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With the development of deep learning, the performance of hyperspectral image(HSI) classification has been greatly improved in recent years. The shortage oftraining samples has become a bottleneck for further improvement ofperformance. In this paper, we propose a novel convolutional neural networkframework for the characteristics of hyperspectral image data, called HSI-CNN.Firstly, the spectral-spatial feature is extracted from a target pixel and itsneighbors. Then, a number of one-dimensional feature maps, obtained byconvolution operation on spectral-spatial features, are stacked into atwo-dimensional matrix. Finally, the two-dimensional matrix considered as animage is fed into standard CNN. This is why we call it HSI-CNN. In addition, wealso implements two depth network classification models, called HSI-CNN+XGBoostand HSI-CapsNet, in order to compare the performance of our framework.Experiments show that the performance of hyperspectral image classification isimproved efficiently with HSI-CNN framework. We evaluate the model'sperformance using four popular HSI datasets, which are the Kennedy Space Center(KSC), Indian Pines (IP), Pavia University scene (PU) and Salinas scene (SA).As far as we concerned, HSI-CNN has got the state-of-art accuracy among allmethods we have known on these datasets of 99.28%, 99.09%, 99.42%, 98.95%separately.

HSI-CNN: A Novel Convolution Neural Network for Hyperspectral Image