Auswahl der wissenschaftlichen Literatur zum Thema „Water body extraction“
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Zeitschriftenartikel zum Thema "Water body extraction"
Luo, Yuanjiang, Ao Feng, Hongxiang Li, Danyang Li, Xuan Wu, Jie Liao, Chengwu Zhang, Xingqiang Zheng und Haibo Pu. „New deep learning method for efficient extraction of small water from remote sensing images“. PLOS ONE 17, Nr. 8 (05.08.2022): e0272317. http://dx.doi.org/10.1371/journal.pone.0272317.
Der volle Inhalt der QuelleYe, 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.
Der volle Inhalt der QuelleJiang, Wei, Yuan Ni, Zhiguo Pang, Xiaotao Li, Hongrun Ju, Guojin He, Juan Lv, Kun Yang, June Fu und 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.
Der volle Inhalt der QuelleNaik, B. Chandrababu, und B. Anuradha. „Extraction of Water-body Area from High-resolution Landsat Imagery“. International Journal of Electrical and Computer Engineering (IJECE) 8, Nr. 6 (01.12.2018): 4111. http://dx.doi.org/10.11591/ijece.v8i6.pp4111-4119.
Der volle Inhalt der QuelleZhang, Yonghong, Huanyu Lu, Guangyi Ma, Huajun Zhao, Donglin Xie, Sutong Geng, Wei Tian und 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.
Der volle Inhalt der QuelleYe, 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.
Der volle Inhalt der QuelleWeng, Yijie, Zongmei Li, Guofeng Tang und 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.
Der volle Inhalt der QuelleZhang, Q., X. Hu und 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 (05.12.2023): 889–94. http://dx.doi.org/10.5194/isprs-annals-x-1-w1-2023-889-2023.
Der volle Inhalt der QuelleHe, S. A., und Xiao Yan Zhu. „Preparation of Zirconia Fiber Body with Extrusion-Extraction Molding“. Key Engineering Materials 519 (Juli 2012): 291–96. http://dx.doi.org/10.4028/www.scientific.net/kem.519.291.
Der volle Inhalt der QuelleChe, Xianghong, Min Feng, Hao Jiang, Jia Song und 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.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleSpaceborne 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.
Der volle Inhalt der QuelleThesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2012.
Buchteile zum Thema "Water body extraction"
Jun, Wang, und 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.
Der volle Inhalt der QuelleLou, Linjiang, Chen Chen, Xinyuan Gao, Kun Liu, Minmin Li und 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.
Der volle Inhalt der QuelleHesham, Anas, und 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.
Der volle Inhalt der QuelleRithin Paul Reddy, K., Suda Sai Srija, R. Karthi und 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.
Der volle Inhalt der QuelleJakovljević, Gordana, und 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.
Der volle Inhalt der QuelleFang, Yiwei, Xin Lyu, Baogen Tong, Shengkai Gao, Xin Li, Xinyuan Wang und 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.
Der volle Inhalt der QuelleChandrababu Naik, B., Bairam Ravi Kumar, K. Vasu Babu, K. Purushotham Prasad und 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.
Der volle Inhalt der QuelleLi, Xiumei, Xianbin Liu, Lina Liu und 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.
Der volle Inhalt der QuelleGoel, Lavika, Daya Gupta und 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.
Der volle Inhalt der QuelleWei, Zhang, Wang Guanghui, Qi Jianwei und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Water body extraction"
Yuan Tian, Xiuwan Chen, Peng Luo und 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.
Der volle Inhalt der QuelleAahlen, 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.
Der volle Inhalt der QuelleArreola Esquivel, Mario Martin, Maricela Delgadillo, Carina Toxqui und Alfonso Padilla. „Index-based methods for water body extraction in satellite data“. In Applications of Digital Image Processing XLII, herausgegeben von Andrew G. Tescher und Touradj Ebrahimi. SPIE, 2019. http://dx.doi.org/10.1117/12.2529756.
Der volle Inhalt der QuelleWang, Yong, Yaqi Li und 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.
Der volle Inhalt der QuelleR, Nagaraj, und 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.
Der volle Inhalt der QuelleZhao, Lijun, Wei Zhang und 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, herausgegeben von Robert J. Frouin und Hiroshi Murakami. SPIE, 2018. http://dx.doi.org/10.1117/12.2323444.
Der volle Inhalt der QuelleShen, Li, und 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.
Der volle Inhalt der Quelleli, meilin, Jie Rui, Songkun Yang, li Ma, Shuangjun Chen und 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), herausgegeben von Hongzhi Wang und Xiangjie Kong. SPIE, 2023. http://dx.doi.org/10.1117/12.2673522.
Der volle Inhalt der QuelleDing, Zhang, Ni Qi, Fang Dong, Li Jinhui, Yao Wei und 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.
Der volle Inhalt der QuelleZhao, Chunzhe, Xueying Li, Rong Xu und 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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