TEXTURE AND SHAPE FEATURES FOR GRASS WEED CLASSIFICATION USING HYPERSPECTRAL REMOTE SENSING IMAGES

Automatic grass weed detection and classification deals with large intraclass challenges since they are similar to grass in shape, sizes and colors. Grass weed detection and mapping is critical for site-specific weed control to reduce the cost of labor and impact of herbicides. In this paper, we investigate different shape and texture based feature extraction methods to discriminate three different grass weed categories using hyperspectral images. Feature extraction methods including Gabor features, Histogram of Oriented Gradients (HoG), and Local Binary Pattern (LBP) are evaluated in this paper. The experimental results indicate that the overall accuracy of the grass weed classification using the propose combination of texture and shape features generated from LBP and HoG is higher as compared to using shape and texture features separately.