Class-semantic textons with superpixel neighborhoods for natural roadside vegetation classification

Accurate classification of roadside vegetation plays a significant role in many practical applications, such as vegetation growth management and fire hazard identification. However, relatively little attention has been paid to this field in previous studies, particularly for natural data. In this paper, a novel approach is proposed for natural roadside vegetation classification, which generates class-sematic color-texture textons at a pixel level and then makes a collective classification decision in a neighborhood of superpixels. It first learns two individual sets of bag-of-word visual dictionaries (i.e. class-semantic textons) from color and filter-bank texture features respectively for each object. The color and texture features of all pixels in each superpixel in a test image are mapped into one of the learnt textons using the nearest Euclidean distance, which are further aggregated into class probabilities for each superpixel. The class probabilities in each superpixel and its neighboring superpixels are combined using a linear weighting mixing, and the classification of this superpixel is finally achieved by assigning itthe class with the highest class probability. Our approach shows higher accuracy than four benchmarking approaches on both a cropped region and an image datasets collected by the Department of Transport and Main Roads, Queensland, Australia.