Res-LK-SLR: A Residual Network Based on Large Kernels and Shapelet-Level Representations for Spatial Infrared Spot Target Discrimination

Spatial infrared spot target (SIST) discrimination based on infrared radiation sequences (IRSs) can be considered a univariate trending time series classification task. However, due to the complexity of actual scenarios and the limited opportunities for acquiring IRSs, resulting in noise interference, extremely small-scale datasets with imbalanced distribution of classes and widely varying sequence lengths range from a few hundred to several thousand time steps. Current research is primarily based on idealized simulation datasets, resulting in a performance gap when applied to actual applications. To address these issues, firstly, we construct a simulation dataset tailored to the challenges of actual scenarios. Secondly, we design a practical data preprocessing method to achieve uniform sequence length, coarse alignment of shapelets and filtering while preserving key points. Thirdly, we propose a residual network Res-LK-SLR for IRS classification based on large kernels (LKs, providing long-term dependence) and shapelet-level representations (SLRs, where the hidden layer features are aligned with the learned high-level representations to obtain the optimal segmentation and generate shapelet-level representations). Additionally, we conduct extensive evaluations and validations on both the simulation dataset and 18 UCR time series classification datasets. The results demonstrate the effectiveness and generalization ability of our proposed Res-LK-SLR.