Multi-dimensional, multi-branch hyperspectral remote sensing image classification with limited training samples

Deep learning-based hyperspectral remote sensing image classification methods are currently a research hotspot. However, they suffer from issues such as large feature network parameter size, complex calculations, and the need for a large number of training data to achieve good classification results. Moreover, hyperspectral remote sensing images face challenges such as difficulty in obtaining the ground truth of land cover, limited availability of effective datasets for training, and endmember spectral variability, making it difficult for existing algorithm models to be widely adopted. To address these issues, this paper proposes a multi-branch classification model with multi-dimensional feature fusion, constructing lightweight deep network models for one-dimensional spectral, two-dimensional spatial, and three-dimensional depth feature extraction, respectively. This enriches feature information while reducing the parameters of each branch’s deep model, effectively improving the land cover classification accuracy using hyperspectral remote sensing images under limited training sample conditions. Experimental verification with open-source hyperspectral remote sensing datasets shows that the proposed classification method can obtain over 90% classification accuracy when the training set account for only 5% of the total dataset, which is significantly better than current mainstream deep network classification models.