RANDOM FOREST CLASSIFICATION OF JAMBI AND SOUTH SUMATERA USING ALOS PALSAR DATA
DOI:
https://doi.org/10.30536/j.ijreses.2013.v10.a1852Keywords:
Land cover, ALOS-PALSAR, random forest (RF), classification, remote sensingAbstract
Recently, Synthetic Aperture Radar (SAR) satellite imaging has become an increasing
popular data source especially for land cover mapping because its sensor can penetrate clouds, haze,
and smoke which a serious problem for optical satellite sensor observations in the tropical areas. The
objective of this study was to determine an alternative method for land cover classification of ALOS
PALSAR data using Random Forest (RF) classifier. RF is a combination (ensemble) of tree predictors
that each tree predictor depends on the values of a random vector sampled independently and with the
same distribution for all trees in the forest. In this paper, the performance of the RF classifier for land
cover classification of a complex area was explored using ALOS PALSAR data (25m mosaic, dual
polarization) in the area of Jambi and South Sumatra, Indonesia. Overall accuracy of this method was
88.93%, with producer’s accuracies for forest, rubber, mangrove & shrubs with trees, cropland, and
water classes were greater than 92%.
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