Water extraction based on self-fusion of ETM+ remote sensing data and normalized ratio index

Wen-bo Li; Qiu-wen Zhang

doi:10.1117/12.713010

28 October 2006 Water extraction based on self-fusion of ETM+ remote sensing data and normalized ratio index

Wen-bo Li, Qiu-wen Zhang

Proceedings Volume 6419, Geoinformatics 2006: Remotely Sensed Data and Information; 641911 (2006) https://doi.org/10.1117/12.713010
Event: Geoinformatics 2006: GNSS and Integrated Geospatial Applications, 2006, Wuhan, China

Abstract

The water body information is accurately extracted from remotely sensed images with the method of normalized ratio index, and the water information is greatly enhanced through restricting the brightness of backgrounds. What's more, there is no noise formed by shadows in results. However, the spatial resolution of most images used for water extraction is usually not high enough to identify water body clearly. Fusion of remotely sensed images with different spatial resolution can solve this problem. Four data fusion methods such as Modified Brovey Transform (MBT), Multiplication Transform (MLT), Smoothing Filter-based Intensity Modulation Transform (SFIMT) and High Pass Filter Transform (HPTF) have been applied to merge ETM+ panchromatic band with multi-spectral band data. Normalized ration method is adopted to extract water body information from both original and merged images. The effect of data fusion and extracting result are validated and evaluated by qualitative analysis and quantitative statistical calculation. SFIMT model enjoys the best maintenance of spectral quality from the multi-spectral bands. On the other hand, MLT model has the highest spatial frequency information gain. In the data fusion algorithms, SFIMT is the optimization data fusion method appropriate to the normalized ration water extracting model.

Citation Download Citation

Wen-bo Li and Qiu-wen Zhang "Water extraction based on self-fusion of ETM+ remote sensing data and normalized ratio index", Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 641911 (28 October 2006); https://doi.org/10.1117/12.713010

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