## 1 Introduction  
The hyperspectral image (HSI) is a technique of adding spectral information to spatial information and deriving the state, composition, characteristics, and variation of an object by composing two-dimensional image information according to the spectral band of an electromagnetic wave in the form of a hyperspectral cube [1]. The hyperspectral cube has more than 100 spectral resolution functions, and is classified into multispectral, hyperspectral, and ultra spectral according to the number of spectral bands. A human eye or color camera can also be referred to as spectral imaging because it recognizes the color or state of an object by acquiring spectral information of red, green, and blue. However, usually a spectral image means that it has a larger number of spectral bands. Hyperspectral images do not measure distant spectral bands, but rather contiguous spectral bands [2]. The higher the number of spectral bands, the higher the resolution. The ultra spectral images classify objects according to the chemical composition ratio of solid or liquid, and hyperspectral images can even analyze the chemical composition ratio of gases. These characteristics are used in various fields such as defense, geology, environment, and medical care [3, 4, 5]. There are two main methods for obtaining hyperspectral images. There are reflection spectroscopy using light reflected from an object and radiation spectroscopy using radiantheat information. Depending on the band of the spectrum to be detected, an appropriate spectroscopy method should be used. In general, reflection spectroscopy detects a spectrum in the VNIR\(\sim\)SWIR (0.4-2.5 \(\upmu\)m) band, and radiation spectroscopy detects MWIR (3-5 \(\upmu\)m) and LWIR (8-14 \(\upmu\)m). Since the reflected spectral area measures light reflected from an object, it is greatly affected by the surrounding environment, such as the reflected angle and the intensity of light. In contrast, radiative spectroscopy is less susceptible to atmospheric moisture. Reflected spectroscopy is relatively cheaper to acquire than radiated spectroscopy. Recently, deep learning has been one of the most successful techniques and has been very spectacular in the field of computer vision [6]. Motivated by this successful technique development, deep learning was used to classify HSI in the field of remote sensing [7; 8; 9]. Compared with the existing manual classification process, HSI data composed of complex spectral bands can be automatically classified effectively through learning with a high level of function. This can effectively respond with the problem of large variability in the signature spectrum. However, the types of features extracted from the deep network may be different; for example, there is spectral information feature extraction, spatial information feature extraction, and spectral-spatial information feature extraction. Spectral information is the most important feature of HSI and is an important factor in classification. Traditional spectral feature extraction (e.g., PCA [10; 11] ICA [12] and LDA [13]) is still good, but these linear models find it difficult to process the complex spectral information present in HSI. The 1D-CNN [14; 15; 16; 17] is a representative deep learning network using spectral information feature extraction for HSI classification. In previous studies, it was proved that the performance can be further improved by adding spatial information features to the classifier in HSI classification [18; 19]. Adding spatial information here is a subsequent fusion to other feature extraction. In [15; 16; 20; 21; 22], PCA is first applied to the entire HSI to reduce the dimensions of the original space, and spatial information of the peripheral pixels of the spectral information being entered is utilized for 2D-CNN. The above methods successfully combined CNN and PCA, combined spatial feature extraction, and reduced computational cost.