메뉴 건너뛰기




Volumn 40, Issue 4, 2008, Pages 409-424

An objective analysis of support vector machine based classification for remote sensing

Author keywords

Hyperspectral classification; Maximum likelihood; Multispectral; Remote sensing; Support vector machines

Indexed keywords

IMAGE CLASSIFICATION; MAXIMUM LIKELIHOOD; OPTIMIZATION; REMOTE SENSING; STATISTICAL METHODS; SUPERVISED LEARNING; SUPPORT VECTOR MACHINES;

EID: 42949094902     PISSN: 18748961     EISSN: 18748953     Source Type: Journal    
DOI: 10.1007/s11004-008-9156-6     Document Type: Article
Times cited : (160)

References (28)
  • 1
    • 85121066717 scopus 로고    scopus 로고
    • Barry P, Shippert P, Gorodetzky D, Beck R (2003) Draft Hyperion hyperspectral mapping exercise using atmospheric correction and end members from spectral libraries and regions of interest with data from Cuprite, Nevada. EO-1 User Guide, v 2.3, 74 p
  • 2
    • 0029370531 scopus 로고
    • Classification and feature extraction of AVIRIS data
    • JA Benediktsson JR Sveinsson K Arnason 1995 Classification and feature extraction of AVIRIS data IEEE Trans Geosci Remote Sens 33 5 1194 1205 10.1109/36.469483 Benediktsson JA, Sveinsson JR, Arnason K (1995) Classification and feature extraction of AVIRIS data. IEEE Trans Geosci Remote Sens 33(5):1194–1205
    • (1995) IEEE Trans Geosci Remote Sens , vol.33 , Issue.5 , pp. 1194-1205
    • Benediktsson, JA1    Sveinsson, JR2    Arnason, K3
  • 3
    • 34249810956 scopus 로고    scopus 로고
    • Classification of hyperspectral remote sensing data with primal semi-supervised SVMs: 4rth International Workshop on Pattern Recognition in Remote Sensing (PRRS’06), Hong Kong
    • M Chi L Bruzzone 2007 Classification of hyperspectral remote sensing data with primal semi-supervised SVMs: 4rth International Workshop on Pattern Recognition in Remote Sensing (PRRS’06), Hong Kong IEEE Trans Geosci Remote Sens 45 6 1870 1880 10.1109/TGRS.2007.894550 Chi M, Bruzzone L (2007) Classification of hyperspectral remote sensing data with primal semi-supervised SVMs: 4rth International Workshop on Pattern Recognition in Remote Sensing (PRRS’06), Hong Kong. IEEE Trans Geosci Remote Sens 45(6):1870–1880
    • (2007) IEEE Trans Geosci Remote Sens , vol.45 , Issue.6 , pp. 1870-1880
    • Chi, M1    Bruzzone, L2
  • 4
    • 0003984676 scopus 로고    scopus 로고
    • Hyperspectral imaging: techniques for spectral detection and classification
    • CI Chang 2003 Hyperspectral imaging: techniques for spectral detection and classification Kluwer/Plenum New York 370 p Chang CI (2003) Hyperspectral imaging: techniques for spectral detection and classification. Kluwer/Plenum, New York, 370 p
    • (2003)
    • Chang, CI1
  • 5
    • 0029473455 scopus 로고
    • The effect of training set size and composition on artificial neural net classification
    • G Foody MB McCullagh WB Yates 1995 The effect of training set size and composition on artificial neural net classification Int J Remote Sens 16 1707 1723 10.1080/01431169508954507 Foody G, McCullagh MB, Yates WB (1995) The effect of training set size and composition on artificial neural net classification. Int J Remote Sens 16:1707–1723
    • (1995) Int J Remote Sens , vol.16 , pp. 1707-1723
    • Foody, G1    McCullagh, MB2    Yates, WB3
  • 6
    • 3042654673 scopus 로고    scopus 로고
    • A relative evaluation of multiclass image classification by support vector machines
    • GM Foody A Mathur 2004 A relative evaluation of multiclass image classification by support vector machines IEEE Trans Geosci Remote Sens 42 1335 1343 10.1109/TGRS.2004.827257 Foody GM, Mathur A (2004) A relative evaluation of multiclass image classification by support vector machines. IEEE Trans Geosci Remote Sens 42:1335–1343
    • (2004) IEEE Trans Geosci Remote Sens , vol.42 , pp. 1335-1343
    • Foody, GM1    Mathur, A2
  • 7
    • 85121072591 scopus 로고    scopus 로고
    • Gunn SR (1998) Support vector machines for classification and regression. Technical Report, University of Southampton, 54 p
  • 8
    • 0003872771 scopus 로고
    • Digital image processing of remotely sensed data
    • MR Hord 1982 Digital image processing of remotely sensed data Academic Press New York 256 p Hord MR (1982) Digital image processing of remotely sensed data. Academic Press, New York, 256 p
    • (1982)
    • Hord, MR1
  • 9
    • 0037138473 scopus 로고    scopus 로고
    • An assessment of support vector machines for land cover classification
    • C Huang LS Davis JRG Townshed 2002 An assessment of support vector machines for land cover classification Int J Remote Sens 23 725 749 10.1080/01431160110040323 Huang C, Davis LS, Townshed JRG (2002) An assessment of support vector machines for land cover classification. Int J Remote Sens 23:725–749
    • (2002) Int J Remote Sens , vol.23 , pp. 725-749
    • Huang, C1    Davis, LS2    Townshed, JRG3
  • 10
    • 77957741951 scopus 로고
    • On the mean accuracy of statistical pattern recognizers
    • GF Hughes 1968 On the mean accuracy of statistical pattern recognizers IEEE Trans Inf Theory 14 55 63 10.1109/TIT.1968.1054102 Hughes GF (1968) On the mean accuracy of statistical pattern recognizers. IEEE Trans Inf Theory 14:55–63
    • (1968) IEEE Trans Inf Theory , vol.14 , pp. 55-63
    • Hughes, GF1
  • 11
    • 85121083867 scopus 로고    scopus 로고
    • Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification. National Taiwan University, 12 p
  • 12
    • 0003738660 scopus 로고
    • Oceanographic applications of remote sensing
    • M Ikeda FW Dobson 1995 Oceanographic applications of remote sensing CRC Press Boca Raton 492 p Ikeda M, Dobson FW (1995) Oceanographic applications of remote sensing. CRC Press, Boca Raton, 492 p
    • (1995)
    • Ikeda, M1    Dobson, FW2
  • 13
    • 85121069157 scopus 로고    scopus 로고
    • Jia X (1999) Adaptable class data representation for hyperspectral image classification. http://www.gisdevelopment.net/aars/acrs/1999/ts10/ts10109pf.htm
  • 14
    • 0037822222 scopus 로고    scopus 로고
    • Asymptotic behaviors of support vector machines with Gaussian kernel
    • SS Keerthi CJ Lin 2003 Asymptotic behaviors of support vector machines with Gaussian kernel Neural Comput 15 1667 1689 10.1162/089976603321891855 Keerthi SS, Lin CJ (2003) Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput 15:1667–1689
    • (2003) Neural Comput , vol.15 , pp. 1667-1689
    • Keerthi, SS1    Lin, CJ2
  • 15
    • 0001290841 scopus 로고    scopus 로고
    • Glossary of terms
    • R Kohavi F Provost 1998 Glossary of terms Mach Learn 30 23 271 274 Kohavi R, Provost F (1998) Glossary of terms. Mach Learn 30(23):271–274
    • (1998) Mach Learn , vol.30 , Issue.23 , pp. 271-274
    • Kohavi, R1    Provost, F2
  • 16
    • 0003760615 scopus 로고    scopus 로고
    • Remote sensing and image interpretation
    • TM Lillesand RW Kiefer JW Chipman 2004 Remote sensing and image interpretation 5 Wiley New York p 724 Lillesand TM, Kiefer RW, Chipman JW (2004) Remote sensing and image interpretation, 5th edn. Wiley, New York, p 724
    • (2004)
    • Lillesand, TM1    Kiefer, RW2    Chipman, JW3
  • 17
    • 85121074704 scopus 로고    scopus 로고
    • Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens, pp 1778–1790
  • 18
    • 2142730161 scopus 로고
    • The Goodnews platinum deposits, Alaska
    • JB Mertie Jr 1940 The Goodnews platinum deposits, Alaska US Geol Surv Bull 918 97 Mertie JB Jr (1940) The Goodnews platinum deposits, Alaska. US Geol Surv Bull 918:97
    • (1940) US Geol Surv Bull , vol.918 , pp. 97
    • Mertie, JB1
  • 19
    • 85121082063 scopus 로고    scopus 로고
    • Meyer D (2001) Support vector machines. R News, Volume 1/3. http://cran.r-project.org/doc/Rnews/Rnews_2001-3.pdf
  • 20
    • 85042520743 scopus 로고    scopus 로고
    • GIS workbook (fundamental course)
    • S Murai 1996 GIS workbook (fundamental course) Japan Association of Surveyors Tokyo 169 p Murai S (1996) GIS workbook (fundamental course). Japan Association of Surveyors, Tokyo, 169 p
    • (1996)
    • Murai, S1
  • 21
    • 13644256120 scopus 로고    scopus 로고
    • Support vector machines for classification in remote sensing
    • M Pal PM Mather 2005 Support vector machines for classification in remote sensing Int J Remote Sens 26 1007 1011 10.1080/01431160512331314083 Pal M, Mather PM (2005) Support vector machines for classification in remote sensing. Int J Remote Sens 26:1007–1011
    • (2005) Int J Remote Sens , vol.26 , pp. 1007-1011
    • Pal, M1    Mather, PM2
  • 22
    • 0003515302 scopus 로고    scopus 로고
    • Remote sensing digital image analysis
    • JA Richards X Jia 1998 Remote sensing digital image analysis 3 Springer Berlin 63 p Richards JA, Jia X (1998) Remote sensing digital image analysis, 3rd edn. Springer, Berlin, 63 p
    • (1998)
    • Richards, JA1    Jia, X2
  • 23
    • 0003588753 scopus 로고    scopus 로고
    • Erdas field guide
    • S Schrader R Pouncey 1997 Erdas field guide 4 Erdas Inc. Atlanta Georgia 686 p Schrader S, Pouncey R (1997) Erdas field guide, 4th edn. Erdas Inc., Atlanta Georgia, 686 p
    • (1997)
    • Schrader, S1    Pouncey, R2
  • 24
    • 0020918796 scopus 로고
    • Techniques for image processing and classification in remote sensing
    • RA Schowengerdt 1983 Techniques for image processing and classification in remote sensing Academic Press New York p 245 Schowengerdt RA (1983) Techniques for image processing and classification in remote sensing. Academic Press, New York, p 245
    • (1983)
    • Schowengerdt, RA1
  • 25
    • 85121087497 scopus 로고    scopus 로고
    • Sherrod PH (2003) Classification and regression trees and support vector machines for predictive modeling and forecasting. DTREG program manual. www.dtreg.com
  • 26
    • 85121070304 scopus 로고    scopus 로고
    • Stewart JH, Carlson JE (1978) Geologic map of Nevada. Nevada Bureau of Mines and Geology, Map
  • 27
    • 0003450542 scopus 로고
    • The nature of statistical learning theory
    • VN Vapnik 1995 The nature of statistical learning theory Springer New York 188 p Vapnik VN (1995) The nature of statistical learning theory. Springer, New York, 188 p
    • (1995)
    • Vapnik, VN1
  • 28
    • 0036113847 scopus 로고    scopus 로고
    • Classification using ASTER data and SVM algorithms – The case study of Beer Sheva, Israel
    • G Zhu DG Blumberg 2002 Classification using ASTER data and SVM algorithms–The case study of Beer Sheva, Israel Remote Sens Environ 80 233 240 10.1016/S0034-4257(01)00305-4 Zhu G, Blumberg DG (2002) Classification using ASTER data and SVM algorithms–The case study of Beer Sheva, Israel. Remote Sens Environ 80:233–240
    • (2002) Remote Sens Environ , vol.80 , pp. 233-240
    • Zhu, G1    Blumberg, DG2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.