An objective analysis of support vector machine based classification for remote sensing. (English) Zbl 1140.62051

Summary: Accurate thematic classification is one of the most commonly desired outputs from remote sensing images. Recent research efforts to improve the reliability and accuracy of image classification have led to the introduction of the Support Vector Classification (SVC) scheme. SVC is a new generation of supervised learning methods based on the principle of statistical learning theory, which is designed to decrease uncertainty in the model structure and the fitness of data. We have presented a comparative analysis of SVC with the Maximum Likelihood Classification (MLC) method, which is the most popular conventional supervised classification technique. SVC is an optimization technique in which the classification accuracy heavily relies on identifying the optimal parameters.
Using a case study, we verify a method to obtain these optimal parameters such that SVC can be applied efficiently. We use multispectral and hyperspectral images to develop thematic classes of known lithologic units in order to compare the classification accuracy of both the methods. We have varied the training to testing data proportions to assess the relative robustness and the optimal training sample requirements of both the methods to achieve comparable levels of accuracy. The results of our study illustrated that SVC improved the classification accuracy, was robust and did not suffer from dimensionality issues such as the Hughes effect [see G.F.Hughes, IEEE Trans. Inf. Theory 14, 55–63 (1968)].


62H30 Classification and discrimination; cluster analysis (statistical aspects)
68T05 Learning and adaptive systems in artificial intelligence
62P99 Applications of statistics


Full Text: DOI


[1] 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] Benediktsson JA, Sveinsson JR, Arnason K (1995) Classification and feature extraction of AVIRIS data. IEEE Trans Geosci Remote Sens 33(5):1194–1205 · doi:10.1109/36.469483
[3] 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 · doi:10.1109/TGRS.2007.894550
[4] Chang CI (2003) Hyperspectral imaging: techniques for spectral detection and classification. Kluwer/Plenum, New York, 370 p
[5] 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 · doi:10.1080/01431169508954507
[6] Foody GM, Mathur A (2004) A relative evaluation of multiclass image classification by support vector machines. IEEE Trans Geosci Remote Sens 42:1335–1343 · doi:10.1109/TGRS.2004.827257
[7] Gunn SR (1998) Support vector machines for classification and regression. Technical Report, University of Southampton, 54 p
[8] Hord MR (1982) Digital image processing of remotely sensed data. Academic Press, New York, 256 p
[9] Huang C, Davis LS, Townshed JRG (2002) An assessment of support vector machines for land cover classification. Int J Remote Sens 23:725–749 · doi:10.1080/01431160110040323
[10] Hughes GF (1968) On the mean accuracy of statistical pattern recognizers. IEEE Trans Inf Theory 14:55–63 · doi:10.1109/TIT.1968.1054102
[11] Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification. National Taiwan University, 12 p
[12] Ikeda M, Dobson FW (1995) Oceanographic applications of remote sensing. CRC Press, Boca Raton, 492 p
[13] Jia X (1999) Adaptable class data representation for hyperspectral image classification. http://www.gisdevelopment.net/aars/acrs/1999/ts10/ts10109pf.htm
[14] Keerthi SS, Lin CJ (2003) Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput 15:1667–1689 · Zbl 1086.68569 · doi:10.1162/089976603321891855
[15] Kohavi R, Provost F (1998) Glossary of terms. Mach Learn 30(23):271–274 · doi:10.1023/A:1017181826899
[16] Lillesand TM, Kiefer RW, Chipman JW (2004) Remote sensing and image interpretation, 5th edn. Wiley, New York, p 724
[17] Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens, pp 1778–1790
[18] Mertie JB Jr (1940) The Goodnews platinum deposits, Alaska. US Geol Surv Bull 918:97
[19] Meyer D (2001) Support vector machines. R News, Volume 1/3. http://cran.r-project.org/doc/Rnews/Rnews_2001-3.pdf
[20] Murai S (1996) GIS workbook (fundamental course). Japan Association of Surveyors, Tokyo, 169 p
[21] Pal M, Mather PM (2005) Support vector machines for classification in remote sensing. Int J Remote Sens 26:1007–1011 · doi:10.1080/01431160512331314083
[22] Richards JA, Jia X (1998) Remote sensing digital image analysis, 3rd edn. Springer, Berlin, 63 p
[23] Schrader S, Pouncey R (1997) Erdas field guide, 4th edn. Erdas Inc., Atlanta Georgia, 686 p
[24] Schowengerdt RA (1983) Techniques for image processing and classification in remote sensing. Academic Press, New York, p 245
[25] Sherrod PH (2003) Classification and regression trees and support vector machines for predictive modeling and forecasting. DTREG program manual. www.dtreg.com
[26] Stewart JH, Carlson JE (1978) Geologic map of Nevada. Nevada Bureau of Mines and Geology, Map
[27] Vapnik VN (1995) The nature of statistical learning theory. Springer, New York, 188 p · Zbl 0833.62008
[28] 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 · doi:10.1016/S0034-4257(01)00305-4
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.