Abstract
Pan-sharpening aims to sharpen a low spatial resolution multispectral (MS) image by combining the spatial detail information extracted from a panchromatic (PAN) image. An effective pan-sharpening method should produce a high spatial resolution MS image while preserving more spectral information. Unlike traditional intensity-hue-saturation (IHS)- and principal component analysis (PCA)-based multiscale transform methods, a novel pan-sharpening framework based on the matting model (MM) and multiscale transform is presented in this paper. First, we use the intensity component (I) of the MS image as the alpha channel to generate the spectral foreground and background. Then, an appropriate multiscale transform is utilized to fuse the PAN image and the upsampled I component to obtain the fused high-resolution gray image. In the fusion, two preeminent fusion rules are proposed to fuse the low- and high-frequency coefficients in the transform domain. Finally, the high-resolution sharpened MS image is obtained by linearly compositing the fused gray image with the upsampled foreground and background images. The proposed framework is the first work in the pan-sharpening field. A large number of experiments were tested on various satellite datasets; the subjective visual and objective evaluation results indicate that the proposed method performs better than the IHS- and PCA-based frameworks, as well as other state-of-the-art pan-sharpening methods both in terms of spatial quality and spectral maintenance.
Highlights
High-resolution multichannel satellite images with both high spatial resolution and spectral diversity are required in many image-processing applications, such as change detection, land-cover segmentation, and road extraction
Experiments on WorldView-2, QuickBird, and IKONOS satellite images demonstrate that the proposed method outperforms several state-of-the-art pan-sharpening methods in terms of both subjective and objective measures
Addressing existing problems in the pan-sharpening area, this paper presented a new pan-sharpening framework by exploiting the matting model and multiscale transform
Summary
High-resolution multichannel satellite images with both high spatial resolution and spectral diversity are required in many image-processing applications, such as change detection, land-cover segmentation, and road extraction. Most remote sensing satellites (e.g., WorldView-2, QuickBird, IKONOS) simultaneously provide a panchromatic (PAN) image with high spatial but low spectral resolution, and a multispectral (MS) image with complementary properties [2,3]. A pan-sharpening algorithm is applied to merge the MS and PAN images to have a high spatial resolution MS image, preserving the spectral information of MS. When the spatial details are obtained from a multispectral/hyperspectral sequence, the pan- sharpening algorithm is called hyper-sharpening [4]. In the last two decades, many pan-sharpening methods have been proposed to fuse MS and PAN images. A detailed comparison of the characteristics and performance of classical and famous pan-sharpening methods was presented in [5].
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