HyperNet: A deep network for hyperspectral, multispectral, and panchromatic image fusion

全色胶片 多光谱图像 计算机科学 高光谱成像 人工智能 图像分辨率 图像融合 模式识别(心理学) 特征(语言学) 计算机视觉 特征提取 遥感 图像(数学) 地理 语言学 哲学
作者
Kun Li,Wei Zhang,Dian Yu,Xin Tian
出处
期刊:Isprs Journal of Photogrammetry and Remote Sensing 卷期号:188: 30-44 被引量:32
标识
DOI:10.1016/j.isprsjprs.2022.04.001
摘要

Traditional approaches mainly fuse a hyperspectral image (HSI) with a high-resolution multispectral image (MSI) to improve the spatial resolution of the HSI. However, such improvement in the spatial resolution of HSIs is still limited because the spatial resolution of MSIs remains low. To further improve the spatial resolution of HSIs, we propose HyperNet, a deep network for the fusion of HSI, MSI, and panchromatic image (PAN), which effectively injects the spatial details of an MSI and a PAN into an HSI while preserving the spectral information of the HSI. Thus, we design HyperNet on the basis of a uniform fusion strategy to solve the problem of complex fusion of three types of sources (i.e., HSI, MSI, and PAN). In particular, the spatial details of the MSI and the PAN are extracted by multiple specially designed multiscale-attention-enhance blocks in which multi-scale convolution is used to adaptively extract features from different reception fields, and two attention mechanisms are adopted to enhance the representation capability of features along the spectral and spatial dimensions, respectively. Through the capability of feature reuse and interaction in a specially designed dense-detail-insertion block, the previously extracted features are subsequently injected into the HSI according to the unidirectional feature propagation among the layers of dense connection. Finally, we construct an efficient loss function by integrating the multi-scale structural similarity index with the L1 norm, which drives HyperNet to generate high-quality results with a good balance between spatial and spectral qualities. Extensive experiments on simulated and real data sets qualitatively and quantitatively demonstrate the superiority of HyperNet over other state-of-the-art methods.
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