# 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. black ice; convolutional neural network; hyperspectral imaging; image classification; principal component analysis +
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Y are spatial dimensions and Z is the spectral dimension [3]. This imaging technique can be defined as the combination of traditional spectroscopy and modern imaging [4; 5]. Due to its shape and numerous bands, the hyperspectral image is considered as a \"data cube\". Since the first work on remote sensing was published, the popularity of the hyperspectral camera has been increasing exponentially [6]. Thus, the areas of application for hyperspectral imaging have also increased, such as analytical techniques for nanoscale materials [7], plant seeds [8], fruit quality and safety [9; 10], biological tissues [11], and wound care [12]. In the past decade, the development of deep learning models has increased rapidly and received widespread attention. Unlike traditional machine learning models, deep learning does not need any patterns because it can learn a pattern from the given data. Thus, it is used in natural language processing, object detection, speech recognition, semantic segmentation, and other branches of computer vision. Many training samples are given to a general deep learning framework to train the model and tune the numerous parameters. Various deep learning models have been used to handle huge datasets of hyperspectral images. In [13], an autoencoder was used for the classification of hyperspectral images. Similarly, the Recurrent Neural Network [14], the Transformer (SpectralFormer) [15], a Deep Belief Network [16], a Deep Recurrent Neural Network [17], and a Convolutional Neural Network [18] have been used for classification of hyperspectral images. In this paper, a 2D-3D Convolutional Neural Network is used for the classification of hyperspectral images of black ice. The next section introduces a short review of related works until now. It presents an approach for black ice classification with hyperspectral imaging data for the first time. Considering the survey result from the Korea Transport Institute (Figure 1), in the initial phase, the main aim of the paper was to collect data during the daytime. We have utilized hyperspectral imaging for capturing a richer set of features compared to RGB or grayscale images. We have introduced a custom deep neural network architecture optimized for hyperspectral image data. Even though there are limited sources of research on black ice detection using computer vision and hyperspectral imaging, in the following section, we discussed the existing related works.