The combination of a multispectral (MS) image and a panchromatic (PAN) image, the so-called pansharpening, allows to produce very appealing images that are useful both for visual interpretation and for feature extraction. The state-of-the-art multiresolution analysis pansharpening algorithms are based on the extraction of spatial details from the PAN image through image filters matched with the MS sensors' modulation transfer function. However, this knowledge is often poor due to measurement inaccuracies and/or its aging. Thus, deconvolution algorithms have been proposed to overcome this limitation. In this paper, we propose a multiband filter estimation (FE) approach to improve the solutions in the literature. The main idea in this paper is to exploit a preliminary pansharpened image to estimate the spatial filter used for detail extraction associated with each spectral band. We demonstrate that the proposed method outperforms the state-of-the-art FE approaches by employing data sets acquired by the IKONOS, the Quickbird, and the WorldView-3 sensors.

Pansharpening Based on Deconvolution for Multiband Filter Estimation

Vivone, Gemine;Addesso, Paolo;Restaino, Rocco;Dalla Mura, Mauro;
2019-01-01

Abstract

The combination of a multispectral (MS) image and a panchromatic (PAN) image, the so-called pansharpening, allows to produce very appealing images that are useful both for visual interpretation and for feature extraction. The state-of-the-art multiresolution analysis pansharpening algorithms are based on the extraction of spatial details from the PAN image through image filters matched with the MS sensors' modulation transfer function. However, this knowledge is often poor due to measurement inaccuracies and/or its aging. Thus, deconvolution algorithms have been proposed to overcome this limitation. In this paper, we propose a multiband filter estimation (FE) approach to improve the solutions in the literature. The main idea in this paper is to exploit a preliminary pansharpened image to estimate the spatial filter used for detail extraction associated with each spectral band. We demonstrate that the proposed method outperforms the state-of-the-art FE approaches by employing data sets acquired by the IKONOS, the Quickbird, and the WorldView-3 sensors.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4724189
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