Discrimination of hoary cress and determination of its detection limits via hyperspectral image processing and accuracy assessment techniques

Abstract

This study documents successful discrimination of hoary cress ( Cardaria draba) in southwestern Idaho using hyperspectral imagery to a maximum producer's accuracy of 82% for infestations with greater than 30% cover. Different hyperspectral processing parameters were evaluated and compared, including data transformations, endmember selection, classification algorithms, and post-classification accuracy assessment methods. In this study, the Spectral Angle Mapper (SAM) and Mixture Tuned Matched Filtering (MTMF) classification algorithms performed equally. Minimum Noise Fraction (MNF) data transformation generated producer's accuracies 23% higher than did similar classifications using Principal Components Analysis (PCA) transformed data. Two hoary cress endmembers derived from different vegetative regimes were necessary for successful classification. Finally, this study documents a methodology comparing incremental map accuracies to optimize classifier performance and determine the detectable limits of hoary cress. Detection limits using hyperspectral imagery were as low as 10% cover over a 3 m × 3 m pixel using a mesic vegetative regime endmember. However, for management level use of the imagery, both a mesic and a xeric endmember were necessary for the 30% cover threshold.