A Study on Tropical Land Cover Classification Using ALOS PALSAR 50 m Ortho-Rectified Mosaic Data

Lan Mi¹, Nguyen Thanh Hoan², Ryutaro Tateishi¹, Kotaro Iizuka¹, Bayan Alsaaideh¹, Toshiyuki Kobayashi¹

¹Center for Environmental Remote Sensing, Chiba University, Chiba, Japan.
²Institute of Geography Vietnam Academy of Science and Technology (VAST), Hanoi, Vietnam.

DOI: 10.4236/ars.2014.33014                

Abstract

The main objective of this study is to find better classifier of mapping tropical land covers using Synthetic Aperture Radar (SAR) imagery. The data used are Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) 50 m ortho-rectified mosaic data. Training data for forest, herbaceous, agriculture, urban and water body in the test area located in Kalimantan were collected. To achieve more accurate classification, a modified slope correction formula was created to calibrate the intensity distortions of SAR data. The accuracy of two classifiers called Sequential Minimal Optimization (SMO) and Random Forest (RF) were applied and compared in this study. We focused on object-based approach due to its capability of providing both spatial and spectral information. Optimal combination of features was selected from 32 sets of features based on layer value, texture and geometry. The overall accuracy of land cover classification using RF classifier and SMO classifier was 46.8% and 55.6% respectively, and that of forest and non-forest classification was 74.4% and 79.4% respectively. This indicates that RF classifier has better performance than SMO classifier.

Keywords

Slope Correction, Land Cover Classification, Feature Selection, Sequential Minimal Optimization, Random Forest

Conflicts of Interest

The authors declare no conflicts of interest.

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