高光谱成像
探测器
计算机科学
窗口(计算)
异常检测
核(代数)
人工智能
传感器融合
灵敏度(控制系统)
滑动窗口协议
模式识别(心理学)
数据挖掘
计算机视觉
遥感
数学
工程类
电信
组合数学
电子工程
地质学
操作系统
标识
DOI:10.1117/1.jrs.9.097297
摘要
In hyperspectral anomaly detection, the dual-window-based detector is a widely used technique that employs two windows to capture nonstationary statistics of anomalies and background. However, its detection performance is usually sensitive to the choice of window sizes and suffers from inappropriate window settings. In this work, a decision-fusion approach is proposed to alleviate such sensitivity by merging the results from multiple detectors with different window sizes. The proposed approach is compared with the classic Reed-Xiaoli (RX) algorithm as well as kernel RX (KRX) using two real hyperspectral data. Experimental results demonstrate that it outperforms the existing detectors, such as RX, KRX, and multiple-window-based RX. The overall detection framework is suitable for parallel computing, which can greatly reduce computational time when processing large-scale remote sensing image data.
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