Assessment of Spatial–Spectral Feature-Level Fusion for Hyperspectral Target Detection
Assessment of Spatial–Spectral Feature-Level Fusion for Hyperspectral Target Detection
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DOI：
10.1109/jstars.2015.2420651
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发表时间：
2015-05
影响因子：
5.5
通讯作者：
Jason R. Kaufman;M. Eismann;M. Celenk
中科院分区：
文献类型：
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作者：
Jason R. Kaufman;M. Eismann;M. Celenk
在这项工作中，我们评估的检测和分类专门构造的目标，在重合的机载高光谱图像（HSI）和高空间分辨率全色图像（HRI）的光谱，空间和联合空间-光谱特征空间。HSI和HRI的数据级和特征级融合的目标识别能力也直接比较在空间光谱背景下使用机载图像收集明确本研究。我们表明，在山猫2013年图像的情况下，HSI光谱与来自一致HRI数据的空间特征的特征级融合一致地导致场景背景上的虚警减少以及测试目标之间的误分类减少。此外，这种方法也优于计划，其中HSI和HRI图像的数据级融合之前，提取空间光谱特征。
In this work, we assess the detection and classification of specially constructed targets in coincident airborne hyperspectral imagery (HSI) and high spatial resolution panchromatic imagery (HRI) in spectral, spatial, and joint spatial-spectral feature spaces. The target discrimination powers of the data-level and feature-level fusion of HSI and HRI are also directly compared in the spatial-spectral context using airborne imagery collected explicitly for this research. We show that in the case of Bobcat 2013 imagery, feature-level fusion of the HSI spectrum with spatial features derived from the coincident HRI data consistently results in fewer false alarms on the scene background as well as fewer misclassifications among the tested targets. Furthermore, this approach also outperforms schemes in which data-level fusion of the HSI and HRI imagery is performed prior to extracting spatial-spectral features.