Lightweight and Lifelong Hyperspectral Image Classification via Attention-Based Reservoir Computing

The continual progression and expanding applications of hyperspectral image (HSI) technology necessitate the development of lightweight HSI classification models that are capable of lifelong learning. However, the computationally-demanding task of training and updating HSI classification models, exacerbated by the substantial number of trainable parameters in feature extractors, remains a substantial challenge. This article proposes an attention-based reservoir computing (ARC) model to overcome these hurdles. The ARC model utilizes a cross-slicing operation to generate multidirectional inputs, treating the HSI dataset as a spatial sequence for processing within a reservoir. For every target pixel, four spatial sequences from various directions are introduced into the reservoir, generating four corresponding outputs. A voting mechanism then evaluates these outputs to yield the final prediction. In addition, we design an attention-based leaky function for reservoir computing (RC) to capture the spatial correlation inherent in HSI data accurately. The attention-based leaky function enables the reservoir state to weigh less on the pixels outside the region of interest (ROI) and have a longer memory for pixels inside the ROI. The ARC was tested on widely used HSI datasets: Indian Pines, PaviaU, and Salinas. It demonstrated competitive lightweight classification performance against state-of-the-art lightweight models by maintaining comparable training time while achieving superior accuracy. Furthermore, the model’s lifelong learning accuracy also showed superior performance compared to existing lifelong learning models, with a one thousand times reduction of parameter-to-be-updated. This work makes the ARC model an effective contender for HSI classification tasks, excelling in both lightweight classification and lifelong learning capacities. The source codes are available publicly at:
https://github.com/Waterman-Ann/ARC
.