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Deep neural networks face many problems in the field of hyperspectral imageclassification, lack of effective utilization of spatial spectral information,gradient disappearance and overfitting as the model depth increases. In orderto accelerate the deployment of the model on edge devices with strict latencyrequirements and limited computing power, we introduce a lightweight modelbased on the improved 3D-Densenet model and designs DGCNet. It improves thedisadvantage of group convolution. Referring to the idea of dynamic network,dynamic group convolution(DGC) is designed on 3d convolution kernel. DGCintroduces small feature selectors for each grouping to dynamically decidewhich part of the input channel to connect based on the activations of allinput channels. Multiple groups can capture different and complementary visualand semantic features of input images, allowing convolution neural network(CNN)to learn rich features. 3D convolution extracts high-dimensional and redundanthyperspectral data, and there is also a lot of redundant information betweenconvolution kernels. DGC module allows 3D-Densenet to select channelinformation with richer semantic features and discard inactive regions. The3D-CNN passing through the DGC module can be regarded as a pruned network. DGCnot only allows 3D-CNN to complete sufficient feature extraction, but alsotakes into account the requirements of speed and calculation amount. Theinference speed and accuracy have been improved, with outstanding performanceon the IN, Pavia and KSC datasets, ahead of the mainstream hyperspectral imageclassification methods.

DGCNet: An Efficient 3D-Densenet based on Dynamic Group Convolution for Hyperspectral Remote Sensing Image Classification