## 1 Introduction
In addition to the subsequent fusion of spatial information, networks that simultaneously feature spatial information and spectral information have become popular. These deep networks can be divided into three categories--feature fusion by shallowly extracting two features [23; 24], feature extraction at once using 3D convolution [15; 16; 17; 25; 26; 27] and deep feature extraction and the fusion of two pieces of information [28]. The method that subsequently fuses spatial information and the spatial-spectral information feature extraction method have a significantly better performance than the spectral information feature extraction method [9; 15; 29]. This is because both methods fuse the extraction of other information features in addition to the spectral information. However, in the hyperspectral data, fusion of other information and the spectral information to classify the hyperspectral image greatly deteriorates the significance of the hyperspectral data.This is a big drawback in some fields. For example, in the military field, which is one of the hyperspectral fields, the detection and classification performance of objects disguised in remote detection is inevitably poor. This is because the disguised object is not given much spatial information. In contrast, the pure spectral information feature extraction is very robust against such problems because it does not classify as spatial information. There is a risk of overfitting due to the limited public dataset. This is verified through hyperspectral images, to which random spatial information is applied. In addition, 3D-CNN, which also uses spatial information, is diluted with feature extraction for spectral information. We prove the problem of 3D-CNN, a network using the spatial-spectral information feature extraction mentioned above, through an experiment in which spatial information is randomly made. Therefore, a deep learning network using only spectral information is the most useful method for HSI information. Among them, 1D-CNN is one of the most actively studied fields in spectral information feature extraction using deep learning. This network excels at spectral information features extraction using 1D-Convolution and it does not undermine the original purpose of the HSI data mentioned above. The 1D-Convolution extracts features of adjacent spectral information according to kernel size. This method produced significant results [14; 15; 16; 17]. This means that 1D-Convolution is a good spectral information feature extractor. However, 1D-Convolution has a limitation because it extracts features only from adjacent spectral information. The spectral information should be able to see the characteristics of the bands away. In addition, by parallelizing feature extraction according to the spectral distance, a band-selective factor was also added to deep learning. In this paper, we propose networks capable of feature extraction even for spectral information that are separated from each other. Atrous Convolution is also called Dilated convolution. The concept was first introduced in [30], and it can be seen that the performance of deep learning is greatly improved through [31]. Looking at Figure 1, unlike the conventional convolution, the Atrous Convolution can check spectral information separated from each other. This is a very positive factor for deep learning networks that learn spectral information. It is meaningful to classify the spectral information of a substance only with adjacent bands, but it is more meaningful from the viewpoint of HSI data because feature extraction is performed by looking at the association between not only adjacent bands but also distant bands. This can also be seen as an element of band selection. In general 1D-Convolution, the spectral resolution is impaired in the process of extracting features, and this damages spectral information a lot. Based on the above, Atrous Convolution is very suitable for spectral information feature extraction networks. The three contributions in this paper are summarized as follows:  
1.