Applying spectral unmixing and support vector machine to airborne hyperspectral imagery for detecting giant reed

Abstract

This study evaluated linear spectral unmixing (LSU), mixture tuned matched filtering (MTMF) and support vector machine (SVM) techniques for detecting and mapping giant reed (Arundo donax L.), an invasive weed that presents a severe threat to agroecosystems and riparian areas throughout the southern United States and northern Mexico. Airborne hyperspectral imagery with 102 usable bands covering a spectral range of 475–845 nm was collected from a giant reed-infested site along the US-Mexican portion of the Rio Grande in 2009 and 2010. The imagery was transformed with minimum noise fraction (MFN) to reduce the spectral dimensionality and noise. The three classification techniques (LSU, MTMF and SVM) were applied to the transformed MNF imagery based 11 endmember spectra extracted from the images for each of the two years. Accuracy assessment and kappa analysis were performed to compare the differences in classification accuracies among the three classification methods. Results showed that SVM and MTMF performed better than LSU, with SVM being the best classifier in both years. The results from this study indicate that hyperspectral imagery in conjunction with image classification techniques is useful for distinguishing giant reed from associated plant species and for monitoring the progression of this invasive weed.