## 1 Introduction
Recently, deep-learning-based approaches have attracted enormous attention and have been applied into the field of passive HSI classification, since they can automatically learn the representative spectral and spatial features in a hierarchical manner from data and can yield better performance compared to the traditional, shallower classifiers. For example, [PERSON] et al. proposed a three dimensional convolutional neural network (3D CNN) framework for passive HSI classification [17], which used 3D convolutional kernels to extract spectral and spatial joint features. Besides, [PERSON] et al. proposed a spectral-spatial residual network (SSRN) [18]. By learning spectral and spatial features in sequence using a hybrid multi-dimensional residual structure including 3D and two dimensional (2D) operations, SSRN can extract more discriminative features and achieve satisfactory classification results for passive HSI classification. At present, along with the development of the active HSI hardware, the progress on successful applications of deep-learning-based classification model on passive HSI has led many to ask whether a similar success is achievable in classifying objects under the illumination of SC laser rather than ambient light, whose intensity and spectra distribution are much more uniform than SC lasers across entire images [19]. Moreover, there is no publicly available active HSI datasets of SC laser, which limits the development of active HSI classification methods. Without accurate objects classification, practical applications of active HSI may be greatly hindered. At present, there are many products and studies of active HSI using halogen lamps to illuminate objects, working in the laboratories or underwater with operating distance less than 100 m. For our work, as a \"proof of concept\", the active HSI system illuminated by a SC laser is developed as a possible alternative to the halogen lamps for readers who will find that SC laser could do the similar things. Besides, this work is also an exploration of the practical applications of SC lasers. The objective of this paper is not only to explore the suitability of visible/near-infrared active HSI as a tool for object detection in the dark but also to create a dataset and spectral library of active HSI with SC laser illumination, and propose a feasible deep-learning-based classification method to achieve high accuracy for active HSI which, is a hybrid DenseNet model containing one dimensional (1D) and 2D feature extracting branches parallelly. The 1D branch is used to learn spectral feature representations from a stack of spectral bands. 2D branch facilitates the spatial feature representations from the data. Then, the extracted spectral features and spatial features are concatenated to make classification via the softmax classifier. To our best knowledge, it is the first time such an attempt is made in the field of active HIS classification, and we propose that this active HSI classification method will be a valuable method for detection and investigation in various applications where there is no enough ambient light. To be summarized, our main contributions can be listed as follows: (1) We establish an active HSI system and create a relevant spectral library and dataset from a selection of materials likely present in many realistic scenarios under the illumination of SC laser, which can be used for future development of active HSI classification technology. (2) We proposed a hybrid DenseNet classification model for the active HSI classification. This classification model can extract spectral and spatial features in parallel with the 1D and 2D branches, which can reduce the interference between the two types of features. The extracted features are then concatenated for classification. Compared with other methods, the proposed network achieves the better classification accuracy for the active HSI dataset. The rest of this paper is organized as follows: In Section 2, we describe the experimental setup and present the selection of materials for the measurement.