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Abstract：
Hyperspectral anomaly detection is used to recognize unusual patterns or anomalies in hyperspectral data. Currently, many spectral-spatial detection methods have been proposed with a cascaded manner; however, they often neglect the complementary characteristics between the spectral and spatial dimensions, which easily leads to yield high false alarm rate. To alleviate this issue, a spectral-spatial information fusion (SSIF) method is designed for hyperspectral anomaly detection. First, an isolation forest is exploited to obtain spectral anomaly map, in which the object-level feature is constructed with an entropy rate segmentation algorithm. Then, a local spatial saliency detection scheme is proposed to produce the spatial anomaly result. Finally, the spectral and spatial anomaly scores are integrated together followed by a domain transform recursive filtering to generate the final detection result. Experiments on five hyperspectral datasets covering ocean and airport scenes prove that the proposed SSIF produces superior detection results over other state-of-the-art detection techniques.
摘要：
高光谱异常检测用于识别高光谱数据中的异常模式或异常。目前,许多光谱空间检测方法已经提出了级联的方式；然而，它们往往忽略了光谱和空间维度之间的互补特征，这容易导致产生高误报率。为了缓解这个问题,设计了一种用于高光谱异常检测的光谱-空间信息融合(SSIF)方法。首先,利用隔离森林获取光谱异常图，其中使用熵率分割算法构造对象级特征。然后，提出了一种局部空间显著性检测方案来产生空间异常结果。最后,将频谱和空间异常分数集成在一起，然后进行域变换递归滤波以生成最终的检测结果。在覆盖海洋和机场场景的五个高光谱数据集上的实验证明，与其他最先进的检测技术相比，所提出的SSIF产生了更好的检测结果。
