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。