Spatial-Spectral Hyperspectral Classification based on Learnable 3D
  Group Convolution
Guandong Li Mengxia Ye
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.