SSML: Spectral-Spatial Mutual-Learning-Based Framework for Hyperspectral Pansharpening
SSML: Spectral-Spatial Mutual-Learning-Based Framework for Hyperspectral Pansharpening
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DOI：
10.3390/rs14184682
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Xi-yuan Peng;Yi-Shiang Fu;Shenglin Peng;Kai Ma;Lu Liu;J. Wang
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作者：
Xi-yuan Peng;Yi-Shiang Fu;Shenglin Peng;Kai Ma;Lu Liu;J. Wang
本文考虑了高光谱泛锐化网络的大尺寸问题和学习其空间光谱特征的困难。提出了一种基于深度互学习的光谱空间信息挖掘和高光谱泛锐化框架（SSML）。该框架引入深度互学习机制，通过信息传递相互学习空间特征和光谱特征，在不输入过多参数的情况下获得较好的融合效果。提出的SSML框架包括两个独立的网络，用于学习hsi和全色图像（pan）的光谱和空间特征。设计了一个包含约束谱和空间信息的混合损失函数，以加强两个网络之间的相互学习。此外，采用互学习策略来平衡频谱和空间特征学习，以提高SSML路径的性能。大量的实验结果证明了互学习机制和所提出的混合损失函数用于高光谱泛锐化的有效性。此外，采用典型的深度学习方法验证了所提框架的泛化能力。在所有病例中均观察到理想的性能。此外，对所使用的参数进行了多次实验分析，结果表明该方法在不添加过多参数的情况下取得了较好的融合效果。因此，提出的SSML代表了一个有前途的高光谱泛锐化框架。
This paper considers problems associated with the large size of the hyperspectral pansharpening network and difficulties associated with learning its spatial-spectral features. We propose a deep mutual-learning-based framework (SSML) for spectral-spatial information mining and hyperspectral pansharpening. In this framework, a deep mutual-learning mechanism is introduced to learn spatial and spectral features from each other through information transmission, which achieves better fusion results without entering too many parameters. The proposed SSML framework consists of two separate networks for learning spectral and spatial features of HSIs and panchromatic images (PANs). A hybrid loss function containing constrained spectral and spatial information is designed to enforce mutual learning between the two networks. In addition, a mutual-learning strategy is used to balance the spectral and spatial feature learning to improve the performance of the SSML path compared to the original. Extensive experimental results demonstrated the effectiveness of the mutual-learning mechanism and the proposed hybrid loss function for hyperspectral pan-sharpening. Furthermore, a typical deep-learning method was used to confirm the proposed framework’s capacity for generalization. Ideal performance was observed in all cases. Moreover, multiple experiments analysing the parameters used showed that the proposed method achieved better fusion results without adding too many parameters. Thus, the proposed SSML represents a promising framework for hyperspectral pansharpening.