Hyperspectral Image Classification Using Geometric Spatial-Spectral Feature Integration: A Class Incremental Learning Approach

Hyperspectral image classification (HSIC) has attracted widespread attention due to its important application in environment alterations and geophysical disaster monitoring. However, surface cultivation is not static as time passes, which leads to different hyperspectral image (HSI) information collected from the same area at different time periods. Therefore, researchers are currently eager to construct an HSIC model that continuously acquires new classes of data. During the continuous learning process, the model is expected to not only be effective in extracting unique spatial-spectral features of the HSI but also ensure the ability to maintain the old classes' knowledge while learning new data. To achieve this purpose, we propose a method that is based on geometric spatial-spectral feature integration network with class incremental learning (GS2FIN-CIL) framework in continuous learning to make the model adaptable to new classes' data and not overly forgetting the old classes' knowledge during the training process. We conduct extensive experiments with the proposed GS2FIN-CIL method on widely used hyperspectral datasets, including Indian Pines (IP), PaviaU, and Salinas (SA). The experimental results show that our GS2FIN-CIL method can achieve significantly improved results compared to current state-of-the-art class incremental learning (CIL) methods, allowing for efficient adaptation and utilization of spatial-spectral features in processing new classes of HSIs and alleviating the problem of catastrophic forgetting of learned old classes' knowledge. The GS2FIN-CIL method could be successfully applied to the challenge of adding new classes' data in the HSIC task.