Deep neural networks face many problems in the field of hyperspectral image classification, lack of effective utilization of spatial spectral information, gradient disappearance and overfitting as the model depth increases. In order to accelerate the deployment of the model on edge devices with strict latency requirements and limited computing power, we introduce a lightweight model based on the improved 3D-Densenet model and designs DGCNet. It improves the disadvantage of group convolution. Referring to the idea of dynamic network, dynamic group convolution(DGC) is designed on 3d convolution kernel. DGC introduces small feature selectors for each grouping to dynamically decide which part of the input channel to connect based on the activations of all input channels. Multiple groups can capture different and complementary visual and semantic features of input images, allowing convolution neural network(CNN) to learn rich features. 3D convolution extracts high-dimensional and redundant hyperspectral data, and there is also a lot of redundant information between convolution kernels. DGC module allows 3D-Densenet to select channel information with richer semantic features and discard inactive regions. The 3D-CNN passing through the DGC module can be regarded as a pruned network. DGC not only allows 3D-CNN to complete sufficient feature extraction, but also takes into account the requirements of speed and calculation amount. The inference speed and accuracy have been improved, with outstanding performance on the IN, Pavia and KSC datasets, ahead of the mainstream hyperspectral image classification methods.

翻译：深度神经网络在高光谱图像分类领域面临诸多问题，包括空间光谱信息利用不足、随模型深度增加出现的梯度消失与过拟合现象。为加速模型在具有严格延迟要求和有限计算能力的边缘设备上部署，我们基于改进的3D-DenseNet模型引入轻量化方案，设计了DGCNet。该方法改进了组卷积的不足，借鉴动态网络思想，在三维卷积核上设计了动态组卷积（DGC）。DGC为每个分组引入小型特征选择器，根据所有输入通道的激活值动态决定连接输入通道的哪一部分。多个分组可捕获输入图像不同且互补的视觉与语义特征，使卷积神经网络（CNN）学习到丰富的特征。三维卷积提取高维度、高冗余的高光谱数据，卷积核之间同样存在大量冗余信息。DGC模块使3D-DenseNet能够选择语义特征更丰富的通道信息，并丢弃非活跃区域。经过DGC模块的3D-CNN可视为剪枝后的网络。DGC不仅使3D-CNN完成充分特征提取，同时兼顾了速度与计算量的需求。推理速度与精度均得到提升，在IN、Pavia和KSC数据集上表现突出，领先于主流高光谱图像分类方法。