Effectiveness of the red-edge band of RapidEye in land cover classification

Abstract

ABSTRACT This study examined the effectiveness of the red-edge band using RapidEye satellite images for land cover classification. The analysis comprises three schemes for evaluating the effectiveness of the red-edge band: principal component analysis (PCA), vegetation index, and supervised image classification. The factor loadings computed by means of PCA were applied to analyze the importance of each band in the training samples. The analysis results of the factor loadings indicated that the red-edge band performed better than the visible band in the vegetation region. When rice paddy and peanuts were classified using the NDVI_RE, the improvement in accuracy was approximately 7%. Further, the accuracy of rice paddy classification using CMFI_RE was improved by approximately 6%. It can thus be inferred that the red-edge band made a certain contribution to vegetation classification. In land cover classification using reflectance, the accuracy of the support vector machine (SVM) was higher than that of the maximum likelihood classifier (MLC), the iterative self-organizing data analysis technique, and the K-means algorithm. When the red-edge band was included, the overall accuracy improved from 1% to 3%. The results of our experiments indicated that the red-edge band contributed marginally to land cover classification.