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.

该研究设计并提出了一种基于动态分组卷积的轻量级模型，并应用于高光谱图像分类计算任务，取得了较高的速度和准确度，较传统方法优越。

基于动态分组卷积的高光谱遥感图像分类的高效三维 DenseNet (DGCNet)

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

Replacing normal convolutions with group convolutions can significantly
increase the computational efficiency of modern deep convolutional networks,
which has been widely adopted in compact network architecture designs. However,
existing group convolutions undermine the original network structures by
cutting off some connections permanently resulting in significant accuracy
degradation. In this paper, we propose dynamic group convolution (DGC) that
adaptively selects which part of input channels to be connected within each
group for individual samples on the fly. Specifically, we equip each group with
a small feature selector to automatically select the most important input
channels conditioned on the input images. Multiple groups can adaptively
capture abundant and complementary visual/semantic features for each input
image. The DGC preserves the original network structure and has similar
computational efficiency as the conventional group convolution simultaneously.
Extensive experiments on multiple image classification benchmarks including
CIFAR-10, CIFAR-100 and ImageNet demonstrate its superiority over the existing
group convolution techniques and dynamic execution methods. The code is
available at this https URL

本文提出一种动态组卷积 (Dynamic Group Convolution, DGC) 方法，它不仅保留了原有网络结构，还能够根据输入图像动态选择连接哪些输入通道，以实现对图像语义特征的自适应提取，有效提高了卷积神经网络的计算效率和图像分类性能。