CIELab Color Moments: Alternative Descriptors for LANDSAT Images Classification System

Abstract

This study compares the image classification system based on normalized difference vegetation index (NDVI) and Latent Dirichlet Allocation (LDA) using CIELab color moments as image descriptors. It was implemented for LANDSAT images classification by evaluating the accuracy values of classification systems. The aim of this study is to evaluate whether the CIELab color moments can be used as an alternatif descriptor replacing NDVI when it is implemented using LDA-based classification model. The result shows that the LDA-based image classification system using CIELab color moments provides better performance accuracy than the NDVI-based image classification system, i.e 87.43% and 86.25% for LDA-based and NDVI-based respectively. Therefore, we conclude that the CIELab color moments which are implemented under the LDA-based image classification system can be assigned as alternative image descriptors for the remote sensing image classification systems with the limited data availability, especially when the data only available in true color composite images.

Identifkasi Kerusakan Lahan Hutan Akibat Aktivitas Pertambangan Emas Desa Bowone Kecamatan Tabukan Selatan Tengah

“…Sedangkan untuk melihat tingkat kerusakan lahan hutan dapat dilihat dari tutupan vegetasi yang di mana merupakan salah satu indikator penting dalam melihat rusak atau tidaknya suatu lahan. Kesehatan dan kerapatan vegetasi dapat dilihat dari nilai NDVI yang makin mendekati +1 makin baik (Kusumaningrum et al, 2015;Mailendra & Buchori, 2019).…”