论文标题
高光谱图像的光谱空间融合异常检测方法
A spectral-spatial fusion anomaly detection method for hyperspectral imagery
论文作者
论文摘要
在高光谱中，高质量的光谱信号传达了微妙的光谱差异以区分相似的材料，从而为异常检测提供了独特的优势。因此，可以从异质背景像素中有效筛选异常像素的细光谱。由于相同的材料在空间和光谱维度上具有相似的特征，因此通过接合空间和光谱信息可以显着提高检测性能。在本文中，提出了用于高光谱图像的光谱融合异常检测（SSFAD）方法。首先，原始光谱信号映射到由中位数组成的局部线性背景空间，并具有较高的置信度，其中实施了显着权重和特征增强策略以在光谱域中获得初始检测图。 futhermore，为了充分利用测试像素周围本地背景的相似性信息，新的检测器旨在提取空间域中补丁图像的局部相似性空间特征。最后，通过自适应结合光谱和空间检测图来检测异常。实验结果表明，我们所提出的方法比传统方法具有优越的检测性能。
In hyperspectral, high-quality spectral signals convey subtle spectral differences to distinguish similar materials, thereby providing unique advantage for anomaly detection. Hence fine spectra of anomalous pixels can be effectively screened out from heterogeneous background pixels. Since the same materials have similar characteristics in spatial and spectral dimension, detection performance can be significantly enhanced by jointing spatial and spectral information. In this paper, a spectralspatial fusion anomaly detection (SSFAD) method is proposed for hyperspectral imagery. First, original spectral signals are mapped to a local linear background space composed of median and mean with high confidence, where saliency weight and feature enhancement strategies are implemented to obtain an initial detection map in spectral domain. Futhermore, to make full use of similarity information of local background around testing pixel, a new detector is designed to extract the local similarity spatial features of patch images in spatial domain. Finally, anomalies are detected by adaptively combining the spectral and spatial detection maps. The experimental results demonstrate that our proposed method has superior detection performance than traditional methods.
