Bai Yu, Liu Dongmin, Zhang Lili, Wu Haoqi
Electronic and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China.
Sensors (Basel). 2024 Oct 15;24(20):6647. doi: 10.3390/s24206647.
The cost of hyperspectral image (HSI) classification primarily stems from the annotation of image pixels. In real-world classification scenarios, the measurement and annotation process is both time-consuming and labor-intensive. Therefore, reducing the number of labeled pixels while maintaining classification accuracy is a key research focus in HSI classification. This paper introduces a multi-strategy triple network classifier (MSTNC) to address the issue of limited labeled data in HSI classification by improving learning strategies. First, we use the contrast learning strategy to design a lightweight triple network classifier (TNC) with low sample dependence. Due to the construction of triple sample pairs, the number of labeled samples can be increased, which is beneficial for extracting intra-class and inter-class features of pixels. Second, an active learning strategy is used to label the most valuable pixels, improving the quality of the labeled data. To address the difficulty of sampling effectively under extremely limited labeling budgets, we propose a new feature-mixed active learning (FMAL) method to query valuable samples. Fine-tuning is then used to help the MSTNC learn a more comprehensive feature distribution, reducing the model's dependence on accuracy when querying samples. Therefore, the sample quality is improved. Finally, we propose an innovative dual-threshold pseudo-active learning (DSPAL) strategy, filtering out pseudo-label samples with both high confidence and uncertainty. Extending the training set without increasing the labeling cost further improves the classification accuracy of the model. Extensive experiments are conducted on three benchmark HSI datasets. Across various labeling ratios, the MSTNC outperforms several state-of-the-art methods. In particular, under extreme small-sample conditions (five samples per class), the overall accuracy reaches 82.97% (IP), 87.94% (PU), and 86.57% (WHU).
高光谱图像（HSI）分类的成本主要源于图像像素的标注。在实际的分类场景中，测量和标注过程既耗时又费力。因此，在保持分类精度的同时减少标记像素的数量是HSI分类中的一个关键研究重点。本文引入了一种多策略三重网络分类器（MSTNC），通过改进学习策略来解决HSI分类中标记数据有限的问题。首先，我们使用对比学习策略设计了一种对样本依赖性低的轻量级三重网络分类器（TNC）。由于三重样本对的构建，可以增加标记样本的数量，这有利于提取像素的类内和类间特征。其次，使用主动学习策略来标记最有价值的像素，提高标记数据的质量。为了解决在极其有限的标注预算下有效采样的困难，我们提出了一种新的特征混合主动学习（FMAL）方法来查询有价值的样本。然后使用微调来帮助MSTNC学习更全面的特征分布，减少模型在查询样本时对准确性的依赖。因此，样本质量得到提高。最后，我们提出了一种创新的双阈值伪主动学习（DSPAL）策略，过滤掉具有高置信度和不确定性的伪标签样本。在不增加标注成本的情况下扩展训练集进一步提高了模型的分类精度。我们在三个基准HSI数据集上进行了广泛的实验。在各种标注比例下，MSTNC均优于几种最新方法。特别是在极端小样本条件下（每类五个样本），总体准确率达到82.97%（IP）、87.94%（PU）和86.57%（WHU）。
