电光与控制, 2017, 24 (6): 53, 网络出版: 2021-01-25
基于边缘扩展和局部求和的高光谱异常目标检测
A Hyperspectral Anomaly Detection Algorithm Based on Edge Expansion and Local Summation
摘要
针对使用滑动窗不易检测高光谱图像边缘像元的缺陷, 采用边缘扩展的方法使得边缘像元能够得到正常检测;而后在滑动窗内采用局部求和的方式建立高光谱图像数据模型, 提高了检测的合理性;最后为验证算法的有效性, 利用真实的高光谱数据进行实验, 结果表明该算法优于传统RX算法和KRX算法, 能检测到更多的异常目标, 且降低了虚警率。
Abstract
Considering that it's not easy to detect the defect of the pixels on the edge of hyperspectral image with slide window, we used edge expansion method to make the pixels on the edge be detected normally. Then, a new hyperspectral data model was established in the form of local summation in slide window, hence the rationality was promoted. At last, to prove the effectiveness of the proposed algorithm, experiments were implemented with real data. The results show that this algorithm performs better than the traditional algorithms of RX and KRX, and it can detect more anomalies with lower false alarm rate.
常红伟, 王涛, 方浩, 吴志林. 基于边缘扩展和局部求和的高光谱异常目标检测[J]. 电光与控制, 2017, 24(6): 53. CHANG Hong-wei, WANG Tao, FANG Hao, WU Zhi-lin. A Hyperspectral Anomaly Detection Algorithm Based on Edge Expansion and Local Summation[J]. Electronics Optics & Control, 2017, 24(6): 53.