Random Forest with Attribute Profile for Remote Sensing Image Classification

Although hyperspectral images contain rich spectral information due to high number of spectral bands acquired in a wide and continous range of wavelengths, there are also worthful spatial features in adjacent regions, i.e., neighboring pixels. Three spectral-spatial fusion frameworks are introduced in this work. The extended multi-attribute profile (EMAP) are used for spatial feature extraction. The performance of EMAP is assessed when it fed to the random forest classifier. The use of EMAP alone as well as fusion of EMAP with spectral features in both cases of full bands and reduced dimensionality are investigated. The advanced binary ant colony optimization is used for implementation of feature reduction. Three fusion frameworks are introduced for integration of EMAP and the spectral bands; and the classification results are discussed compared to the use of EMAP alone. The experimental results on three popular hyperspectral images show the superior performance of EMAP features fed to the random forest classifier.