Abstract
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.
Highlights
Images of modern remote-sensing systems allow us to solve various problems such as carrying out operational monitoring of land resources, city building, monitoring state of the environment and influence of anthropogenic factors, detecting contaminated territories, unauthorized buildings, estimating the state of forest plantation and other (Pandit, 2015, Hnatushenko, 2016)
After image fusion by methods discussed above, images that even visually are more precise than primary multispectral image were obtained, but with significant color distortion (Figure 6 a-f)
To minimize the spectral distortion we propose a remote sensing image fusion method which combines the Independent Component Analysis (ICA) and optimized wavelet transform
Summary
Images of modern remote-sensing systems allow us to solve various problems such as carrying out operational monitoring of land resources, city building, monitoring state of the environment and influence of anthropogenic factors, detecting contaminated territories, unauthorized buildings, estimating the state of forest plantation and other (Pandit, 2015, Hnatushenko, 2016) These images may be captured from different sensors, acquired at different times, or having different spatial and spectral characteristics. The modification to the IHS method, called the IHS-SA, proposed the incorporation of weighted coefficients on the green and blue bands so as to reduce the difference between the intensity and the panchromatic bands (Tu et al, 2005) These methods allow us to take into account only spectral components of primary grayscale image (Vivone, 2015). There is a necessity in a new technology, which will increase spatial resolution of satellite images considering physical mechanisms of fixing species of information and conducting further research of fusion efficiency with obtaining quantitative estimates (criteria) of information content (quality) of synthesized images
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