基于局部表征少样本学习的高光谱图像跨场景分类

In cross-scene classification tasks, most domain adaptation (DA) methods typically focus on transfer tasks where the source domain data and the target domain data are obtained using the same sensor and share the same land cover class. However, the adaptive performance is significantly reduced when new classes are present in the target data. Moreover, many hyperspectral image (HSI) classification methods rely on a global representation mechanism, where representation learning is performed on samples with fixed-size windows, limiting their ability to effectively represent ground object classes. A framework called local representation few-shot learning (LrFSL) is proposed, which aims to overcome the limitations of global representation ability by constructing a local representation mechanism in few-shot learning. In this proposed framework, meta-tasks are created for all labeled source domain data and a few labeled target domain data, and scenario training is performed simultaneously using a meta-learning strategy. Additionally, an Intra-domain local representation block (ILR-block) is designed to extract semantic information from multiple local representations within each sample. Furthermore, the inter-domain local alignment block (ILA-block) is designed to align cross-domain class-wise distribution, thereby mitigating the impact of domain shift on few-shot learning. Experimental results on three publicly available HSI datasets demonstrate that the proposed method outperforms state-of-the-art methods by a significant margin.