DCT Based Pansharpening of Satellite Images Using Adaptive Linear Model
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DCT Based Pansharpening of Satellite Images Using Adaptive Linear Model Madhuri Khambete & Aditi Divekar
Received: 21 February 2013 / Accepted: 6 May 2013 / Published online: 8 October 2013 # Indian Society of Remote Sensing 2013
Abstract Remote sensing offers a wide variety of image data with different characteristics in terms of spatial and spectral resolutions. For optical sensor systems, imaging systems have a trade-off between high spatial and high spectral resolution, and no single system offers both. Hence, in the remote sensing application, an image with ‘greater quality’ often means higher spatial and higher spectral resolution. It is, therefore, necessary and very useful to merge images with higher spectral information and higher spatial information. Pansharpening combines spatial information from the high-resolution panchromatic image and color information from multispectral bands to create a high-resolution color image. Here we propose Discrete Cosine Transform (DCT) based pansharpening algorithm using Adaptive Linear model which preserves spectral information from Multispectral image and retains spatial resolution of Panchromatic image. Keywords Pansharpening . Panchromatic . Multispectral . Spatial resolution . Spectral resolution
Introduction The satellites are commonly capable of producing two types of images: a multispectral image and a panchromatic image. Generally, the multispectral sensor provides M. Khambete : A. Divekar (*) Cummins College of Engineering for Women, Pune, India e-mail: [email protected] M. Khambete e-mail: [email protected]
multi-band color images with low spatial resolution. The panchromatic sensor provides grayscale images with high spatial resolution. A large variety of applications in remote sensing require images with both high spatial and high spectral resolution. Pansharpening combines spatial information from the high-resolution panchromatic image and color information from multispectral bands to create a highresolution color image. Thus the fusion of the multispectral image (Fig. 1) and panchromatic image (Fig. 2) provides a Pansharpened image (Fig. 3) having clear geometric features of the panchromatic image and the color information of the multispectral image. Pansharpening techniques are classified into five categories (Amro et al. 2011). These categories are, 1) Component Substitution - IHS based pansharpening (Choi et al. 2006; Merkurjev et al. 2008; AlWassai et al. 2011; Amro et al. 2011) where Multispectral image is transferred to Intensity, Hue and Saturation (IHS) representation and Intensity band from a multispectral image is replaced by Panchromatic image and again IHS representation is converted back to multispectral image. PCA based pansharpening (Smith 2002; Merkurjev et al. 2008; Amro et al. 2011) where the first principal component which has the highest variance, is replaced by the Panchromatic image. 2) Multiresolution family - This includes wavelet and contourlet methods where Multispectral and Panchromatic images are decompos
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