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