| 高光谱目标探测的进展与前沿问题 |
| |
| 引用本文： | 张良培. 高光谱目标探测的进展与前沿问题[J]. 武汉大学学报 ( 信息科学版), 2014, 39(12): 1377-1394+1400. |
| |
| 作者姓名： | 张良培 |
| |
| 作者单位： | 1武汉大学测绘遥感信息工程国家重点实验室湖北 武汉 430079 |
| |
| 基金项目： | 国家自然科学基金资助项目(41431175)~~ |
| |
| 摘 要： | 针对高光谱目标探测问题的主要挑战，将高光谱目标探测的进展与前沿问题分为两个方面进行综述。基于信号检测理论的方法如结构化背景的约束能量最小化方法、非结构化背景的自适应一致性余弦评估器等，是高光谱目标的探测经典算法;随着统计模式识别与机器学习领域中新技术的出现，一些数据驱动的目标探测方法逐渐成为了高光谱目标探测的前沿问题，如核方法、稀疏表达方法等。概述了两类方法的特点，比较了各自的优势和不足，并展望了高光谱目标探测未来的发展趋势。
|
| 关 键 词： | 高光谱图像处理 目标探测 信号检测 机器学习 |
| 收稿时间： | 2014-09-04 |
Advance and Future Challenges in Hyperspectral Target Detection |
| |
| Zhang Liangpei. Advance and Future Challenges in Hyperspectral Target Detection[J]. Geomatics and Information Science of Wuhan University, 2014, 39(12): 1377-1394+1400. |
| |
| Authors: | Zhang Liangpei |
| |
| Affiliation: | 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University Wuhan 430079 China |
| |
| Abstract: | In this review，recent developments and future challenges in hyperspectral target detection are considered in relation to the two main approaches.The signal detection framework induced methods such as the structured backgrounds detector constrained energy minimization(CEM) and the unstructured backgrounds detector adaptive cosine/coherent estimation(ACE) are the classical methods of hyperspectral target detection，while advanced statistical pattern recognition and machine learning based approaches such as the kernel method and the sparse representation related algorithms are becoming the frontier topic in this area. The core concepts of these methods as well as their advantages and disadvantages are overviewed，and the future prospects of hyperspectral target detection are out lined. |
| |
| Keywords: | hyperspectral image processing target detection signal detection machine learning |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《武汉大学学报(信息科学版)》浏览原始摘要信息 |
|
点击此处可从《武汉大学学报(信息科学版)》下载全文 |
