Narzędzia help

Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
first last
cannonical link button

http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-3a18b8ce-01b8-4dbd-a89b-ed2faae6d615

Czasopismo

Acta Geophysica

Tytuł artykułu

Volcanic ash cloud detection from MODIS image based on CPIWS method

Autorzy Liu, L.  Li, Ch.  Lei, Y.  Yin, J.  Zhao, J. 
Treść / Zawartość http://agp.igf.edu.pl/ http://link.springer.com/journal/volumesAndIssues/11600
Warianty tytułu
Języki publikacji EN
Abstrakty
EN Volcanic ash cloud detection has been a difficult problem in moderate-resolution imaging spectroradiometer (MODIS) multispectral remote sensing application. Principal component analysis (PCA) and independent component analysis (ICA) are effective feature extraction methods based on second-order and higher order statistical analysis, and the support vector machine (SVM) can realize the nonlinear classification in low-dimensional space. Based on the characteristics of MODIS multispectral remote sensing image, via presenting a new volcanic ash cloud detection method, named combined PCA-ICA-weighted and SVM (CPIWS), the current study tested the real volcanic ash cloud detection cases, i.e., Sangeang Api volcanic ash cloud of 30 May 2014. Our experiments suggest that the overall accuracy and Kappa coefficient of the proposed CPIWS method reach 87.20 and 0.7958%, respectively, under certain conditions with the suitable weighted values; this has certain feasibility and practical significance.
Słowa kluczowe
PL obraz teledetekcyjny   analiza głównych składowych   analiza składowych niezależnych   technika wektorów podtrzymujących   chmura pyłu wulkanicznego  
EN remote sensing image   principal component analysis (PCA)   independent component analysis (ICA)   support vector machine (SVM)   volcanic ash cloud  
Wydawca Instytut Geofizyki PAN
Springer
Czasopismo Acta Geophysica
Rocznik 2017
Tom Vol. 65, no. 1
Strony 151--163
Opis fizyczny Bibliogr. 36 poz.
Twórcy
autor Liu, L.
  • School of Computer Engineering and Science, Shanghai University, Shanghai, China
autor Li, Ch.
  • School of Computer Engineering and Science, Shanghai University, Shanghai, China, david-0904@163.com
autor Lei, Y.
  • School of Computer Engineering and Science, Shanghai University, Shanghai, China
autor Yin, J.
  • Earthquake Administration of Shanghai Municipality, Shanghai, China
autor Zhao, J.
  • School of Computer Engineering and Science, Shanghai University, Shanghai, China
Bibliografia
1. Centre LVAA (2014), London volcanic ash advisory centre (VAAC), http://www.metoffice.gov.uk/aviation/vaac/. Accessed 14 Aug 2014
2. Chang CI, Chiang SS, Smith JA, Ginsberg IW (2002) Linear spectral random mixture analysis for hyperspectral imagery. IEEE Trans Geosci Remote 40(2):375–392. doi:10.1109/WHISPERS.2009.5289096
3. Christopher SA, Feng N, Naeger A, Johnson B, Marenco F (2012) Satellite remote sensing analysis of the 2010 Eyjafjallajökull volcanic ash cloud over the North Sea during 4–18 May 2010. J Biol Chem 117(D20):401–409. doi:10.1029/2011JD016850
4. Corradini S, Spinetti C, Carboni E (2008) Mt. Etna tropospheric ash retrieval and sensitivity analysis using moderater resolution imaging spectroradiometer measurements. J Appl Remote Sens 2(1):23550–23570. doi:10.1117/1.3046674
5. Corradini S, Merucci L, Folch A (2011) Volcanic ash cloud properties: comparison between MODIS satellite retrievals and FALL3D transport model. IEEE Geosci Remote Sens Lett 8(2):248–252. doi:10.1109/LGRS.2010.2064156
6. Duda T, Canty M (2002) Unsupervised classification of satellite imagery: choosing a good algorithm. Int J Remote Sens 23(11):2193–2212. doi:10.1080/01430060110078467
7. Ellrod GP (2004) Impact on volcanic ash detection caused by the loss of the 12.0 µm “Split Window” band on GOES imagers. J Volcanol Geotherm Res 135(1–2):91–103. doi:10.1016/j.jvolgeores.2003.12.009
8. Ellrod GP, Schreiner AJ (2004) Volcanic ash detection and cloud top height estimates from the GOES-12 imager: coping without a 12 µm infrared band. Geophys Res Lett 31(15):1–4. doi:10.1029/2004GL020395
9. Heblinski J, Schmieder K, Heege T, Agyemang TK, Sayadyan H, Vardanyan L (2011) High-resolution satellite remote sensing of littoral vegetation of Lake Sevan (Armenia) as a basis for monitoring and assessment. Hydrobiologia 61(1):97–111. doi:10.1007/s10750-010-0466-6
10. Hillger DW, Clark J (2002a) Principal component image analysis of MODIS for volcanic ash. Part I: most important bands and implications for future GOES imagers. J Appl Meteorol 41(1):985–1001. doi:10.1175/1520-0450(2002)041<0985:PCIAOM>2.0.CO;2
11. Hillger DW, Clark J (2002b) Principal component image analysis of MODIS for volcanic ash. Part II: simulation of current GOES and GOES-M imagers. J Appl Meteorol 41(10):1003–1010. doi:10.1175/1520-0450(2002)041<1003:PCIAOM>2.0.CO;2
12. Hyvarinen A (1999) Survey on independent component analysis. Neural Comput Surv 22(2):94–128
13. Hyvarinen A, Oja E (2000) Independent component analysis: algorithms and applications. Neural Netw 13(4–5):411–430. doi:10.1016/S0893-6080(00)00026-5
14. Lee TW, Girolami M, Sejnowski TJ (1999) Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Comput 11(2):417–441. doi:10.1162/089976699300016719
15. Li CF, Yin JY (2013) Variational Bayesian independent component analysis-support vector machine for remote sensing classification. Comput Electr Eng 39(3):717–726. doi:10.1016/j.compeleceng.2012.10.004
16. Li CF, Dai YY, Zhao JY, Yin Y, Zhou SQ (2014) Remote sensing detection of volcanic ash cloud using independent component analysis. Seismol Geol 36(1):137–142. doi:10.3969/j.issn.0253-4967.2014.01.011
17. Li CF, Dai YY, Zhao JJ, Zhou SQ, Yin JY, Xue D (2015) Remote sensing monitoring of volcanic ash clouds based on PCA method. Acta Geophys 63(2):432–450. doi:10.2478/s11600-014-0257-y
18. Liang L, Yang MH, Li YF (2010) Hyperspectral remote sensing image classification based on ICA and SVM algorithm. Spectrosc Spectr Anal 30(10):2724–2728. doi:10.3964/j.issn.1000-0593(2010)10-2724-05
19. Mackie S, Millington S, Watson IM (2014) How assumed composition affects the interpretation of satellite observations of volcanic ash. Meteorol Appl 21(1):20–29. doi:10.1002/met.1445
20. Montopoli M, Cimini D, Lamantea M, Herzog M, Graf HF, Marzano FS (2013) Microwave radiometric remote sensing of volcanic ash clouds from space: model and data analysis. IEEE Trans Geosci Remote 51(9):4678–4691. doi:10.1109/TGRS.2013.2260343
21. Mountrakis G, Irn J, Ogole C (2011) Support vector machines in remote sensing: a review. ISPRS J Photogramm 66(3):247–249. doi:10.1016/j.ISPRSJPRS.2010.11.001
22. Pavolonis MJ, Heidinger AK, Sieglaff J (2013) Automated retrievals of volcanic ash and dust cloud properties from upwelling infrared measurements. J Geophys Res 118(3):1436–1458. doi:10.1002/JGRD.50173
23. Prata AJ, Prata AT (2012) Eyjafjallajökull volcanic ash concentrations determined using spin enhanced visible and infrared imager measurements. J Geophys Res 117(D20):2156–2202. doi:10.1029/2011JD016800
24. Sahin M, Yildiz BY, Senkal O, Pestemalci V (2012) Modeling and remote sensing of land surface temperature in Turkey. J Indian Soc Remote 40(3):399–409. doi:10.1007/s12524-011-0158-3
25. Sanchez-Azofeifa A, Rivard B, Wright J, Feng JL, Li PJ, Chong MM, Bohlman SA (2011) Estimation of the distribution of Tabebuia guayacan (Bignoniaceae) using high-resolution remote sensing imagery. Sensors 11(4):3831–3851. doi:10.3390/s110403831
26. Schumann U, Weinzierl B, Reitebuch O, Schlager H, Minikin A, Forster C, Baumann R, Sailer T, Graf K, Mannstein H, Voigt C, Rahm S, Simmet R, Scheibe M, Lichtenstern M, Stock P, Rüba H, Schäuble D, Tafferner A, Rautenhaus M, Gerz T, Ziereis H, Krautstrunk M, Mallaun C, Gayet J-F, Lieke K, Kandler K, Ebert M, Weinbruch S, Stohl A, Gasteiger J, Groß S, Freudenthaler V, Wiegner M, Ansmann A, Tesche M, Olafsson H, Sturm K (2011) Airborne observations of the Eyjafjalla volcano ash cloud over Europe during air space closure in April and May 2010. Atmos Chem Phys 11(5):2245–2279. doi:10.5194/acp-11-2245-2011
27. Segl K, Roessner S, Heiden U (2003) Fusion of spectral and shape features for identification of urban surface cover types using reflective and thermal hyperspectral data. ISPRS J Photogramm 58(1–2):99–112. doi:10.1016/S0924-2716(03)00020-0
28. Spinetti C, Barsotti S, Neri A, Buongiorno MF, Doumaz F, Nannipieri L (2013) Investigation of the complex dynamics and structure of the 2010 Eyjafjallajökull volcanic ash cloud using multispectral images and numerical simulations. J Geophys Res 118(10):4729–4747. doi:10.1002/jgrd.50328
29. Wang XM, Zeng SG, Xia DS (2006) Remote sensing image classification based on a loose modified fast ICA algorithm. J. Comput Res Dev 43(4):708–715 (in Chinese)
30. Wang ZQ, Liu XQ, Zhang GL, Wang ZJ (2007) Face recognition based on PCA and ICA. J Huazhong Nor Univ 41(3):373–376 (in Chinese)
31. Ward CA, Starks SA (1999) An approach to predict Africanized honey bee migration using remote sensing. Comput Electr Eng 26(1):33–45. doi:10.1016/S0045-7906(99)00028-2
32. Wen XP, Yang XF, Hu GD (2011) Relationship between land cover ration and urban heat island from remote sensing and automatic weather stations data. J Ind Soc Remote 39(2):193–201. doi:10.1007/s12524-011-0076-4
33. Western LM, Watson MI, Francis PN (2015) Uncertainty in two-channel infrared remote sensing retrievals of a well-characterised volcanic ash cloud. Bull Volcanol 77(8):1–12. doi:10.1007/s00445-015-0950-y
34. Xu YM, Qin ZH, Wan HX (2010) Spatial and temporal dynamics of urban heat island and their relationship with land cover changes in urbanization process: a case study in Suzhou, China. J Ind Soc Remote 38(4):654–663. doi:10.1007/s12524-011-0073-7
35. Yi WB, Tang H, Chen YH (2011) An object-oriented semantic clustering algorithm for high-resolution remote sensing images using the aspect model. IEEE Geosci Remote Sens Lett 8(3):522–526. doi:10.1109/LGRS.2010.2090034
36. Zhao Y, Liang Y, Ma BJ, Li YS, Wu XJ (2014) Identification of Icelandic volcanic ash cloud based on FY-3A remote sensing data. Acta Petrol Sin 30(12):3693–3700
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-3a18b8ce-01b8-4dbd-a89b-ed2faae6d615
Identyfikatory
DOI 10.1007/s11600-017-0013-1