3D-D2D: An Efficient Hybrid Model For Hyperspectral Image Classification

Abstract

Hyperspectral Image (HSI) classification started with the machine learning classifiers like SVM (Support Vector Machine). It transitioned towards deep neural networks, which perform better than their classifier counterparts.CNN (Convolution Neural Networks) approaches are widely found in the current literature, including architectures like 2D CNN,3D CNN, and various other models using a combination of multiple CNN architectures. The correctness of HSI classification depends on both spectral and spatial features. 2D CNN cannot capture spatial features six, resulting in poor performance. On the other hand, 3D CNNs are computationally heavy and are not widely deployed. To overcome these bottlenecks, hybrid models were proposed where both 3D and 2D CNN are used together to exploit both spatial and spectral features. These models perform considerably well and are more computationally efficient when compared to 3D CNN. This paper proposes a custom hybrid model composed of 3D CNN with Max-Pooling coupled with depth-wise 2D CNN for reduced computational cost. It has considerably fewer learning parameters compared to other proposed CNN models. The performance of the proposed model was evaluated on Indian Pines, University of Pavia, and Salinas Scenes remote sensing datasets and results of the same were compared with state of the art and various other CNN models to validate the performance of the proposed model.