Aggregating Pixel-level Prediction and Cluster-level Texton Occurrence within Superpixel Voting for Roadside Vegetation Classification

Roadside vegetation classification has recently attracted increasing attention, due to its significance in applications such as vegetation growth management and fire hazard identification. Existing studies primarily focus on learning visible feature based classifiers or invisible feature based thresholds, which often suffer from a generalization problem to new data. This paper proposes an approach that aggregates pixel-level supervised classification and cluster-level texton occurrence within a voting strategy over superpixels for vegetation classification, which takes into account both generic features in the training data and local characteristics in the testing data. Class-specific artificial neural networks are trained to predict class probabilities for all pixels, while a texton based adaptive K-means clustering process is introduced to group pixels into clusters and obtain texton occurrence. The pixel-level class probabilities and cluster-level texton occurrence are further integrated in superpixel-level voting to assign each superpixel to a class category. The proposed approach outperforms previous approaches on a roadside image dataset collected by the Department of Transport and Main Roads, Queensland, Australia, and achieves state-of-the-art performance using low-resolution images from the Croatia roadside grass dataset.