发表机构
Tufts University（塔夫茨大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出两种结合费马距离与泊松加权调和标签传播的主动学习算法，用于半监督高光谱图像分类，实验验证了算法的有效性与可扩展性。
AI 中文摘要
本文提出两种高光谱图像（HSI）分类的主动学习算法，将感知密度的费马距离与泊松加权调和标签传播相结合。所提算法采用基于不确定性的采集函数主动查询样本点，对泊松加权拉普拉斯学习（PWLL）进行了扩展。第一种算法费马主动拉普拉斯学习（FALL）利用所有数据点间的费马距离构建亲和矩阵，随后采用最小范数采集函数运行带对角扰动的PWLL；与之不同，近似费马主动拉普拉斯学习（A-FALL）计算每个数据点与通过最远点采样选出的地标像素间的费马距离，利用地标多维标度构建亲和矩阵，在多轮查询后，A-FALL通过一种留一交叉验证变体选择费马指数p。FALL与A-FALL借助费马距离及后续的调和标签传播，实现对数据流形的感知密度估计，提升标注准确率。在Salinas A与Pavia数据集上的实验验证了FALL的有效性及A-FALL对大型HSI场景的可扩展性。
英文摘要
Two active learning algorithms for hyperspectral image (HSI) classification are proposed that combine density-aware Fermat distances with Poisson-reweighted harmonic label propagation. Our methods actively query points using an uncertainty-based acquisition function, extending Poisson ReWeighted Laplace Learning (PWLL). Our first algorithm, Fermat Active Laplace Learning (FALL), builds an affinity matrix using Fermat distances between all data points. Then, PWLL is run with a diagonal perturbation using the minimum-norm acquisition function. In contrast, Approximate FALL (A-FALL) computes Fermat distances between each data point and landmark pixels selected via farthest-point sampling and constructs the affinity matrix using landmark multidimensional scaling. After several query rounds, A-FALL selects the Fermat exponent $p$ using a leave-one-out cross-validation variant. FALL and A-FALL leverage Fermat distances and subsequent harmonic label propagation to provide a density-aware estimation of the data manifold, improving labeling accuracy. Experiments on Salinas A and Pavia show the effectiveness of FALL and the scalability of A-FALL to large HSI scenes.
