Semi-Supervised anchor graph ensemble for large-scale hyperspectral image classification

He, Zezhao; Xia, Kewen; Hu, Yu; Yin, Zhixian; Wang, Sijie; Zhang, Jiangnan

doi:10.1080/01431161.2022.2048916

Cited by 6 publications

(1 citation statement)

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“…However, it is hard to label a large number of samples by users in real applications. Therefore, some improved classification technologies have been proposed, such as a semi-supervised classification framework [ 25 ], ensemble learning [ 26 ], and active learning [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Superpixel-Guided Structural Profiles for Hyperspectral Image Classification

Wang

Zeng²,

Duan³

et al. 2022

Sensors

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Hyperspectral image classification has received a lot of attention in the remote sensing field. However, most classification methods require a large number of training samples to obtain satisfactory performance. In real applications, it is difficult for users to label sufficient samples. To overcome this problem, in this work, a novel multi-scale superpixel-guided structural profile method is proposed for the classification of hyperspectral images. First, the spectral number (of the original image) is reduced with an averaging fusion method. Then, multi-scale structural profiles are extracted with the help of the superpixel segmentation method. Finally, the extracted multi-scale structural profiles are fused with an unsupervised feature selection method followed by a spectral classifier to obtain classification results. Experiments on several hyperspectral datasets verify that the proposed method can produce outstanding classification effects in the case of limited samples compared to other advanced classification methods. The classification accuracies obtained by the proposed method on the Salinas dataset are increased by 43.25%, 31.34%, and 46.82% in terms of overall accuracy (OA), average accuracy (AA), and Kappa coefficient compared to recently proposed deep learning methods.

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Section: Introductionmentioning

confidence: 99%