Hybrid Texture Feature Based Roadside Vegetation Classification

In the proposed technique a novel texture feature based multiple classifier technique is applied to roadside vegetation classification. It is well-known that automation of roadside vegetation classification is one of the important issues emerging strongly in improving the fire risk and road safety. The method proposes a novel texture feature based expert system for vegetation identification as dense and sparse grasses. It consist of five steps, namely image pre-processing, feature extraction, training with multiple classifiers, classification and statistical analysis. Initially, to enhance the input image, histogram equalization is applied. Then, Gray level Run length Matrix (GLRLM)and Compound Local Binary Pattern (CLBP) technique is applied in-order to obtain the texture feature relevant to vegetation in the roadside images. In the training and classification stages, three classifiers have been fused to combine the multiple decisions. The first classifier is support vector machine, the second classifier is artificial neural network and the third classifier is k-Nearest Neighbour (k-NN). The combination of multiple classifiers and fusion of classifiers have received much more attention. The strength of the proposed method is based on new descriptor and the incorporation of the multiple classifiers with majority voting. This method is more efficient and achieves high performance.