Abstract
Abstract. The Advanced Land Observing Satellite (ALOS) is developed by the Japanese Aerospace Exploration Agency (JAXA) which was launched in the year 2006 for the Earth observation and exploration purpose. The ALOS was carrying PRISM, AVNIR-2 and PALSAR sensors for this purpose. PALSAR is L-Band synthetic aperture radar (SAR). The PALSAR sensor is designed in a way that it can work in all weather conditions with a resolution of 10 meters. In this research work we have made an investigation on the accuracy obtained from the various supervised classification techniques. We have compared the accuracy obtained by classifying the ALOS PALSAR data of the Roorkee region of Uttarakhand, India. The training ROI’S (Region of Interest) are created manually with the assistance of ArcGIS Earth and for the testing purpose, we have used the Global positioning system (GPS) coordinates of the region. Supervised classification techniques included in this comparison are Parallelepiped classification (PC), Minimum distance classification (MDC), Mahalanobis distance classification (MaDC), Maximum likelihood classification (MLC), Spectral angle mapper (SAM), Spectral information divergence (SID) and Support vector machine (SVM). Later, through the post classification confusion matrix accuracy assessment test is performed and the corresponding value of the kappa coefficient is obtained. In the result, we have concluded MDC as best in term of overall accuracy with 82.3634% and MLC with a kappa value of 0.7591. Finally, a peculiar relationship is developed in between classification accuracy and kappa coefficient.
Highlights
Image classification into several categories or classes followed by an assessment of classification accuracy is complex as well as an interesting area of research in the field of remote sensing (RS) [1]
We have investigated the relation in between the kappa coefficient and the classification accuracy when the classification is made through various supervised classification techniques
Phased Array L-band Synthetic Aperture Radar (PALSAR) consists of some unique features it is designed to operate in three different modes i.e. fine, scan-synthetic aperture radar (SAR) and polarimetric its center frequency of operation is 1270 MHz, chirp bandwidth is 14 and 28 MHz, range resolution is 7 to 100 m, incidence angle can vary from 8 to 60 degree, observational swath vary from 40 to 350 km, bit length vary from 3 to 5 bits, data rate vary from 120 Mbps to 240 Mbps
Summary
Image classification into several categories or classes followed by an assessment of classification accuracy is complex as well as an interesting area of research in the field of remote sensing (RS) [1]. H.Jiao [20] used a combination of the SID and SAM supervised classification techniques to develop a novel algorithm for the computer-based spectral encoding. For the estimation of the accuracy post classification confusion matrix and a kappa coefficient of the classified data is created. The confusion matrix consists of user accuracy [30], producer accuracy [30], omission error [30] and commission error [30] which are computed for the individual class of the classified image, the overall accuracy. The value of the obtained kappa coefficient is related with the accuracy of various supervised technique to establish a relationship pattern in between classification accuracy and the kappa coefficients
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