Saliency-Based Endmember Detection for Hyperspectral Imagery
Saliency-Based Endmember Detection for Hyperspectral Imagery
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DOI：
10.1109/tgrs.2018.2805340
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发表时间：
2018-03
影响因子：
8.2
通讯作者：
Xinyu Wang;Yanfei Zhong;Yao Xu;Liangpei Zhang;Yanyan Xu
中科院分区：
文献类型：
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作者：
Xinyu Wang;Yanfei Zhong;Yao Xu;Liangpei Zhang;Yanyan Xu
本文重点研究了用于高光谱图像分析的端元提取技术。我们首先证明了重建误差（RE）和丰度异常（AAs）（丰度不满足丰度约束）是有效的提取未检测到的端元。然后，根据端元对象的空间连续性，并不同于噪声或离群值与稀疏分布，端元被假定为位于RE和AA地图中的一些显着的区域。提出了一种新的基于显著性的端元检测算法（SED），该算法引入视觉显著性模型来探索和分析AA和RE图中包含的空间信息。具体来说，AA和RE地图被视为视觉输入，而端元被视为视觉刺激。在SED中，我们假设纯像素假设成立。基于人类视觉系统的特点，该方法不仅可以提取均匀区域的端元，但它也可以突出的小目标，其丰度可能是空间变化的。此外，由于在重建过程中充分利用了空间信息，因此在重建过程中自动考虑了端元对高光谱场景的表征能力，检测出的端元既准确又可靠。在模拟和真实的高光谱数据上的实验结果证实了该算法的优点和可行性。
This paper focuses on the endmember extraction (EE) technique for analyzing hyperspectral images. We first prove that the reconstruction errors (REs) and abundance anomalies (AAs) (abundances that fail to satisfy the abundance constraints) are effective in extracting undetected endmembers. Then, according to the spatial continuity of the endmember objects and differing from noise or outliers with a sparse distribution, the endmembers are assumed to be located at some salient areas in the RE and AA maps. A novel EE algorithm termed saliency-based endmember detection (SED) is proposed, where the visual saliency model is introduced to explore and analyze the spatial information that is contained in the AA and RE maps. Specifically, the AA and RE maps are regarded as the visual inputs, whereas the endmembers are treated as the visual stimuli. In SED, we assume that the pure pixel assumption holds. Based on the characteristics of the human visual system, the proposed method can not only extract endmembers in homogenous areas, but it can also highlight the small targets whose abundances may be spatially varied. In addition, since the spatial information is exploited in the reconstruction, the capability of the endmembers to represent the hyperspectral scene is automatically considered in the process of EE, and the detected endmembers are both accurate and reliable. The experimental results obtained on both simulated and real hyperspectral data confirm the merits and viability of the proposed algorithm.