摘要
在ETM+影像全色波段和多光谱数据融合时,Brovey变换是一种较好的融合方法,但是Brovey变换所利用的波段信息量少,并且在对融合后影像分类时常将存在阴影的植被覆盖区误判为水体。因此将主成分和归一化植被指数(NDVI)作为Brovey变换融合时的波段,实验结果显示融合后的影像更利于后期植被信息提取。
Data fusion on remote sensing images can improve visualization of the images involved. For the data fusion between multi-spectral images and panchromatic image of Landsat-7 satellite, Brovey transform is better than PCA transformation or HIS transformation. However, Brovey transformation only uses three bands of multi-spectral images. PCA can compress more than 95 of the original information into PC1 and PC2, and the information of vegetation can be showed in NDVI image. So, PC1 ,PC2 and NDVI were used as the fusion bands of Brovey transformation in this paper. The experimental results showed that vegetation information can be better obtained by the bands compounding than by former bands compounding.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2007年第9期87-89,共3页
Transactions of the Chinese Society for Agricultural Machinery
基金
江苏省科技厅高技术研究资助项目(项目编号:BG2005328)
关键词
植被
遥感影像
融合
信息提取
Brovey变换
主成分分析
Vegetation, Remote sensing image, Fusion, Information extraction, Brovey transformation, PCA
