A Joint Network of 3D-2D CNN Feature Hierarchy and Pyramidal Residual Model for Hyperspectral Image Classification

Since convolutional neural networks (CNN) can extract deeper features from hyperspectral images, they show good classification performance in the hyperspectral image (HSI) classification task. However, the performance of many CNN models is constrained by the complexity of hyperspectral pictures. The typical 2D-CNN method is unable to capture the discriminant spectral-spatial features due to the massive dimensionality of HSI data as well as potential noise and redundancy, while the 3D-CNN method significantly increases the computational burden of the model. Moreover, the gradient problem of the deeper CNN architecture impedes the convergence performance of the neural network, leading to poor classification accuracy. To mitigate these problems, this paper proposes a joint network of Hybrid 3D-2D CNN and pyramid residual (J-NHPR) to realize the classification of HSI. The combined deep pyramid residual network can significantly enhance the performance of the suggested network with the HSI data by progressively improving the diversity of high-level spectral-spatial properties across layers. Our tests, which included eight distinct approaches for classification and three well-known HSI data sets, revealed that our recently built model, J-NHPR, can provide comparative advantage (in terms of classification accuracy) over other HSI classification methods. The source code of the J-NHPR model is available at https://github.com/Dreamvai/J-NHPR.