Abstract
Roadside vegetation classification plays a significant role in many applications, such as grass fire risk assessment and vegetation growth condition monitoring. Most existing approaches focus on the use of vegetation indices from the invisible spectrum, and only limited attention has been given to using visual features, such as color and texture. This paper presents a new approach for vegetation classification using a fusion of color and texture features. The color intensity features are extracted in the opponent color space, while the texture comprises of three color moments. We demonstrate 79% accuracy of the approach on a dataset created from real world video data collected by the Department of Transport and Main Roads (DTMR), Queensland, Australia, and promising results on a set of natural images. We also highlight some typical challenges for roadside vegetation classification in natural conditions.
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