Landsat 7 ETM+全色与多光谱数据融合算法的比较  

On fusion algorithms of Landsat 7 ETM+ PAN and multi-spectral images

作　　者：杨丽萍[1] 夏敦胜[2] 陈发虎[1] 

机构地区：[1]兰州大学西部环境教育部重点实验室,甘肃兰州730000 [2]中国科学院寒区旱区环境与工程研究所,甘肃兰州730000

出　　处：《兰州大学学报（自然科学版）》2007年第4期7-11,17,共6页Journal of Lanzhou University(Natural Sciences)

基　　金：国家自然科学基金项目(40571147);科技部国际合作重点项目(2002CB714004)资助.

摘　　要：以Landsat 7 ETM+数据为例,采用Brovey变换、HSV变换、PCA变换以及Gram-Schmidt光谱锐化算法对比研究了同一传感器全色和多光谱数据的融合问题.由定性和定量分析认为:空间分辨率的增强和光谱特性的改善是相互矛盾的,针对Landsat 7 ETM+本身的融合而言,综合考虑保光谱特性、信息量和清晰度,最佳的融合算法是PCA变换,而Gram-Schmidt光谱锐化法要优于Brovey变换和HSV变换.该研究为充分利用Landsat 7 ETM+的全色高分辨率和多光谱特性,进一步挖掘Landsat 7 ETM+的数据潜力奠定了基础.Four different fusion algorithms such as Brovey, HSV, PCA and Gram-Schmidt spectral sharpening were compared when the panchromatic and multi-spectral images of Landsat 7 ETM＋ were merged. From the qualitative and quantitative analyses we concluded that the enhancement of spatial resolution is inconsistent with the improvement of spectral characteristics. With consideration of the spectral information fidelity, information content and the improvement of spatial detail information, the experiment indicates that PCA is the best fusion algorithm, and Gram-Schmidt is better than Brovey and HSV. We believe that this research has laid a foundation for the fusion of Landsat 7 ETM＋ panchromatic and multi-spectral images.

关 键 词：增强型主题成像传感器 数据融合算法 全色与多光谱 

分 类 号：P237.3[天文地球—摄影测量与遥感] TP751.1[天文地球—测绘科学与技术]