高光谱成像
计算机科学
模式识别(心理学)
人工智能
图像分辨率
背景(考古学)
特征(语言学)
空间语境意识
k-最近邻算法
全光谱成像
光谱带
计算机视觉
遥感
地理
语言学
哲学
考古
作者
Heng Wang,Libiao Wang,Yuan Yuan
出处
期刊:IEEE Transactions on Circuits and Systems for Video Technology
[Institute of Electrical and Electronics Engineers]
日期:2023-11-01
卷期号:33 (11): 6331-6346
标识
DOI:10.1109/tcsvt.2023.3268178
摘要
Single hyperspectral image super-resolution aims to reconstruct a high-resolution hyperspectral image from a low-resolution one, which does not use any auxiliary images. For now, existing super-resolution methods often ignore the difference between the features of neighbor and non-neighbor spectral bands, leaving the feature exploration untargeted. As a result, the complementary information of such bands has not been effectively exploited. To do so, we propose an asymmetric dual-direction quasi-recursive network for single hyperspectral image super-resolution, which separately explores the features among neighbor and non-neighbor bands via forward and backward units. By considering the high similarity among neighbor bands, each forward unit thoroughly exploits spatial-spectral features among such bands through two kinds of correspondence aggregation modules. It also preserves a spectral structure by a spectral band grouping strategy and a spatial-spectral consistency module. Owning to the inconsecutive spectra among non-neighbor bands, backward units focus on extracting spatial features in such bands. With the aid of a global feature context fusion module, the information of global non-neighbor context and neighbor bands are adaptively fused, thus improving information completeness and complementarity. Experimental results reported for natural and remote sensing hyperspectral image datasets demonstrate the proposed network not only outperforms the state-of-the-art methods in terms of reconstruction quality and noise suppression, but also requires a smaller memory footprint.
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