Enhanced Self-Training Superresolution Mapping Technique for Hyperspectral Imagery
Enhanced Self-Training Superresolution Mapping Technique for Hyperspectral Imagery
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DOI：
10.1109/lgrs.2010.2102334
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发表时间：
2011-02
影响因子：
4.8
通讯作者：
F. Mianji;Yanfeng Gu;Ye Zhang;Junping Zhang
中科院分区：
文献类型：
--
作者：
F. Mianji;Yanfeng Gu;Ye Zhang;Junping Zhang
本文提出了一种基于空间光谱数据融合的高光谱图像超分辨技术。采用线性混合模型和全约束最小二乘解混技术提取高分辨率图像的空间和光谱内容。然后，通过基于学习的超分辨率映射（SRM）算法，使用空间相关模型将这些数据组合在一起。提出的空间相关模型真实地模拟了低分辨率（LR） HS图像与其下采样版本（LR2 HS图像）之间的映射模型，以训练设计的SRM算法从LR映射到高分辨率。在真实HS图像上的实验验证了所提出的关键信息自主检测技术的准确性和低复杂度。
An efficient superresolution technique through spatial-spectral data fusion for hyperspectral (HS) imagery is proposed in this letter. The spatial and spectral contents of an HS image are extracted using a linear mixture model and a fully constrained least squares unmixing technique. These data are then combined using a spatial correlation model through a learning-based superresolution mapping (SRM) algorithm. The proposed spatial correlation model realistically simulates a mapping model between the low-resolution (LR) HS image and its subsampled version ( LR2 HS image) to train the designed SRM algorithm for mapping from the LR to high resolution. The experiments on real HS images validate the accuracy and low complexity of the proposed autonomous technique for key information detection in HS imagery.