Multi-View Structural Feature Extraction for Hyperspectral Image Classification

نویسندگان

چکیده

The hyperspectral feature extraction technique is one of the most popular topics in remote sensing community. However, methods are based on region-based local information descriptors while neglecting correlation and dependencies different homogeneous regions. To alleviate this issue, paper proposes a multi-view structural method to furnish complete characterization for spectral–spatial structures objects, which mainly made up following key steps. First, spectral number original image reduced with minimum noise fraction (MNF) method, relative total variation exploited extract from dimension data. Then, help superpixel segmentation technique, nonlocal features intra-view inter-view constructed by considering intra- inter-similarities superpixels. Finally, merged together form final classification. Experiments several real datasets indicate that proposed outperforms other state-of-the-art classification terms visual performance objective results, especially when training set limited.

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ژورنال

عنوان ژورنال: Remote Sensing

سال: 2022

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs14091971