction of Gaofen-3 Polarimetric SAR Based on Deep Learning". W Lecture Notes in Electrical Engineering, 274–83. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8202-6_24.
Pełny tekst źródłaStreszczenia konferencji na temat "Water body extraction"
Yuan Tian, Xiuwan Chen, Peng Luo i Yubin Xu. "Beijiang water body information extraction based on ENVISAT-ASAR". W 2012 Second International Workshop on Earth Observation and Remote Sensing Applications (EORSA). IEEE, 2012. http://dx.doi.org/10.1109/eorsa.2012.6261181.
Pełny tekst źródłaAahlen, Julia. "AUTOMATIC WATER BODY EXTRACTION FROM REMOTE SENSING IMAGES USING ENTROPY". W 15th International Multidisciplinary Scientific GeoConference SGEM2015. Stef92 Technology, 2011. http://dx.doi.org/10.5593/sgem2015/b21/s8.064.
Pełny tekst źródłaArreola Esquivel, Mario Martin, Maricela Delgadillo, Carina Toxqui i Alfonso Padilla. "Index-based methods for water body extraction in satellite data". W Applications of Digital Image Processing XLII, redaktorzy Andrew G. Tescher i Touradj Ebrahimi. SPIE, 2019. http://dx.doi.org/10.1117/12.2529756.
Pełny tekst źródłaWang, Yong, Yaqi Li i Dingsheng Wang. "Extraction of small water body information based on Res2Net-Unet". W 2023 17th International Conference on Ubiquitous Information Management and Communication (IMCOM). IEEE, 2023. http://dx.doi.org/10.1109/imcom56909.2023.10035605.
Pełny tekst źródłaR, Nagaraj, i Lakshmi Sutha Kumar. "Performance Analysis of Machine Learning Techniques for Water body Extraction". W 2021 IEEE Bombay Section Signature Conference (IBSSC). IEEE, 2021. http://dx.doi.org/10.1109/ibssc53889.2021.9673372.
Pełny tekst źródłaZhao, Lijun, Wei Zhang i Ping Tang. "Application potential of GF-4 satellite images for water body extraction". W Remote Sensing of the Open and Coastal Ocean and Inland Waters, redaktorzy Robert J. Frouin i Hiroshi Murakami. SPIE, 2018. http://dx.doi.org/10.1117/12.2323444.
Pełny tekst źródłaShen, Li, i Changchun Li. "Water body extraction from Landsat ETM+ imagery using adaboost algorithm". W 2010 18th International Conference on Geoinformatics. IEEE, 2010. http://dx.doi.org/10.1109/geoinformatics.2010.5567762.
Pełny tekst źródłali, meilin, Jie Rui, Songkun Yang, li Ma, Shuangjun Chen i keke Jiang. "Method for inland water body extraction fused atrous spatial pyramid pooling". W International Conference on Internet of Things and Machine Learning (IoTML 2022), redaktorzy Hongzhi Wang i Xiangjie Kong. SPIE, 2023. http://dx.doi.org/10.1117/12.2673522.
Pełny tekst źródłaDing, Zhang, Ni Qi, Fang Dong, Li Jinhui, Yao Wei i Yuan Shenggui. "Application of multispectral remote sensing technology in surface water body extraction". W 2016 International Conference on Audio, Language and Image Processing (ICALIP). IEEE, 2016. http://dx.doi.org/10.1109/icalip.2016.7846565.
Pełny tekst źródłaZhao, Chunzhe, Xueying Li, Rong Xu i Jiang Xiong. "Water Body Extraction for the Landsat TM Imagery of Hulun Lake". W International Symposium on Automation, Information and Computing. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0011927800003612.
Pełny tekst źródła