Over the past two decades, hyperspectral (HS) imaging has provided remarkable performance in ground objects classification and disease identification, due to its high spectral resolution. In this paper, a novel method named ‘extended spectral angle mapping (ESAM)’ is proposed to detect citrus greening disease (Huanglongbing or HLB), which is a destructive disease of citrus. Firstly, Savitzky-Golay smoothing filter was applied to the raw image to remove spectral noise within the data, yet keep the shape, reflectance and absorption features of the spectrum. Then support vector machine (SVM) was used to build a mask to segment tree canopy from the other background. Vertex component analysis (VCA) was chosen to extract the pure endmembers of the masked dataset, due to its better performance compared to other spectral linear unmixing methods. Spectral angle mapping (SAM) was applied to classify healthy and citrus greening disease infected areas in the image using the pure endmembers as an input. Finally, red edge position (REP) was used to filter out most of false positive detections. The experiment was carried out with the image acquired by an airborne hyperspectral imaging system from the Citrus Research and Education Center (CREC) in Florida, USA. Ground truth including ground reflectance measurement and diseased tree confirmation was conducted. The experimental results were compared with another supervised method, Mahalanobis distance, and an unsupervised method, K-means. The ESAM performed better than those two methods.