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Статті в журналах з теми "Remote Sensing Image Data Analysis"
Fu, N., L. Sun, H. Z. Yang, J. Ma, and B. Q. Liao. "RESEARCH ON MULTI-SOURCE SATELLITE IMAGE DATABASE MANAGEMENT SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 565–68. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-565-2020.
Повний текст джерелаLi, Runya, and Shenglian Li. "Multimedia Image Data Analysis Based on KNN Algorithm." Computational Intelligence and Neuroscience 2022 (April 12, 2022): 1–8. http://dx.doi.org/10.1155/2022/7963603.
Повний текст джерелаKarimov, B., G. Karimova, and N. Amankulova. "Land Cover Classification Improvements by Remote Sensing Data Fusion." Bulletin of Science and Practice, no. 2 (February 15, 2023): 66–74. http://dx.doi.org/10.33619/2414-2948/87/07.
Повний текст джерелаVeljanovski, Tatjana, Urša Kanjir, and Krištof Oštir. "Object-based image analysis of remote sensing data." Geodetski vestnik 55, no. 04 (2011): 641–64. http://dx.doi.org/10.15292/geodetski-vestnik.2011.04.641-664.
Повний текст джерелаBazi, Yakoub, Gabriele Cavallaro, Begüm Demir, and Farid Melgani. "Learning from Data for Remote Sensing Image Analysis." International Journal of Remote Sensing 43, no. 15-16 (August 18, 2022): 5527–33. http://dx.doi.org/10.1080/01431161.2022.2131481.
Повний текст джерелаLukáš Brodský and Luboš, Borůvka. "Object-oriented Fuzzy Analysis of Remote Sensing Data for Bare Soil Brightness Mapping." Soil and Water Research 1, No. 3 (January 7, 2013): 79–84. http://dx.doi.org/10.17221/6509-swr.
Повний текст джерелаYu, Songyi, and Guotao Wang. "Study on the example segmentation method of remote sensing image based on neural network." Advances in Engineering Technology Research 6, no. 1 (June 12, 2023): 129. http://dx.doi.org/10.56028/aetr.6.1.129.2023.
Повний текст джерелаKhare, Smriti. "Remote Sensing Imagery Sensors and Image Interpretation." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 606–7. http://dx.doi.org/10.22214/ijraset.2021.38019.
Повний текст джерелаHutapea, Destri Yanti, and Octaviani Hutapea. "WATERMARKING METHOD OF REMOTE SENSING DATA USING STEGANOGRAPHY TECHNIQUE BASED ON LEAST SIGNIFICANT BIT HIDING." International Journal of Remote Sensing and Earth Sciences (IJReSES) 15, no. 1 (July 6, 2018): 63. http://dx.doi.org/10.30536/j.ijreses.2018.v15.a2824.
Повний текст джерелаTang, Yang, Jiongchao Yan, Yueqi Wu, Jie Hong, Lei Xu, and Zhangrui Lin. "Design of Remote Sensing Image Data Analysis and Processing Platform Based on Environmental Monitoring." Journal of Physics: Conference Series 2136, no. 1 (December 1, 2021): 012056. http://dx.doi.org/10.1088/1742-6596/2136/1/012056.
Повний текст джерелаДисертації з теми "Remote Sensing Image Data Analysis"
Fountanas, Leonidas. "Principal components based techniques for hyperspectral image data." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Dec%5FFountanas.pdf.
Повний текст джерелаCheriyadat, Anil Meerasa. "Limitations of principal component analysis for dimensionality-reduction for classification of hyperspectral data." Master's thesis, Mississippi State : Mississippi State University, 2003. http://library.msstate.edu/etd/show.asp?etd=etd-11072003-133109.
Повний текст джерелаRobert, Denis J. "Selection and analysis of optimal textural features for accurate classification of monochrome digitized image data /." Online version of thesis, 1989. http://hdl.handle.net/1850/11364.
Повний текст джерелаKHALIQ, ALEEM. "Advancements in Multi-temporal Remote Sensing Data Analysis Techniques for Precision Agriculture." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2839838.
Повний текст джерелаMarcellin, Michael W., Naoufal Amrani, Serra-Sagristà Joan, Valero Laparra, and Jesus Malo. "Regression Wavelet Analysis for Lossless Coding of Remote-Sensing Data." IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2016. http://hdl.handle.net/10150/621311.
Повний текст джерелаFischer, Manfred M., Sucharita Gopal, Petra Staufer-Steinnocher, and Klaus Steinocher. "Evaluation of Neural Pattern Classifiers for a Remote Sensing Application." WU Vienna University of Economics and Business, 1995. http://epub.wu.ac.at/4184/1/WSG_DP_4695.pdf.
Повний текст джерелаSeries: Discussion Papers of the Institute for Economic Geography and GIScience
Linden, Sebastian van der. "Investigating the potential of hyperspectral remote sensing data for the analysis of urban imperviousness." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2008. http://dx.doi.org/10.18452/15757.
Повний текст джерелаUrbanization is one of the most powerful and irreversible processes by which humans modify the Earth''s surface. Optical remote sensing is a main source of Earth observation products which help to better understand this dynamic process and its consequences. This work investigates the potential of airborne hyperspectral data to provide information on urban imperviousness that is needed for an integrated analysis of the coupled natural and human systems therein. For this purpose the complete processing workflow from preprocessing of the raw image to the generation of geocoded maps on land cover and impervious surface coverage is performed using Hyperspectral Mapper data acquired over Berlin, Germany. The traditional workflow for hyperspectral data is extended or modified at several points: a normalization of brightness gradients that are caused by directional reflectance properties of urban surfaces is included into radiometric preprocessing; support vector machines are used to classify five spectrally complex land cover classes without previous feature extraction or the definition of sub-classes. A detailed assessment of such maps is performed based on various reference products. Results show that the accuracy of derived maps depends on several steps within the processing workflow. For example, the support vector machine classification of hyperspectral data itself is accurate but geocoding without detailed terrain information introduces critical errors; impervious surface estimates correlate well with ground data but trees covering impervious surface below generally causes offsets; image segmentation does not enhance spectral classification accuracy of the spatially heterogeneous area but offers an interesting way of data compression and more time effective processing. Findings from this work help judging the reliability of data products and in doing so advance a possible extension of urban remote sensing approaches to areas where only little additional data exists.
Parshakov, Ilia. "Automatic class labeling of classified imagery using a hyperspectral library." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Geography, c2012, 2012. http://hdl.handle.net/10133/3372.
Повний текст джерелаvii, 93 leaves : ill., maps (some col.) ; 29 cm
Magnini, Luigi. "Remote sensing e object-based image analysis: metodologie di approccio per la creazione di standard archeologici." Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3423260.
Повний текст джерелаIl campo del remote sensing ha vissuto un incredibile sviluppo negli ultimi anni per merito della crescente qualità e varietà dei sensori e dell’abbattimento dei costi strumentali. Le potenzialità archeologiche sono state ben presto evidenti. Finora, l’interpretazione dei dati è rimasta però prerogativa dell’operatore umano, mediata dalle sue competenze e dalla sua esperienza. Il progressivo aumento di volume dei dataset (cd. “big data explosion”) e la necessità di lavorare su progetti territoriali ad ampia scala hanno reso ora indispensabile una revisione delle modalità di studio tradizionalmente impiegate in ambito archeologico. In questo senso, la ricerca presentata di seguito contribuisce alla valutazione delle potenzialità e dei limiti dell’emergente campo d’indagine dell’object-based image analysis (OBIA). Il lavoro si è focalizzato sulla definizione di protocolli OBIA per il trattamento di dati tridimensionali acquisiti tramite laser scanner aviotrasportato e terrestre attraverso l’elaborazione di un variegato spettro di casi di studio in grado di esemplificare le possibilità offerte dal metodo in archeologia. I risultati ottenuti hanno consentito di identificare, mappare e quantificare in modo automatico e semi-automatico le tracce del paesaggio di guerra nell’area intorno a Forte Luserna (TN) e il tessuto osteologico ricalcificato sui crani di due inumati della necropoli protostorica dell’Olmo di Nogara (VR). Infine, il metodo è stato impiegato per lo sviluppo di un modello predittivo per la localizzazione dei “punti di controllo” in ambiente montano, che è stato studiato per l’area occidentale dell’Altopiano di Asiago (VI) e in seguito riapplicato con successo nella conca di Bressanone (BZ). L’accuratezza dei risultati, verificati di volta in volta tramite ricognizioni a terra, validazione incrociata tramite analisi da remoto e comparazione con i dati editi in letteratura, ha confermato il potenziale della metodologia, consentendo di introdurre il concetto di Archaeological Object-Based Image Analysis (ArchaeOBIA), per rimarcare le specificità delle applicazioni object-based nell’ambito della disciplina archeologica.
Gapper, Justin J. "Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images." Chapman University Digital Commons, 2019. https://digitalcommons.chapman.edu/cads_dissertations/2.
Повний текст джерелаКниги з теми "Remote Sensing Image Data Analysis"
Processing of remote sensing data. Lisse: Balkema, 2003.
Знайти повний текст джерелаGirard, Michel-Claude. Processing of remote sensing data. Lisse: Balkema, 2001.
Знайти повний текст джерелаCanty, Morton John. Image analysis, classification, and change detection in remote sensing: With algorithms for ENVI/IDL. 2nd ed. Boca Raton: Taylor & Francis, 2010.
Знайти повний текст джерелаCanty, Morton John. Image analysis, classification, and change detection in remote sensing: With algorithms for ENVI/IDL. 2nd ed. Boca Raton: Taylor & Francis, 2010.
Знайти повний текст джерелаIEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data (2003 Greenbelt, Md.). 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data (a workshop honoring Professor David A. Landgrebe): NASA Goddard Space Flight Visitor Center, Greenbelt, Maryland, USA, 27-28 October 2003. Piscataway, NJ: IEEE, 2004.
Знайти повний текст джерелаJ, Lavreau, and Bardinet Claude, eds. Image analysis, geological control, and radiometric survey of LAND[S]AT TM data in Tanzania. Tervuren, Belgique: Musee royal de l'Afrique centrale, 1988.
Знайти повний текст джерелаDigital analysis of remotely sensed imagery. New York: McGraw-Hill, 2009.
Знайти повний текст джерелаImage analysis, classification, and change detection in remote sensing: With algorithms for ENVI/IDL. 2nd ed. Boca Raton: Taylor & Francis, 2010.
Знайти повний текст джерела2-D and 3-D image registration for medical, remote sensing, and industrial applications. Hoboken, NJ: J. Wiley & Sons, 2005.
Знайти повний текст джерелаA hierarchical object-based approach for urban land-use classification from remote sensing data. Enschede, Netherlands: ITC, 2003.
Знайти повний текст джерелаЧастини книг з теми "Remote Sensing Image Data Analysis"
Richards, John A., and Xiuping Jia. "Data Fusion." In Remote Sensing Digital Image Analysis, 293–312. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03978-6_12.
Повний текст джерелаRichards, John A. "Multispectral Transformations of Image Data." In Remote Sensing Digital Image Analysis, 127–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-662-02462-1_6.
Повний текст джерелаRichards, John A. "Fourier Transformation of Image Data." In Remote Sensing Digital Image Analysis, 148–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-662-02462-1_7.
Повний текст джерелаRichards, John A. "Multispectral Transformations of Image Data." In Remote Sensing Digital Image Analysis, 133–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-88087-2_6.
Повний текст джерелаRichards, John A. "Fourier Transformation of Image Data." In Remote Sensing Digital Image Analysis, 155–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-88087-2_7.
Повний текст джерелаRichards, John A., and Xiuping Jia. "Interpretation of Hyperspectral Image Data." In Remote Sensing Digital Image Analysis, 313–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03978-6_13.
Повний текст джерелаRichards, John A., and Xiuping Jia. "Multispectral Transformations of Image Data." In Remote Sensing Digital Image Analysis, 133–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03978-6_6.
Повний текст джерелаRichards, John A., and Xiuping Jia. "Fourier Transformation of Image Data." In Remote Sensing Digital Image Analysis, 155–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03978-6_7.
Повний текст джерелаRichards, John A. "The Interpretation of Digital Image Data." In Remote Sensing Digital Image Analysis, 69–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-662-02462-1_3.
Повний текст джерелаRichards, John A. "The Interpretation of Digital Image Data." In Remote Sensing Digital Image Analysis, 75–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-88087-2_3.
Повний текст джерелаТези доповідей конференцій з теми "Remote Sensing Image Data Analysis"
Teillet, P. M., Brian L. Markham, and Richard R. Irish. "Landsat radiometric cross-calibration: extended analysis of tandem image data sets." In Remote Sensing, edited by Roland Meynart, Steven P. Neeck, and Haruhisa Shimoda. SPIE, 2005. http://dx.doi.org/10.1117/12.626324.
Повний текст джерелаLiu, Zhigang, and Zhichao Sun. "Active one-class classification of remote sensing image." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.816115.
Повний текст джерелаSoergel, Uwe, and Ulrich Thoennessen. "Automatic geocoding of high-value targets using structural image analysis and GIS data." In Remote Sensing, edited by Sebastiano B. Serpico. SPIE, 1999. http://dx.doi.org/10.1117/12.373246.
Повний текст джерелаShao, Zhenfeng, and Deren Li. "A strategy of using remote sensing image to update the geographical data." In MIPPR 2005 Image Analysis Techniques, edited by Deren Li and Hongchao Ma. SPIE, 2005. http://dx.doi.org/10.1117/12.654553.
Повний текст джерелаZhang, Hongsheng, and Yan Li. "Shape-adaptive neighborhood classification method for remote sensing image." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.815857.
Повний текст джерелаDu, Qian. "Noise estimation for remote sensing image data analysis." In Optical Science and Technology, SPIE's 48th Annual Meeting, edited by Sylvia S. Shen and Paul E. Lewis. SPIE, 2004. http://dx.doi.org/10.1117/12.508101.
Повний текст джерелаLiu, Liangming, and Deren Li. "Drought analysis based on remote sensing and ancillary data." In Multispectral Image Processing and Pattern Recognition, edited by Qingxi Tong, Yaoting Zhu, and Zhenfu Zhu. SPIE, 2001. http://dx.doi.org/10.1117/12.441382.
Повний текст джерелаHe, Hui, and Xianchuan Yu. "A comparison of PCA/ICA for data preprocessing in remote sensing imagery classification." In MIPPR 2005 Image Analysis Techniques, edited by Deren Li and Hongchao Ma. SPIE, 2005. http://dx.doi.org/10.1117/12.652298.
Повний текст джерелаLiu, Tingting, Pingxiang Li, Liangpei Zhang, and Xu Chen. "Multi-spectral remote sensing image retrieval based on semantic extraction." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.815899.
Повний текст джерелаJackson, Philip T. G., Carl J. Nelson, Jens Schiefele, and Boguslaw Obara. "Runway detection in High Resolution remote sensing data." In 2015 9th International Symposium on Image and Signal Processing and Analysis (ISPA). IEEE, 2015. http://dx.doi.org/10.1109/ispa.2015.7306053.
Повний текст джерелаЗвіти організацій з теми "Remote Sensing Image Data Analysis"
Falconer, David G. L51774 Remote Sensing of Hazardous Ground Movement about Buried Gas Transmission Lines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), August 1997. http://dx.doi.org/10.55274/r0011973.
Повний текст джерелаKholoshyn, Ihor V., Olga V. Bondarenko, Olena V. Hanchuk, and Iryna M. Varfolomyeyeva. Cloud technologies as a tool of creating Earth Remote Sensing educational resources. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3885.
Повний текст джерелаCohen, Yafit, Carl Rosen, Victor Alchanatis, David Mulla, Bruria Heuer, and Zion Dar. Fusion of Hyper-Spectral and Thermal Images for Evaluating Nitrogen and Water Status in Potato Fields for Variable Rate Application. United States Department of Agriculture, November 2013. http://dx.doi.org/10.32747/2013.7594385.bard.
Повний текст джерелаDucey, Craig. Hierarchical Image Analysis and Characterization of Scaling Effects in Remote Sensing. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.399.
Повний текст джерелаLasko, Kristofer, and Elena Sava. Semi-automated land cover mapping using an ensemble of support vector machines with moderate resolution imagery integrated into a custom decision support tool. Engineer Research and Development Center (U.S.), November 2021. http://dx.doi.org/10.21079/11681/42402.
Повний текст джерелаSaltus, Allen, Maygarden Jr., Saucier Benjamin, and Roger T. Analysis and Technical Report of Remote Sensing Data for the USS Kinsman. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada375666.
Повний текст джерелаBorrett, Veronica, Melissa Hanham, Gunnar Jeremias, Jonathan Forman, James Revill, John Borrie, Crister Åstot, et al. Science and Technology for WMD Compliance Monitoring and Investigations. The United Nations Institute for Disarmament Research, December 2020. http://dx.doi.org/10.37559/wmd/20/wmdce11.
Повний текст джерелаCarroll, Herbert B., and Genliang Guo. A New Methodology for Oil and Gas Exploration Using Remote Sensing Data and Surface Fracture Analysis. Office of Scientific and Technical Information (OSTI), February 1999. http://dx.doi.org/10.2172/3244.
Повний текст джерелаSeginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7575271.bard.
Повний текст джерелаScott R. Reeves and Randal L. Billingsley. Identifying Oil Exploration Leads using Intergrated Remote Sensing and Seismic Data Analysis, Lake Sakakawea, Fort Berthold Indian Reservation, Willistion Basin. Office of Scientific and Technical Information (OSTI), February 2004. http://dx.doi.org/10.2172/925463.
Повний текст джерела