当前位置: X-MOL 学术Int. J. 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.)
Dual unsupervised features fusion for hyperspectral image classification
International Journal of Remote Sensing ( IF 3.4 ) Pub Date : 2020-06-01 , DOI: 10.1080/01431161.2020.1736729
Bing Tu 1, 2 , Xiaofei Zhang 1 , Guoyun Zhang 1 , Jinping Wang 1 , Wei He 1
Affiliation  

ABSTRACT An unsupervised feature extraction method could effectively address the phenomenon where using a limited number of labelled samples leads to poor hyperspectral image (HSI) classification. Inspired by this method, we have developed a new method based on fusing dual unsupervised features for HSI classification in this paper. First, we used principal component analysis (PCA) to reduce the dimensions of HSI data and obtain the first set of unsupervised features. Then, we got the second set of unsupervised features by adopting the self-taught learning method, which could learn more discriminatory features by making full use of the information of unlabelled samples in the same HSI. Next, we used the correlation canonical analysis (CCA) algorithm to fuse linearly dual unsupervised learning features. Finally, we proved the effectiveness of the features extracted by the proposed algorithm by applying the extreme learning machine (ELM) classifier to evaluate the purposes. Experiments with three widely used real HSI datasets showed good classification performance when the number of training samples was quite limited. This demonstrates that the proposed feature learning method was indeed an effective method for HSI classification.

中文翻译:

用于高光谱图像分类的双无监督特征融合

摘要 无监督特征提取方法可以有效解决使用有限数量的标记样本导致高光谱图像(HSI)分类不佳的现象。受此方法的启发,我们在本文中开发了一种基于融合双无监督特征进行 HSI 分类的新方法。首先,我们使用主成分分析 (PCA) 来降低 HSI 数据的维度并获得第一组无监督特征。然后,我们采用自学的学习方法得到了第二组无监督特征,可以充分利用同一HSI中未标记样本的信息来学习更多的判别性特征。接下来,我们使用相关规范分析(CCA)算法来融合线性对偶无监督学习特征。最后,我们通过应用极限学习机(ELM)分类器来评估目的,证明了所提出算法提取的特征的有效性。当训练样本数量非常有限时,三个广泛使用的真实 HSI 数据集的实验显示出良好的分类性能。这表明所提出的特征学习方法确实是一种有效的 HSI 分类方法。
更新日期:2020-06-01
down
wechat
bug