Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 653-659, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
21 Jun 2016
V. V. Hnatushenko1,2 and V. V. Vasyliev2 1Dept. of Automated Data Processing Systems, Faculty of Physics, Electronics and Computer Systems, 49010 Oles Honchar Dnipropetrovsk National University, Ukraine
2EOS Data Analytics Ukraine, LLC, Heroiev Stalingrada Str. Dnipropetrovsk, 49055, Ukraine
Keywords: remote sensing, data fusion, multispectral image, ICA, wavelet transform Abstract. In remote-sensing image processing, fusion (pan-sharpening) is a process of merging high-resolution panchromatic and lower resolution multispectral (MS) imagery to create a single high-resolution color image. Many methods exist to produce data fusion results with the best possible spatial and spectral characteristics, and a number have been commercially implemented. However, the pan-sharpening image produced by these methods gets the high color distortion of spectral information. In this paper, to minimize the spectral distortion we propose a remote sensing image fusion method which combines the Independent Component Analysis (ICA) and optimization wavelet transform. The proposed method is based on selection of multiscale components obtained after the ICA of images on the base of their wavelet decomposition and formation of linear forms detailing coefficients of the wavelet decomposition of images brightness distributions by spectral channels with iteratively adjusted weights. These coefficients are determined as a result of solving an optimization problem for the criterion of maximization of information entropy of the synthesized images formed by means of wavelet reconstruction. Further, reconstruction of the images of spectral channels is done by the reverse wavelet transform and formation of the resulting image by superposition of the obtained images. To verify the validity, the new proposed method is compared with several techniques using WorldView-2 satellite data in subjective and objective aspects. In experiments we demonstrated that our scheme provides good spectral quality and efficiency. Spectral and spatial quality metrics in terms of RASE, RMSE, CC, ERGAS and SSIM are used in our experiments. These synthesized MS images differ by showing a better contrast and clarity on the boundaries of the "object of interest - the background". The results show that the proposed approach performs better than some compared methods according to the performance metrics.
Conference paper (PDF, 1728 KB)

Citation: Hnatushenko, V. V. and Vasyliev, V. V.: REMOTE SENSING IMAGE FUSION USING ICA AND OPTIMIZED WAVELET TRANSFORM, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 653-659,, 2016.

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