# Hyperspectral Image Classification Using 3D Capsule-Net Based Architecture
###### Abstract  
Convolution neural networks have received much interest recently in the categorization of hyperspectral images (HSI). Deep learning requires a large number of labeled samples in order to optimize numerous parameters due to the expansion of architecture depth and feature aggregation. Unfortunately, only few examples with labels are accessible, and the majority of spectral images are not labeled. For HSI categorization, the difficulty is how to acquire richer features with constrained training data. In order to properly utilize HSI features at various scales, a 3D Capsule-Net based supervised architecture is presented in this paper for HSI classification. First, the input data undergo incremental principal component analysis (IPCA) for dimensionality reduction. The reduced data are then divided into windows and given to a 3D convolution layer to get the shallow features. These shallow features are then used by 3D Capsule-Net to compute high-level features for classification of HSI. Experimental investigation on three common datasets demonstrates that the categorization performance by Capsule-Net based architecture exceeds a number of other state-of-the-art approaches. hyperspectral imagery; Capsule-Net; classification; incremental principal component analysis; high-level features +
Footnote †: journal: Applied  
because of its physical connection and weight-sharing features, which greatly minimize the number of learning parameters. Additionally, CNN is capable of directly recording both spatial and spectral data by separating patches [29]. In the area of hyperspectral image classification, CNN-based techniques are becoming more and more significant. The spectral-spatial deep learning approach, which is more accurate than conventional machine-learning techniques used for the research problem related to HSI classification, has been suggested in [28; 30]. Real 3D blocks can be used as data input for the end-to-end spectral-spatial-residual-network (SSRN) [30], eliminating the need for complicated feature extraction on HSI. The hybrid spectral CNN (HybridSN) [31], which combines a spatial 2D-CNN with a spectral-spatial 3D-CNN to acquire a more abstracted kind of spatial context, has been proposed. Additionally, deep learning techniques such as residual-networks and densely linked CNN are increasingly being used for HSI classification [28; 29; 30]. [PERSON] et al. [32] suggested a spatial-spectral residual architecture to identify HSI in light of the popularity of ResNet in the domain of image categorization. This approach considerably increased the feature usage rate, making the details about the front layer's characteristics as an addition to the traits of the rear layer. A pyramidal ResNet framework created to categorize HSI is found in [33]. This approach successfully extracted the features information disclosed by the convolution layer by enlarging the feature space dimension and organizing the feature vector into a pyramidal ResNet block. In addition, [PERSON] et al. [34] enhanced the topology of the densely connected CNN model and suggested a brand-new cubic-CNN architecture for extracting features. The input data to the network for this technique were the recovered original picture chunks, the attributes after feature reduction, and the outputs after 1D convolutional operation. The redundant features were efficiently removed by this technique. In order to properly utilize characteristics in various scales, a newly supervised multiresolution alternatively maintained clique architecture was developed for HSI classification in [35]. Using convolution kernels of varying sizes, a multiscale alternatively revised clique block was created in this technique to dynamically use multiscale data. The majority of the strategies noted above are dependent on CNN and its variations.