1Automated Labeling of Materials in Hyperspectral Imagery

Abstract—We present a technique for automatically labeling segmented hyperspectral imagery with semantically meaningful material labels. The technique compares the mean signatures of each image segment to a spectral library of known materials, and material labels are assigned to image segments according to the most similar library entry. Similarity between spectral signatures is evaluated using the CICRd similarity measure proposed in [1], designed specifically for hyperspectral imagery. This measure considers both the continuum-intact reflectance spectrum and its continuum-removed representation. We provide a thorough assessment of this measure by comparison to several commonly used similarity measures on a well-studied Low Altitude AVIRIS image of an urban area. We evaluate our results using both information-theoretic techniques and visual validation of the resulting spectral matches. Index Terms—automatic labeling, material labeling, spectral matching, AVIRIS, hyperspectral imagery, urban I