Automatic Sand Dune Detection and Classification Framework Using Remote Sensing Images

Dune field landscape patterns serve as an important signs of aeolian processes, such as wind conditions, sediment supply, and so on. A novel framework was proposed and evaluated for automatic dune detection and classification with remotely sensed images. The framework consists of two main steps: (1) The first step is to detect sand dunes from remote sensing images by SandUnet, which is firstly proposed in this paper. SandUnet, a Convolutional Neural Network (CNN), has a similar network structure with Attention U-net but modifies its attention gate module. In SandUnet, the input signals' information is not compressed as in the Attention U-net, therefore, the nuanced color and texture information of dunes are preserved. This paper demonstrated that SandUnet has better detection accuracy than other popular CNNs such as FCN, U-net, U-net++, and Attention U-net. (2) The second step is to compute the image similarity scores through MobileNet between each dune detection result image and the representative images of 6 different types of dunes. Then, each dune detection result image is classified into a dune type automatically. This paper applied the proposed framework to Taklimkan Desert in China. The average classification accuracy rate is around 80%, which proves the usefulness of this framework in automatic, no-cost, and accurate sand dune classification.