Computationally Efficient Hyperspectral Image Classification Using Deep Autoencoder and Multistage 2 Dimensional Convolutional Autoencoder Architectures

Hyper-spectral imaging (HSI) sensor technology enables extensive coverage with spatial, spectral, and temporal flexibility. However, the substantial volume of spectral-spatial information it provides poses significant challenges in data management and extraction. To address these challenges, various advancements have been made in hyperspectral imaging over recent decades. Key issues in hyperspectral image Classification include the large data size, insufficient training samples, particularly the scarcity of labeled samples and computational burden effective feature extraction methods and the appropriate selection of filters for classification are critical for achieving reliable results without data loss. In this paper, two methods named Deep Autoencoder (DAE) and Multistage 2D- Convolutional Autoencoder (2D-CAE) proposed with simplified and robust architecture which helped in reduced space and time complexity and efficient in dimentionality reduction. Analysed comparison for Support Vector Machine (SVM), K- nearest Neighbour (KNN), HybridSN, Wavelet-CNN is done with proposed methods (DAE) and Multistage 2D- Convolutional Autoencoder (2D- CAE), evaluating their accuracy and computational efficiency with respect to Overall Accuracy (OA), Average Accuracy(AA), Kappa(k), FLOPs, Throughput and Test time required for prediction by performing extensive experiments on publically available dataset Indian Pines (IP), Salina (SA) and Pavia University (PU). FLOPs reduction in DAE is 99.97% and 2D-CAE is 99.95%, throughput reduction in DAE is 99.95% and 2D-CAE is 99.98% when compared individually with HybridSN and Wavelet-CNN, showing significant reduction in space complexity.