Image classification in natural scenes: Are a few selective spectral channels sufficient?

A tenet of object classification is that accuracy improves with an increasing number (and variety) of spectral channels available to the classifier. Hyperspectral images provide hundreds of narrowband measurements over a wide spectral range, and offer superior classification performance over color images. However, hyperspectral data is highly redundant. In this paper we suggest that only 6 measurements are needed to obtain classification results comparable to those realized using hyperspectral data. We present classification results for a natural scene using three imaging modalities: 1) using three broadband color filters (RGB) and three narrowband samples, 2) using six narrowband samples, and 3) using six commonly available optical filters. If these results hold for larger datasets of natural images, recently proposed multispectral image sensors [1, 2] can be used to offer material classification results equal to that of hyperspectral data.