Deep neural networks have faced many problems in hyperspectral image
classification, including the ineffective utilization of spectral-spatial joint
information and the problems of gradient vanishing and overfitting that arise
with increasing depth. In order to accelerate the deployment of models on edge
devices with strict latency requirements and limited computing power, this
paper proposes a learnable group convolution network (LGCNet) based on an
improved 3D-DenseNet model and a lightweight model design. The LGCNet module
improves the shortcomings of group convolution by introducing a dynamic
learning method for the input channels and convolution kernel grouping,
enabling flexible grouping structures and generating better representation
ability. Through the overall loss and gradient of the backpropagation network,
the 3D group convolution is dynamically determined and updated in an end-to-end
manner. The learnable number of channels and corresponding grouping can capture
different complementary visual features of input images, allowing the CNN to
learn richer feature representations. When extracting high-dimensional and
redundant hyperspectral data, the 3D convolution kernels also contain a large
amount of redundant information. The LGC module allows the 3D-DenseNet to
choose channel information with more semantic features, and is very efficient,
making it suitable for embedding in any deep neural network for acceleration
and efficiency improvements. LGC enables the 3D-CNN to achieve sufficient
feature extraction while also meeting speed and computing requirements.
Furthermore, LGCNet has achieved progress in inference speed and accuracy, and
outperforms mainstream hyperspectral image classification methods on the Indian
Pines, Pavia University, and KSC datasets.

本文提出了一种基于改进的 3D-DenseNet 模型和轻量级模型设计的可学习组卷积网络 (LGCNet)，用于加速在具有严格延迟要求和受限计算能力的边缘设备上模型的部署，该网络通过引入针对输入通道和卷积核分组的动态学习方法改善了组卷积的不足，从而实现灵活的分组结构和更好的表示能力。此外，LGCNet 在印度藤条，帕维亚大学和 KSC 数据集上取得了速度和精度方面的进展，优于主流的高光谱图像分类方法。

基于可学习的三维分组卷积的空间光谱高光谱分类

Spatial-Spectral Hyperspectral Classification based on Learnable 3D  Group Convolution

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。