当前位置: X-MOL 学术J. Appl. Remote Sens. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Lithological mapping using EO-1 Hyperion hyperspectral data and semisupervised self-learning method
Journal of Applied Remote Sensing ( IF 1.7 ) Pub Date : 2021-09-01 , DOI: 10.1117/1.jrs.15.032209
Xiaoye Guo 1 , Peijun Li 1 , Jun Li 2
Affiliation  

Hyperspectral remote sensing data have been widely used in lithological identification and mapping. In existing studies, sufficient training samples are required. However, collecting sufficient labeled training samples for lithological classification in remote and inaccessible areas is generally time consuming, expensive, and even hard to implement, which causes the insufficient training sample problems in lithological classification using hyperspectral data. The semisupervised self-learning (SSL) method provides an alternative way of addressing this ill-posed problem by enlarging the training set from unlabeled samples using the limited labeled samples as a priori. This study evaluates and analyzes the SSL method in lithological mapping using EO-1 Hyperion hyperspectral image over a remote area. The performance of SSL with limited training samples was validated by comparing with multinomial logistic regression (MLR) and random forest (RF) classification using full training samples. The experimental results indicate that SSL method with limited training samples can produce results comparable with the MLR with all the training samples and better results than those of the RF with all the training samples. Therefore, the SSL method provides a useful way for hyperspectral lithological mapping with limited training samples over remote and inaccessible areas.

中文翻译:

使用 EO-1 Hyperion 高光谱数据和半监督自学习方法进行岩性成图

高光谱遥感数据已广泛应用于岩性识别和制图。在现有的研究中,需要足够的训练样本。然而,在偏远和交通不便的地区收集足够的标记训练样本进行岩性分类通常耗时、昂贵,甚至难以实施,这导致了使用高光谱数据进行岩性分类时训练样本不足的问题。半监督自学习 (SSL) 方法通过使用有限的标记样本作为先验来扩大未标记样本的训练集,提供了一种解决这种不适定问题的替代方法。本研究使用 EO-1 Hyperion 高光谱图像在偏远地区评估和分析了岩性测绘中的 SSL 方法。通过与使用完整训练样本的多项逻辑回归 (MLR) 和随机森林 (RF) 分类进行比较,验证了具有有限训练样本的 SSL 的性能。实验结果表明,有限训练样本的 SSL 方法可以产生与所有训练样本的 MLR 相当的结果,并且优于所有训练样本的 RF 方法。因此,SSL 方法为在偏远和无法到达的地区使用有限的训练样本进行高光谱岩性测绘提供了一种有用的方法。实验结果表明,有限训练样本的 SSL 方法可以产生与所有训练样本的 MLR 相当的结果,并且优于所有训练样本的 RF 方法。因此,SSL 方法为在偏远和无法到达的地区使用有限的训练样本进行高光谱岩性测绘提供了一种有用的方法。实验结果表明,有限训练样本的 SSL 方法可以产生与所有训练样本的 MLR 相当的结果,并且优于所有训练样本的 RF 方法。因此,SSL 方法为在偏远和无法到达的地区使用有限的训练样本进行高光谱岩性测绘提供了一种有用的方法。
更新日期:2021-09-01
down
wechat
bug