TITLE:
A Study on Tropical Land Cover Classification Using ALOS PALSAR 50 m Ortho-Rectified Mosaic Data
AUTHORS:
Lan Mi, Nguyen Thanh Hoan, Ryutaro Tateishi, Kotaro Iizuka, Bayan Alsaaideh, Toshiyuki Kobayashi
KEYWORDS:
Slope Correction, Land Cover Classification, Feature Selection, Sequential Minimal Optimization, Random Forest
JOURNAL NAME:
Advances in Remote Sensing,
Vol.3 No.3,
September
30,
2014
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.