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Convolutional neural networks have demonstrated remarkable performance in capturing discriminative features in the hyperspectral domain. Deep learning and CNN have carved out a distinct niche in the remote-sensing community having the potential to classify high-dimensional images. However, the challenge of limited labelled samples and high dimensional hyperspectral data aggravates overfitting in deep networks. Many existing works produce high accuracy but are computationally slower due to the massive computations involved. The Inception model, on the other hand, won the ImageNet Large Scale Visual Recognition Competition (ILSVRC) 2014 challenge and has been validated as a top-tier model by maintaining a trade-off between classification accuracy and computing time. In this paper, we propose a novel lightweight network, Xcep-Dense, a hybrid classification model that combines the core benefits of the extreme version of inception and dense networks. The Xception network employs depth-wise separable convolutions, and the 3D slicing phenomenon, which requires fewer parameters, is computationally efficient and provides excellent classification accuracy. The proposed network is configured with dense network and optimization parameters to alleviate overfitting and gradient vanishing. Xcep-Dense’s performance is validated using two benchmark hyperspectral datasets, Indian Pines and Salinas.

Xcep-Dense: a novel lightweight extreme inception model for hyperspectral image classification