Land Cover Classification Using ALOS Imagery For Penang, Malaysia

8th International Symposium of the Digital Earth

地球科学;计算机科学

Sim, C.K.^1 ; Abdullah, K.^1 ; Matjafri, M.Z.^1 ; Lim, H.S.^1

School of Physics, Universiti Sains Malaysia, Malaysia^1

关键词: Classification accuracy;    Feature combination;    Land cover classification;    Land cover informations;    Maximum likelihood classifiers;    Radar remote sensing;    Spectral signature;    Statistical decision;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/18/1/012025/pdf
DOI  :  10.1088/1755-1315/18/1/012025

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

This paper presents the potential of integrating optical and radar remote sensing data to improve automatic land cover mapping. The analysis involved standard image processing, and consists of spectral signature extraction and application of a statistical decision rule to identify land cover categories. A maximum likelihood classifier is utilized to determine different land cover categories. Ground reference data from sites throughout the study area are collected for training and validation. The land cover information was extracted from the digital data using PCI Geomatica 10.3.2 software package. The variations in classification accuracy due to a number of radar imaging processing techniques are studied. The relationship between the processing window and the land classification is also investigated. The classification accuracies from the optical and radar feature combinations are studied. Our research finds that fusion of radar and optical significantly improved classification accuracies. This study indicates that the land cover/use can be mapped accurately by using this approach.