Black Ice Classification with Hyperspectral Imaging and Deep Learning

Abstract

With the development of new technologies inside car mechanisms with various sensors connected to the IoT, a new generation of automation is attracting attention. However, there are still some factors that are difficult to detect. Among them, one of the highest risk factors is black ice. A road covered with black ice, which is hard to see from a distance, is not only the cause of damage to vehicles passing over the spot, but it also puts lives at risk. Hence, the detection of black ice is essential. A lot of research has been done on this topic with various sensors and methods. However, hyperspectral imaging has not been used for this particular purpose. Therefore, in this paper, black ice classification has been performed with the help of hyperspectral imaging in collaboration with a deep learning model for the first time. With abundant spectral and spatial information, hyperspectral imaging is a good way to analyze any material. In this paper, a 2D–3D Convolutional Neural Network (CNN) has been used to classify hyperspectral images of black ice. The spectral data were preprocessed, and the dimension of the image cube was reduced with the help of Principal Component Analysis (PCA). The proposed method was then compared with the existing method for better evaluation.

Keywords

• black ice
• convolutional neural network
• hyperspectral imaging
• image classification
• principal component analysis