Extended Attribute Profiles for Precise Crop Classification in UAV-Borne Hyperspectral Imagery

Unmanned aerial vehicle (UAV)-borne hyperspectral imagery has been applied in precision agriculture, owing to its high spatial and spectral resolution. Specifically, the high spatial resolution is conducive to revealing the textural characteristics of crops, while the high spectral resolution can depict detailed spectral differences among crops. In this study, we explored the potential of extended attribute profiles (EAP) in modeling the spectral-spatial characteristics of UAV-borne hyperspectral imagery for the precise crop classification. Specifically, two dimensionality reduction approaches, namely, principle component analysis (PCA) and independent component analysis (ICA), were performed on the hyperspectral image to extract components, based on which a series of EAP that measure different image characteristics are generated. To exploit the complementary information of different attributes, the extracted EAP were fused for the classification of crops using feature stacking (FS) and decision fusion (DF) strategies. Meanwhile, random forest (RF), support vector machine (SVM), and deep neural networks (DNN) were used as classifier for the precise classification of crops. Experiments conducted on the WHU-Hi dataset demonstrated that EAP exploited the spectral-spatial information of UAV-borne hyperspectral imagery and obtained satisfactory crop classification performance.