Multi-Spectral Material Classification in Landscape Scenes Using Commodity Hardware

 

We investigate the advantages of a stereo, multi-spectral acquisition system for material classi cation in ground-level landscape images.

August 23, 2013
International Conference on Computer Analysis of Images and Patterns (CAIP) 2013

 

Authors

Gwyneth Bradbury (University College London)

Kenny Mitchell (Disney Research)

Tim Weyrich (University College London)

Multi-Spectral Material Classification in Landscape Scenes Using Commodity Hardware

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Abstract

Our novel system allows us to acquire high-resolution, multi- spectral stereo pairs using commodity photographic equipment. Given additional spectral information we obtain better classi cation of vegetation classes than the standard RGB case. We test the system in two modes: splitting the visible spectrum into six bands and extending the recorded spectrum to near infra-red. Our six-band design is more practical than standard multi-spectral techniques and foliage classi cation using acquired images compares favourably to using a standard camera.

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