学术
摘 要:
冗余信息的大量存在,给高光谱数据的分析和处理带来很大的困难.波段选择能够有效地去除由于高光谱图像较高的数据维导致的冗余信息,从而减少计算量.高光谱图像处理的另一重要技术--端元选择到波段选择存在着方法上的可移植性.作者经过可行性分析,将3种典型的端元选择算法应用于波段选择之中,并通过引入自动子空间分解、核主成分分析、距离等效原则等内容来解决由此带来的相关问题.仿真实验证明了这种算法移植的有效性.[著者文摘]
文章出处:
《吉林大学学报：工学版》-2007年37卷4期 -915-919页
分 类 号:
文献标识码:
A
文章编号:
1671-5497（2007）04-0915-05
Application of endmember extraction method to band selection
Wang Li-guo, Zhao Chun-hui, Bi Xiao-jun （College of Information and Communications Engineering, Harbin Engineering University, Harbin 150001, China）
Abstract:
High dimensionality of hyperspectral imagery leads to the existing of much redundant information. This case brings difficulty to the analysis and processing of hyperspectral data. Band selection can remove the redundant information and so reduce the computational cost. Endmember selection is another important technique of hyperspectral imagery processing. Theoretically speaking, it is possible to transplant endmember selection algorithm to band selection. After the analysis of feasibility, three endmember selection algorithms are used in band selection, and the subsequent technique problems are also resolved by introduced automatic spatial partition, kernel based PCA （principal component analysis） and distance equivalent principle. Numerical experiments show the validation of this kind of algorithm transplantation.[著者文摘]
Key words:
information processing; band selection; endmember selection; algorithm transplantation hyperspectral imagery
基金资助:
国家自然科学基金资助项目（60672034）;高等学校博士学科点专项科研基金资助基金（20060217021）.
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