SSNET: an improved deep hybrid network for hyperspectral image classification

Classification is one of the most important task in hyperspectral image processing. In the last few decades, several classification techniques have been introduced. However, most of them could not efficiently extract features from hyperspectral images (HSI). A novel deep learning framework is proposed in this paper which efficiently utilises convolutional neural network (CNN) and spatial pyramid pooling (SPP) for extracting both the spectral-spatial features for classification. The proposed hybrid framework uses principal component analysis (PCA), 3D-CNN, 2D-CNN and SPP. The proposed CNN-based model is applied on three benchmark hyperspectral datasets, and subsequently the performance is compared with state-of-the-art methods in the same field. The obtained results reveal the superiority of the proposed model in effectively classifying HSI.

Automated summary

A novel deep learning framework utilizing convolutional neural networks is proposed for feature extraction in hyperspectral image classification. Experimental results demonstrate the superiority of the proposed model in effectively classifying hyperspectral images.