  The mining and utilization of features directly affect the classification
performance of models used in the classification and recognition of
hyperspectral remote sensing images. Traditional models usually conduct feature
mining from a single perspective, with the features mined being limited and the
internal relationships between them being ignored. Consequently, useful
features are lost and classification results are unsatisfactory. To fully mine
and utilize image features, a new multi-scale feature-mining learning algorithm
(MGRNet) is proposed. The model uses principal component analysis to reduce the
dimensionality of the original hyperspectral image (HSI) to retain 99.99% of
its semantic information and extract dimensionality reduction features. Using a
multi-scale convolution algorithm, the input dimensionality reduction features
were mined to obtain shallow features, which then served as inputs into a
multi-scale graph convolution algorithm to construct the internal relationships
between eigenvalues at different scales. We then carried out cross fusion of
multi-scale information obtained by graph convolution, before inputting the new
information obtained into the residual network algorithm for deep feature
mining. Finally, a flexible maximum transfer function classifier was used to
predict the final features and complete the classification. Experiments on
three common hyperspectral datasets showed the MGRNet algorithm proposed in
this paper to be superior to traditional methods in recognition accuracy.
