A Locally Adaptive Background Density Estimator: An Evolution for RX-Based Anomaly Detectors
A Locally Adaptive Background Density Estimator: An Evolution for RX-Based Anomaly Detectors
复制标题
DOI：
10.1109/lgrs.2013.2257670
复制
发表时间：
2014
影响因子：
4.8
通讯作者：
S. Matteoli;Tiziana Veracini;M. Diani;G. Corsini
中科院分区：
文献类型：
--
作者：
S. Matteoli;Tiziana Veracini;M. Diani;G. Corsini
提出了一种多高光谱图像的局部异常检测策略，利用核密度估计器估计背景概率密度函数，并将提取的局部自适应信息注入到带宽选择过程中。不同场景下的多光谱图像结果表明，该策略在异常检测和避免核宽度参数选择的有效性方面具有优势。
We propose a local anomaly detection strategy for multi-hyperspectral images in which the background probability density function is estimated with a kernel density estimator and locally adaptive information extracted from the image is injected into the bandwidth selection process. Results for multispectral images of different scenarios show the benefits of the proposed strategy regarding its effectiveness both at detecting anomalies and at avoiding the crucial issue of properly selecting the kernel-width parameter.