Spatial and Quantitative Comparison of Topographically Derived Different Classification Algorithms Using AWiFS Data over Himalayas, India
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In recent years, the significant increase in research on spatial information is observed. Classification or clustering is one of the well-known methods in spatial data analysis. Traditionally, classifiers are generally based on per-pixel approaches and are not utilizing the spatial information within pixel, called mixels which is an important source of information to image classification. There are two foremost reasons behind the existence of mixels: (a) coarse or low spatial resolution of sensor and (b) topographic effects that recorded on optical satellite imagery due to differential terrain illuminations over rugged areas such as Himalayas. In the present study, different classification algorithms have been implemented to drive the impact of topography on them. Among various available, three algorithms for the mapping of snow cover region over north Indian Himalayas (India) are compared: (a) maximum likelihood classification (MLC) as supervised classifier; (b) k-mean clustering as unsupervised classifier; and (c) linear spectral mixing model (LSMM) as soft classifier. These algorithms have been implemented on AWiFS multispectral data, and analysis was carried out. The classification accuracy is estimated by the error matrices, and LSMM achieved higher accuracy (84.5–88.5%) as compared to MLC (81–84%) and k-mean (74–81%). The results highlight that topographically derived classifiers achieved better accuracy in mapping as compared to simple classifiers. The study has many applications in snow hydrology, glaciology and climatology of mountain topography.
KeywordsTopographic correction (TC) Classification algorithms Subpixel classification Himalayas Advance Wide Field Sensor (AWiFS) satellite data
The authors would like to thank Indian Remote Sensing (IRS) for their great efforts in developing and distributing remotely sensed AWiFS satellite data and their DEM products online to public for free downloading. Thanks are also due to United States Geological Survey (USGS) for providing ASTER Global DEM and Landsat 8 data for research and educational purposes.
Compliance with Ethical Standards
Conflict of interest
No potential conflict of interest was reported by the authors.
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