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Abstract: Hyperspectral image (HSI) has hundreds of continuous bands that contain a lot of redundant information. Besides, a spatial patch of a hyperspectral cube often contains so... View more
Hyperspectral image (HSI) has hundreds of continuous bands that contain a lot of redundant information. Besides, a spatial patch of a hyperspectral cube often contains some pixels different from the center pixel category, which are usually called interference pixels. The existence of such interference pixels has a negative effect on extracting more discriminative information. Therefore, in this letter, a multiattention fusion network (MAFN) for HSI classification is proposed. Compared with the current state-of-the-art methods, MAFN uses band attention module (BAM) and spatial attention module (SAM), respectively, to alleviate the influence of redundant bands and interfering pixels. In this way, MAFN realizes feature reuse and obtains complementary information from different levels by combining multiattention and multilevel fusion mechanisms, which can extract more representative features. Experiments were conducted on two public HSI data sets to demonstrate the effectiveness of MAFN. Our source code is available at
https://github.com/Li-ZK/MAFN-2021
.
Date of Publication: 01 February 2021
References is not available for this document.
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Hyperspectral image (HSI) contains hundreds of narrow spectral bands with extremely high spectral resolution. Therefore, HSI classification is widely used in various remote sensing applications, such as urban development, environmental monitoring, military reconnaissance, and land cover classification.
IEEE Transactions on Geoscience and Remote Sensing
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
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