Texture aware unsupervised segmentation for assessment of flood severity in uav aerial images

The severity of flooding in a given region is essential information required for better planning and managing post-flood relief and rescue efforts. This work proposes an unsupervised segmentation-based approach to estimate the severity of flooding by analyzing images acquired from Unmanned Aerial Vehicles (UAV). In this work, handcrafted texture feature (Local Binary Pattern) is integrated with k-means segmentation algorithm to obtain an accurate segmentation of the flooded region. Subsequently, the image is categorized as severely flooded, moderately flooded, minor flooding, and no flooding based on the percentage of pixels belonging to the flooded region in the image. The proposed approach is evaluated on FloodNet dataset containing the UAV aerial images acquired after hurricane Harvey. The experimental results demonstrate that the severity of flooding was correctly estimated in 84.29% of the images illustrating the robustness of the proposed approach. Moreover, the use of handcrafted features along with unsupervised segmentation eliminates the need of manually annotated images. Besides, the proposed unsupervised segmentation approach performs competitively with the deep learning method (UNet) to identify the flooded regions. Therefore, the proposed method could be preferred for analysing the images on-board UAV for post-flood scene understanding.