Multispectral remote sensing image cross simulation based on nonlinear spectral fitting model
The remote sensing image recorded the ground object spectral responses with a special spectral, temporal, and spatial resolution. There are some complex relationship may exist between the remote sensing images with different spectral, spatial, and temporal scale. In this study, we try to use a nonlinear regression model – Cubist regression tree model to mining the spectral relationship between the image bands. The Landsat5 TM image was used as reference image to collect samples to train Cubist model, and then the target image – SPOT5 image was used to predict its lacked TM-liked band1 and band7 with the TM-trained Cubist model. The experiments shows that the Cubist nonlinear regression model could simulate TM band1 and band7 with a high accuracy and the TM-trained Cubist model also could be used to predict SPOT5 lacked TM-liked band1 and band7.
Multispectral cross simulation SPOT5 TM Cubist
Jinxiang SHEN Liao YANG Xi CHEN Junli Li
Xinjiang Ecology and Geography Institute, Chinese Academy of Science, No. 818, South of Beijing Road Xinjiang Ecology and Geography Institute, Chinese Academy of Science, No. 818, South of Beijing Road
国际会议
桂林
英文
1-8
2011-11-01（万方平台首次上网日期，不代表论文的发表时间）