Добірка наукової літератури з теми "Ocean synoptic feature extraction"
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Статті в журналах з теми "Ocean synoptic feature extraction"
Chen, Xi, Shaojie Sun, Jun Zhao, and Bin Ai. "Spectral Discrimination of Pumice Rafts in Optical MSI Imagery." Remote Sensing 14, no. 22 (November 18, 2022): 5854. http://dx.doi.org/10.3390/rs14225854.
Повний текст джерелаShaji, C., and A. Gangopadhyay. "Synoptic Modeling in the Eastern Arabian Sea during the Southwest Monsoon Using Upwelling Feature Models." Journal of Atmospheric and Oceanic Technology 24, no. 5 (May 1, 2007): 877–93. http://dx.doi.org/10.1175/jtech1984.1.
Повний текст джерелаMa, Hualin, and Liyan Zhang. "Ocean SAR Image Segmentation and Edge Gradient Feature Extraction." Journal of Coastal Research 94, sp1 (September 9, 2019): 141. http://dx.doi.org/10.2112/si94-028.1.
Повний текст джерелаWang, Xiong Liang, and Chun Ling Wang. "Extraction of Ocean Fronts Based on Empirical Mode Decomposition." Applied Mechanics and Materials 701-702 (December 2014): 303–7. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.303.
Повний текст джерелаIbebuchi, Chibuike Chiedozie, and Itohan-Osa Abu. "Relationship between synoptic circulations and the spatial distributions of rainfall in Zimbabwe." AIMS Geosciences 9, no. 1 (2022): 1–15. http://dx.doi.org/10.3934/geosci.2023001.
Повний текст джерелаDai, Panxi, and Ji Nie. "A Global Quasigeostrophic Diagnosis of Extratropical Extreme Precipitation." Journal of Climate 33, no. 22 (November 15, 2020): 9629–42. http://dx.doi.org/10.1175/jcli-d-20-0146.1.
Повний текст джерелаJinkerson, Richard A., Stephen L. Abrams, Leonidas Bardis, Chryssostomos Chryssostomidis, Andre Cldment, Nicholas M. Patrikalakis, and Franz-Erich Wolter. "Inspection and Feature Extraction of Marine Propellers." Journal of Ship Production 9, no. 02 (May 1, 1993): 88–106. http://dx.doi.org/10.5957/jsp.1993.9.2.88.
Повний текст джерелаSpensberger, Clemens, and Thomas Spengler. "Feature-Based Jet Variability in the Upper Troposphere." Journal of Climate 33, no. 16 (August 15, 2020): 6849–71. http://dx.doi.org/10.1175/jcli-d-19-0715.1.
Повний текст джерелаMATSUOKA, Daisuke, Fumiaki ARAKI, Shinichiro KIDA, Hideharu SASAKI, and Bunmei TAGUCHI. "J013024 Feature Extraction and Visualization of Ocean Currents via Cluster Analysis." Proceedings of Mechanical Engineering Congress, Japan 2013 (2013): _J013024–1—_J013024–3. http://dx.doi.org/10.1299/jsmemecj.2013._j013024-1.
Повний текст джерелаGonzález-Alemán, Juan J., Francisco Valero, Francisco Martín-León, and Jenni L. Evans. "Classification and Synoptic Analysis of Subtropical Cyclones within the Northeastern Atlantic Ocean*." Journal of Climate 28, no. 8 (April 7, 2015): 3331–52. http://dx.doi.org/10.1175/jcli-d-14-00276.1.
Повний текст джерелаДисертації з теми "Ocean synoptic feature extraction"
Jastram, Michael Oliver. "Inspection and feature extraction of marine propellers." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/42632.
Повний текст джерелаGuo, Da 1976. "Automated feature extraction in oceanographic visualization." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/33438.
Повний текст джерелаIncludes bibliographical references (leaves 141-147).
The ocean is characterized by a multitude of powerful, sporadic biophysical dynamical events; scientific research has reached the stage that their interpretation and prediction is now becoming possible. Ocean prediction, analogous to atmospheric weather prediction but combining biological, chemical and physical features is able to help us understand the complex coupled physics, biology and acoustics of the ocean. Applications of the prediction of the ocean environment include exploitation and management of marine resources, pollution control such as planning of maritime and naval operations. Given the vastness of ocean, it is essential for effective ocean prediction to employ adaptive sampling to best utilize the available sensor resources in order to minimize the forecast error. It is important to concentrate measurements to the regions where one can witness features of physical or biological significance in progress. Thus automated feature extraction in oceanographic visualization can facilitate adaptive sampling by presenting the physically relevant features directly to the operation planners. Moreover it could be used to help automate adaptive sampling. Vortices (eddies and gyres) and upwelling, two typical and important features of the ocean, are studied.
(cont.) A variety of feature extraction methods are presented, and those more pertinent to this study are implemented, including derived field generation and attribute set extraction. Detection results are evaluated in terms of accuracy, computational efficiency, clarity and usability. Vortices, a very important flow feature is the primary focus of this study. Several point-based and set-based vortex detection methods are reviewed. A set-based vortex core detection method based on geometric properties of vortices is applied to both classical vortex models and real ocean models. The direction spanning property, which is a geometric property, guides the detection of all the vortex core candidates, and the conjugate pair eigenvalue method is responsible for filtering out the false positives from the candidate set. Results show the new method to be analytically accurate and practically feasible, and superior to traditional point-based vortex detection methods. Detection methods of streamlines are also discussed. Using the novel cross method or the winding angle method, closed streamlines around vortex cores can be detected.
(cont.) Therefore, the whole vortex area, i.e., the combination of vortex core and surrounding streamlines, is detected. Accuracy and feasibility are achieved through automated vortex detection requiring no human inspection. The detection of another ocean feature, upwelling, is also discussed.
by Da Guo.
S.M.
Lambhate, Devyani. "Deep Convolutional and Generative Networks for Ocean Synoptic Feature Extraction and Super Resolution from Remotely Sensed Images." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5683.
Повний текст джерелаTai, Chih-Chiang, and 戴志強. "A Study of Linear Feature Extraction on Ocean Surface Satellite Image Using Spatial Information Techniques." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/07554551120967948695.
Повний текст джерела義守大學
資訊管理學系碩士班
95
Modern techniques of satellite image acquisition have been of great advance lately, which provide a great amount of images with a higher resolution both in spatial and spectral resolution. However, the rate of utilizing the existed images has not yet been sufficient in comparison to the rate of obtaining them. Hence, issues in using automated method of linear feature extraction for replacing manual process have drawn a great deal of attentions in this area lately. The purpose of this study is to develop an integrated method for extracting linear features of oceanic internal waves from satellite imagery using spatial information techniques, which include: wavelet transform based de-noise, Multiscale Retinex (MSR), and linear feature extraction (LEF). To evaluate the performance of the integrated method, the extracted linear features will be vectorized and overlapped with the original image in the Geographic Information System (GIS) to investigate the position discrepancy between them and the true features’ boundary. The results show that the MSR method provides enhanced image with improved color contrast and brightness, which result in a better quality of extracted linear features. Finally, we evaluate the performance of feature extraction using both the Canny method and the Wavelet Transform Modulus Maxima (WTMM) method. It is shown that the Canny method is superior to the WTMM method in terms of visualization quality and positioning accuracy.
Частини книг з теми "Ocean synoptic feature extraction"
Huynh, Quyen, Walter Greene, and John Impagliazzo. "Feature Extraction and Classification of Underwater Acoustic Signals." In Full Field Inversion Methods in Ocean and Seismo-Acoustics, 183–88. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-015-8476-0_30.
Повний текст джерелаТези доповідей конференцій з теми "Ocean synoptic feature extraction"
Shi, Hai-Quan, Jing-He Chen, and Li-Tao Yang. "Feature Vector Generation of Underwater Acoustic Signal based on Multi-Domain Feature Extraction." In 2021 OES China Ocean Acoustics (COA). IEEE, 2021. http://dx.doi.org/10.1109/coa50123.2021.9519920.
Повний текст джерелаGui, Feng, and QiWei Lin. "Morphological theory in image feature extraction." In Third International Asia-Pacific Environmental Remote Sensing Remote Sensing of the Atmosphere, Ocean, Environment, and Space, edited by Stephen G. Ungar, Shiyi Mao, and Yoshifumi Yasuoka. SPIE, 2003. http://dx.doi.org/10.1117/12.468079.
Повний текст джерелаWang, Wenbo, Sichun Li, Jianshe Yang, Zhao Liu, and Weicun Zhou. "Feature extraction of underwater target in auditory sensation area based on MFCC." In 2016 IEEE/OES China Ocean Acoustics (COA). IEEE, 2016. http://dx.doi.org/10.1109/coa.2016.7535736.
Повний текст джерелаXiangdong Jiang. "Noise detection and feature extraction method for underwater vehicle engine speedup radiated noise." In 2016 IEEE/OES China Ocean Acoustics (COA). IEEE, 2016. http://dx.doi.org/10.1109/coa.2016.7535629.
Повний текст джерелаMu, Lin, Yuan Peng, Mengran Qiu, Xuemeng Yang, Chen Hu, and Fengzhen Zhang. "Study on modulation spectrum feature extraction of ship radiated noise based on auditory model." In 2016 IEEE/OES China Ocean Acoustics (COA). IEEE, 2016. http://dx.doi.org/10.1109/coa.2016.7535765.
Повний текст джерелаZhao, Peng, Xun Yang, Yan Chen, Ling Tong, and Lei He. "Feature extraction and classification of ocean oil spill based on SAR image." In IGARSS 2016 - 2016 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2016. http://dx.doi.org/10.1109/igarss.2016.7729380.
Повний текст джерелаHuan Wang and Junhui Deng. "Feature extraction of complex ocean flow field using the helmholtz-hodge decomposition." In 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW). IEEE, 2014. http://dx.doi.org/10.1109/icmew.2014.6890546.
Повний текст джерелаRen, Huimin, Fangfang Li, Bing Han, Wen Hong, Yingbo Dong, and Bitong Wu. "Ocean Oil Spill Classification with Polarimetric SAR Based on VGG16 Multi-Feature Extraction." In 2021 SAR in Big Data Era (BIGSARDATA). IEEE, 2021. http://dx.doi.org/10.1109/bigsardata53212.2021.9574126.
Повний текст джерелаBostater, Charles R., and Teddy Ghir. "Coastal water feature extraction using airborne hyperspectral imagery in shallow estuarine water." In Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2023, edited by Charles R. Bostater and Xavier Neyt. SPIE, 2023. http://dx.doi.org/10.1117/12.2680314.
Повний текст джерелаZhang Bentao, Chen Biao, and Gao Guoxing. "Research on the spaceborne SAR image processing and feature extraction for ocean fronts detection." In 2010 International Conference On Computer and Communication Technologies in Agriculture Engineering (CCTAE). IEEE, 2010. http://dx.doi.org/10.1109/cctae.2010.5544849.
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