Image Classification Using RapidEye Data: Integration of Spectral and Textual Features in a Random Forest Classifier

Information on crop types derived from remotely sensed images provides valuable input for many applications such as crop growth modeling and yield forecasting. In this paper, a random forest (RF) classifier was used for crop classification using multispectral RapidEye imagery over two study sites, one in north-eastern China and one in eastern Ontario, Canada. Both vegetation indices (VIs) and textural features were derived from the RapidEye imagery and used for classification. A total of 20 VIs, categorized into two groups with and without the red edge (RE) band in an index, were calculated. A total of eight types of textural features were derived using four different window sizes from both the RE and the near-infrared bands. To reduce redundancies among the VIs and textural features, feature selection using the principal component analysis, correlation analysis, and stepwise discriminant analysis was performed. Results showed that the overall classification accuracy was improved by similar to 7% when the RE indices were combined with the five spectral bands in classification, as compared with that using the five bands alone. When textural information was included, the overall classification accuracy increased by similar to 6% compared with that using the band reflectance alone. Furthermore, when all the features (band reflectance, VIs, and texture) were used, the overall classification accuracy increased by similar to 12% compared with that using only the band reflectance. The RF importance measures showed that the RE reflectance was important for classification, as indicated by the high importance for the triangular vegetation index, transformed chlorophyll absorption in reflectance index, and green-rededge normalized difference vegetation index. The gray-level co-occurrence matrix mean is the most useful for classification among the textural features. The study provides a means to feature extraction and selection for crop classification from remote sensing imagery.