A Lightweight Spatial–Spectral Deformable CNN for UAV Hyperspectral Image Classification

In recent years, uncrewed aerial vehicle (UAV) technology has shown great potential for application in hyperspectral image (HSI) classification tasks due to its advantages of flexible scheduling and fast response. However, existing deep learning-based classification algorithms have not thoroughly studied the problems caused by the higher spatial resolution of UAV HSIs. Such as more severe intraclass variation and higher computational resource consumption. These issues limit classification performance. To address these challenges, this article proposes a lightweight spatial–spectral deformable convolutional neural network (LS2DCNet) for UAV HSI classification. This network reduces computational resource usage and data processing time, meeting the fast-response application requirements while ensuring classification performance. First, spatial–spectral deformable convolution (S2DConv) is designed to construct a lightweight feature extraction network. This not only enhances the adaptive extraction of fine features but also reduces computational resource consumption and improves response speed. Second, a dynamic labeling-based joint loss (DL-JLoss) is designed to dynamically learn the distribution relationship between data classes. This improves the network’s generalization performance. A more realistic experimental validation was conducted on three UAV HSI datasets using regionally divided training samples, and inference comparisons were performed on embedded devices. The results show that the proposed LS2DCNet exhibits a better overall performance in terms of classification accuracy and inference speed. The relevant code can be found at https://github.com/niuroushu/A-Lightweight-Spatial-Spectral-Deformable-CNN-for-UAV-Hyperspectral-Image-Classification