Watanachaturaporn, Pakorn

Preferred name

Watanachaturaporn, Pakorn

Alternative Name

Watanachaturaporn, P.

Main Affiliation

School of Engineering

Email

pakorn.wa@kmitl.ac.th

Now showing 1 - 2 of 2

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Sub-pixel land cover classification using support vector machines
(2006-12-01)
Watanachaturaporn, Pakorn
;
Arora, Manoj K.
;
Varshney, Pramod K.
Over the last few years, support vector machines (SVMs) have shown a great potential as classifiers for remotely sensed data. Generally, these have been used to perform conventional hard classification where each pixel is allocated to only one class. Remote sensing images, particularly at coarse spatial resolutions, are contaminated with mixed pixels that contain more than one class on the ground. Hard classification process may result in erroneous classification of images dominated by mixed pixels. Therefore, sub-pixel classification approaches that decompose the pixel into its class constituents in the form of class proportions have been advocated. In this paper, we propose a SVM based algorithm for sub-pixel land cover classification. The proposed SVM based algorithm uses probability estimates for multiclass classification by pairwise coupling. The algorithm is employed to produce sub-pixel land cover classification from a Landsat ETM+ image. Classification accuracy achieved is assessed using three measures, namely, the overall accuracy obtained from a fuzzy error matrix, the squared correlation coefficient, and the root mean squared error. The results are compared with the posterior probabilities derived from the maximum likelihood classifier (MLC) and the fuzzy classification based on MLC. Our experiments show that accuracy obtained from the proposed algorithm is significantly higher than the two bench-marked classifiers. Thus, the outputs from SVM based algorithm can be used to reflect the actual class composition of the pixels on ground.
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Multisource classification using support vector machines: An empirical comparison with decision tree and neural network classifiers
(2008-01-01)
Watanachaturaporn, Pakorn
;
Arora, Manoj K.
;
Varshney, Pramod K.
Remote sensing image classification has proven to be attractive for extracting useful thematic information such as landcover. However, often for a given application, spectral information acquired by a remote sensing sensor may not be sufficient to derive accurate information. Incorporation of data from other sources such as a digital elevation model (DEM), and geophysical and geological data may assist in achieving more accurate land-cover classification from remote sensing images. Recently, support vector machines (SVM) have been proposed as an alternative for classification of remote sensing data, and the results are promising. In this paper, we employ the SVM algorithm to perform multi-source classification. An IRS-1C LISS III image along with normalized differenced vegetation index (NDVI) image and DEM are used to produce a land-cover classification for a region in the Himalayas. The accuracy of SVM-based multi-source classification is compared with several other non-parametric algorithms namely a decision tree classifier, and back propagation and radial basis function neural network classifiers. The well-known kappa coefficient of agreement is used to assess classification accuracy. The differences in the kappa coefficient of classifiers have been statistically evaluated using a pairwise Z-test. The results show a significant increase in the accuracy of the SVM based classifier on incorporation of ancillary data over classification performed solely on the basis of spectral data from remote sensing sensors. © 2008 American Society for Photogrammetry and Remote Sensing.