A Global Quality Measurement of Pan-Sharpened Multispectral Imagery

In remote sensing, pansharpening refers to the technique that combines the complementary spectral and spatial resolution characteristics of a multispectral image and a panchromatic image, with the objective to generate a high-resolution color image. This paper presents a new pansharpening method based on the minimization of a variant of total variation. We consider the fusion problem as the colorization of each pixel in the panchromatic image. A new term concerning the gradient of the panchromatic image is introduced in the functional of total variation so as to preserve edges. Experimental results on IKONOS satellite images demonstrate the effectiveness of the proposed method.

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“…SAM [16], ERGAS [5], Q4 [16] cannot be used. Therefore we adopt the QNR metric proposed in [17] to examine the quality of the fused image.…”

Section: Comparison To Other Pansharpening Methodsmentioning

confidence: 99%

Pansharpening using total variation regularization

Condat

Chanussot

et al. 2012

2012 IEEE International Geoscience and Remote Sensing Symposium

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“…The peak signal-to-noise ratio (PSNR) was not evaluated because reflectance ranges from 0 to 1. As RMSE is not the most suitable metric for remote sensing applications when spectral loss is involved , some popular metrics are also used in Tables 4 and 5, including spectral angle mapper (SAM), relative average spectral error (RASE) [24], Erreur Relative Globale Adimensionnelle de Synthese (ERGAS) [25], and Q4 [26]. Note that the ideal result is 0 for SAM, RASE, and ERGAS, while it is 1 for Q4.…”

Section: Comparative Summary: Prediction Performancementioning

confidence: 99%

Spatiotemporal Fusion of Remote Sensing Images with Structural Sparsity and Semi-Coupled Dictionary Learning

et al. 2016

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Fusion of remote sensing images with different spatial and temporal resolutions is highly needed by diverse earth observation applications. A small number of spatiotemporal fusion methods using sparse representation appear to be more promising than traditional linear mixture methods in reflecting abruptly changing terrestrial content. However, one of the main difficulties is that the results of sparse representation have reduced expressional accuracy; this is due in part to insufficient prior knowledge. For remote sensing images, the cluster and joint structural sparsity of the sparse coefficients could be employed as a priori knowledge. In this paper, a new optimization model is constructed with the semi-coupled dictionary learning and structural sparsity to predict the unknown high-resolution image from known images. Specifically, the intra-block correlation and cluster-structured sparsity are considered for single-channel reconstruction, and the inter-band similarity of joint-structured sparsity is considered for multichannel reconstruction, and both are implemented with block sparse Bayesian learning. The detailed optimization steps are given iteratively. In the experimental procedure, the red, green, and near-infrared bands of Landsat-7 and Moderate Resolution Imaging Spectrometer (MODIS) satellites are put to fusion with root mean square errors to check the prediction accuracy. It can be concluded from the experiment that the proposed methods can produce higher quality than state-of-the-art methods.

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“…The evaluation of the employed fusion technique is also an important step in the fusion process (Alparone, 2004, Hnatushenko, 2015b. Various quality metrics have been used in the literature to study, compare and assess implementation of the fusion technique (Ehlers, 2008, Karathanassi, 2007, Wald, 2000, Zhang, 2008.…”

Section: Visual and Quantitative Evalutionsmentioning

confidence: 99%

Remote Sensing Image Fusion Using Ica and Optimized Wavelet Transform

Hnatushenko

Vasyliev²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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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.

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A Global Quality Measurement of Pan-Sharpened Multispectral Imagery

Cited by 527 publications

References 12 publications

Pansharpening using total variation regularization

Pansharpening using total variation regularization

Spatiotemporal Fusion of Remote Sensing Images with Structural Sparsity and Semi-Coupled Dictionary Learning

Remote Sensing Image Fusion Using Ica and Optimized Wavelet Transform

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