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Proceedings Paper

Multispectral remote sensing image cross simulation based on nonlinear spectral fitting model
Author(s): Jinxiang Shen; Liao Yang; Xi Chen
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Paper 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.

Paper Details

Date Published: 8 December 2011
PDF: 8 pages
Proc. SPIE 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis, 800212 (8 December 2011); doi: 10.1117/12.901765
Show Author Affiliations
Jinxiang Shen, Xinjiang Ecology and Geography Institute (China)
Graduate Univ. of CAS (China)
Liao Yang, Xinjiang Ecology and Geography Institute (China)
Xi Chen, Xinjiang Ecology and Geography Institute (China)

Published in SPIE Proceedings Vol. 8002:
MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis
Faxiong Zhang; Faxiong Zhang, Editor(s)

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