# Hyperspectral Image Classification Using 3D Capsule-Net Based Architecture
## 3 Proposed Methodology
### Capsule-Net  
Modern day technologies such as artificial intelligence (AI) and computer vision (CV) are paving the way for multiple applications that provide autonomous and intelligent systems [36]. Whether it is in the field of robotics, health, education, or transport industry, both AI and CV are playing their role in the development of top-notch intelligent systems [37]. Computer vision encompasses a variety of applications that further perform autonomous and intelligent processes, including object detection, image classification, image recognition, and natural language processing [38]. To meet these requirements of CV applications, multiple approaches are listed in the literature. The choice of researchers in such cases was using deep learning models, primarily convolutional neural networks (CNN) [39]. But as research progressed, options to complete computer vision tasks kept on changing, and a novel method was proposed by [PERSON] et al. (2017). They introduced the idea of capsule networks, which would ultimately be more effective than CNNs in many aspects. In CNNs, a layered type of structure is formed, and the model is trained using a large quantity of images from a dataset. The trained model efficiency is determined by testing the model on multiple test subjects. The information that is gathered by the CNN model is utilized in performing computer vision tasks [40]. However, a drawback that occurs in CNN models led to the designing of a new approach that can perform effectively in CV and AI applications. The inefficiency of the pooling function of CNN and the drawback that CNN require a larger quantity of data in the training process means labeling and acquiring such vast amounts of data is an arduous task [38]. Another inefficiency of the CNN model arises because there exists no spatial relationship between the features when they are extracted from an image using CNNs [41]. Capsule networks fill in the loopholes that are present in convolutional neural networks. First, CNNs have difficulty obtaining the spatial information due to ineffective pooling functions, but capsule networks can effectively obtain this information [41]. Furthermore, the dynamic routing approach is utilized in capsule nets. In a capsule network,  
Figure 2: Visual salience based proposed framework for no-reference image quality assessment. capsules are used to take in input values, computation is performed on the given input values, and output is encapsulated as well. The inputs and outputs in a capsule network are in vector form, which is a major distinction from CNN, where both of them are in scalar form [42]. Information related to the disorientation and deformation that can exist in an image can be extracted using capsule nets, and this is another edge for which capsule nets excel over CNNs [38]. The utility of capsule networks arise as they are used as a subdivision in the neural network design. The input to a capsule network is the output from a CNN model. This input is given to a capsule in a network that produces an output vector form that comprises two values: vector values and probability [41]. A two-part structure is formed in a capsule network that consists of an encoder and a decoder. The encoder part in the structure further comprises three layers: convolution, digitcaps, and a primary layer. The decoder part in the structure has the ability to take input from the digitcaps layer of the encoder block and reconstruct an image by decoding the information obtained from the digitcaps layer [38, 41]. In the first step, low-level features (LLFs) are obtained using CNN; these low-level features are used in the next stage where these LLF are provided as an input to the capsule network. By performing computations on these low-level features, the output of the capsule network comes out to be high-level features (HLFs). The obtained features in a vector form from a capsule network are provided to a denser layer [43].