Multispectral remote sensing image cross simulation based on nonlinear spectral fitting model

Jinxiang Shen; Liao Yang; Xi Chen

doi:10.1117/12.901765

8 December 2011 Multispectral remote sensing image cross simulation based on nonlinear spectral fitting model

Jinxiang Shen, Liao Yang, Xi Chen

Proceedings Volume 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis; 800212 (2011) https://doi.org/10.1117/12.901765
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

Abstract

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.

Citation Download Citation

Jinxiang Shen, Liao Yang, and Xi Chen "Multispectral remote sensing image cross simulation based on nonlinear spectral fitting model", Proc. SPIE 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis, 800212 (8 December 2011); https://doi.org/10.1117/12.901765

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