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Deep neural networks have faced many problems in hyperspectral imageclassification, including the ineffective utilization of spectral-spatial jointinformation and the problems of gradient vanishing and overfitting that arisewith increasing depth. In order to accelerate the deployment of models on edgedevices with strict latency requirements and limited computing power, thispaper proposes a learnable group convolution network (LGCNet) based on animproved 3D-DenseNet model and a lightweight model design. The LGCNet moduleimproves the shortcomings of group convolution by introducing a dynamiclearning method for the input channels and convolution kernel grouping,enabling flexible grouping structures and generating better representationability. Through the overall loss and gradient of the backpropagation network,the 3D group convolution is dynamically determined and updated in an end-to-endmanner. The learnable number of channels and corresponding grouping can capturedifferent complementary visual features of input images, allowing the CNN tolearn richer feature representations. When extracting high-dimensional andredundant hyperspectral data, the 3D convolution kernels also contain a largeamount of redundant information. The LGC module allows the 3D-DenseNet tochoose channel information with more semantic features, and is very efficient,making it suitable for embedding in any deep neural network for accelerationand efficiency improvements. LGC enables the 3D-CNN to achieve sufficientfeature extraction while also meeting speed and computing requirements.Furthermore, LGCNet has achieved progress in inference speed and accuracy, andoutperforms mainstream hyperspectral image classification methods on the IndianPines, Pavia University, and KSC datasets.

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