|DU, QIAN - Mississippi State University|
|JAMES, EVERITT - Retired ARS Employee|
|YOUNAN, NICOLAS - Mississippi State University|
Submitted to: Institute of Electrical and Electronics Engineers
Publication Type: Proceedings
Publication Acceptance Date: 6/15/2010
Publication Date: 8/20/2010
Citation: Yang, C., Du, Q., James, E.H., Goolsby, J., Younan, N.H. 2010. Spectral Unmixing of airborne hyperspectral imagery for mapping giant reed infestations. Institute of Electrical and Electronics Engineers. CDROM.
Interpretive Summary: Giant reed is an invasive weed throughout the southern half of the United States and northern Mexico with the densest stands growing along the Rio Grande in Texas and the coastal rivers of southern California. Accurate information on the spatial distribution and infested areas of giant reed is essential for effective management of this invasive weed. This study evaluated four image classification techniques to identify giant reed from airborne hyperspectral imagery. Accuracy assessment showed that these techniques applied to airborne hyperspectral imagery can be used to effectively distinguish giant reed from associated plant species.
Technical Abstract: Spectral unmixing techniques applied to hyperspectral imagery were examined for 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 two giant reed-infested sites along the US-Mexican portion of the Rio Grande. The imagery was transformed with minimum noise fraction (MFN) to reduce the spectral dimensionality and noise. Linear spectral unmixing (LSU) and mixture tuned matched filtering (MTMF) were applied to the transformed MNF imagery based on endmember spectra extracted from the imagery. The abundance images were then converted into classification maps. For comparison, spectral angle mapper (SAM) and support vector machine (SVM) were used to classify the imagery. Accuracy assessment showed that MTMF was slightly better than or similar to LSU and that SVM performed better than the other three methods. The results from this study will be useful for distinguishing giant reed from associated plant species.