Abstract
Due to the difficulty of obtaining labeled data for hyperspectral images (HSIs), cross-scene classification has emerged as a widely adopted approach in the remote sensing community. It involves training a model using labeled data from a source domain (SD) and unlabeled data from a target domain (TD), followed by inferencing on the TD. However, variations in the reflectance spectrum of the same object between the SD and the TD, as well as differences in the feature distribution of the same land cover class, pose significant challenges to the performance of cross-scene classification. To address this issue, we propose a dual classification head self-training network (DHSNet). This method aligns class-wise features across domains, ensuring that the trained classifier can accurately classify TD data of different classes. We introduce a dual classification head self-training strategy for the first time in the cross-scene HSI classification field. The proposed approach mitigates domain gap while preventing the accumulation of incorrect pseudo-labels in the model. Additionally, we incorporate a novel central feature attention mechanism to enhance the model's capacity to learn scene-invariant features across domains. Experimental results on three cross-scene HSI datasets demonstrate that the proposed DHSNET significantly outperforms other state-of-the-art approaches. The code for DHSNet will be available at this https URL.
Abstract (translated)
由于获取高光谱图像（HSI）的标注数据难度较大，跨场景分类已成为遥感社区广泛采用的一种方法。这种方法涉及使用源域（SD）的有标签数据和目标域（TD）的无标签数据训练模型，并在目标域进行推断。然而，同一对象在源域与目标域之间的反射光谱差异以及同一地物类别在不同域中的特征分布差异对跨场景分类的性能构成了重大挑战。为解决这一问题，我们提出了一种双分类头自训练网络（DHSNet）。该方法通过跨领域对齐类别级别的特性，确保经过训练的分类器能够准确区分目标域的不同类别的数据。 我们在跨场景高光谱图像分类领域首次引入了双分类头自训练策略。所提出的这种方法不仅缩小了不同领域的差距，还防止了错误伪标签在模型中的累积。此外，我们融入了一种新颖的中心特性注意力机制来增强模型学习跨领域不变特征的能力。 实验结果表明，在三个跨场景高光谱图像数据集上的测试显示，我们的DHSNet显著优于其他最先进的方法。有关DHSNet的代码将在以下网址提供：[请在此处插入具体URL]。
URL
https://arxiv.org/abs/2502.17879
