Spectral-Spatial and Cascaded Multilayer Random Forests for Tree Species Classification in Airborne Hyperspectral Images

The rapid development of remote sensing sensors has made it possible to collect airborne hyperspectral data with high spectral and spatial resolution. Such data can provide valuable information to identify tree species in the forest. However, it is a challenge to efficiently utilize the abundant spectral information and complex spatial information within the data. In this article, a spectral-spatial and cascaded multilayer random forests (SSCMRF) method is proposed to classify tree species in the high-spatial-resolution hyperspectral image. The SSCMRF adopts two classification stages to fully exploit the spatial information within shape-adaptive superpixels and shape-fixed patches. Two different kinds of spatial information are integrated by concatenating the output of the superpixel-based classification and the spectral features as the input of the patch-based classification. To demonstrate the superiority of the proposed SSCMRF, experiments are conducted with an airborne hyperspectral dataset of a forest area with a spatial resolution of 1 m. Training with 2.5% randomly selected ground-truth samples, the proposed SSCMRF achieves a classification accuracy of 97.50% within 6 min. In addition, the experiment results demonstrate that the proposed SSCMRF outperforms some state-of-the-art spectral-spatial classification models in terms of quantitative metrics and visual quality on the classification map.