  Recently, convolutional neural network (CNN) techniques have gained
popularity as a tool for hyperspectral image classification (HSIC). To improve
the feature extraction efficiency of HSIC under the condition of limited
samples, the current methods generally use deep models with plenty of layers.
However, deep network models are prone to overfitting and gradient vanishing
problems when samples are limited. In addition, the spatial resolution
decreases severely with deeper depth, which is very detrimental to spatial edge
feature extraction. Therefore, this letter proposes a shallow model for HSIC,
which is called depthwise over-parameterized convolutional neural network
(DOCNN). To ensure the effective extraction of the shallow model, the depthwise
over-parameterized convolution (DO-Conv) kernel is introduced to extract the
discriminative features. The depthwise over-parameterized Convolution kernel is
composed of a standard convolution kernel and a depthwise convolution kernel,
which can extract the spatial feature of the different channels individually
and fuse the spatial features of the whole channels simultaneously. Moreover,
to further reduce the loss of spatial edge features due to the convolution
operation, a dense residual connection (DRC) structure is proposed to apply to
the feature extraction part of the whole network. Experimental results obtained
from three benchmark data sets show that the proposed method outperforms other
state-of-the-art methods in terms of classification accuracy and computational
efficiency.
