摘要
为了进一步分析光谱相似性度量在高光谱图像处理中的应用,从距离、投影等角度充分归纳总结了现有二元光谱相似度量方法,并分析讨论了二元光谱相似度量存在的问题。重点介绍了一种多元光谱相似性测量方法,也称N维立体光谱角(N-dimensional solid spectral angle,NSSA)方法。NSSA方法从本质上突破了传统的二元光谱角(spectral angle mapping,SAM)仅能计算两个光谱之间夹角的局限性,具备联合计算多元光谱欧氏空间夹角的能力,为评价多元光谱联合相似性提供了一种定量化的度量手段。最后,对NSSA方法在高光谱波段选择及端元提取领域的潜在研究价值和应用现状进行了分析和展望。通过分析表明NSSA方法所具备的特性可更好地实现光谱相似性度量,在高光谱图像处理领域具有较高的研究价值。
Spectral similarity metrics are important in the field of hyperspectral data analysis. To further analyze their application in hyperspectral image processing, the current binary spectral similarity metrics method based on distance or projection was summarized. The problems in the binary spectral similarity metrics were analyzed and discussed. Then, this study chiefly introduced a multiple spectral similarity metric called N-dimensional solid spectral angle （ NSSA） . The NSSA method breaks through the limitation of the traditional binary spectral angle mapping in essence, which can not only calculate the angle between two spectra but also the angle constructed by multiple spectra jointly in Euclidean space. The method provides a quantitative measure to evaluate the joint similarity of multivariate spectra. The potential research value and applications of the NSSA method in hyperspectral band selection and endmembers extraction were analyzed and forecasted. The analysis indicates that the NSSA method can better realize the spectral similarity measure and has high research value in the field of hyperspectral imaging process.
出处
《哈尔滨工程大学学报》
EI
CAS
CSCD
北大核心
2017年第8期1179-1189,共11页
Journal of Harbin Engineering University
基金
国家自然科学基金项目(61405041
61571145)
黑龙江省自然科学基金重点项目(ZD201216)
哈尔滨市优秀学科带头人基金项目(RC2013XK009003)
关键词
高光谱图像
光谱相似性度量
二元光谱角
N维立体光谱角方法
多元光谱相似性度量
波段选择
端元提取
hyperspectral imagery
spectral similarity metrics
binary spectral angle mapping
N dimensional solid spectral angle
multiple spectra similarity metric
band selection
endmember extraction
