A 2D Wigner Distribution-based multisize windows technique for image fusion

2014

J. Appl. Remote Sens

A new algorithm has been proposed to fuse high-resolution panchromatic image with a low-resolution multispectral image of World View-2, based on pseudo-Wigner distribution (PWD). Spatial-frequency method, particularly PWD, provides pixel-wise analysis, shift invariant as well as characterization of local spectral properties of nonstationary image, which is indispensable for image fusion. The input images are re-sampled using nearest neighbor (NN), bicubic spline (BCS), and cubic convolution (CC). The comparison of performance of the proposed method and the discrete wavelet transform (DWT) has been evaluated using root mean square error, peak signal-to-noise ratio, correlation coefficient index. It has been found that PWD fusion technique outperforms DWT fusion technique using average as well as maximum selection rule, both quantitatively and qualitatively. All three resampling methods-the NN, the BCS, and the CC-do not have any significant effect on the final visual appearance of the fused images.Rajput, Ghosh, and Kumar: Multisensor fusion of satellite images for urban information extraction.

Section: Fusion Methods Based On Pwdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multisensor fusion of satellite images for urban information extraction using pseudo-Wigner distribution

Rajput

Ghosh

2014

J. Appl. Remote Sens

Satellite Information Classification and Interpretation

“…Thus, the goal of pan-sharpening is to produce pan-sharpened images with the highest spectral fidelity possible, as the importance of such images in various applications, ranging from land use/land cover classification to road extraction. Therefore, preserving the spectral information of the original MS images in the pansharpened images is of great importance [31][32][33]. Therefore, an attempt to utilize the concept of pseudo-Wigner distribution (PWD) for the pan-sharpening of highresolution PAN image with a low-resolution MS image has been introduced.…”

Section: Pseudo-wigner Distribution (Pwd) Techniquementioning

confidence: 99%

Pan-sharpening Using Spatial-frequency Method

Kumar¹

2019

Over the years, researchers have formulated various techniques for pan sharpening that attempt to minimize the spectral distortion, i.e., retain the maximum spectral fidelity of the MS images. On the other hand, if the use of the PAN-sharpened image is just to produce maps for better visual interpretation, then the spectral distortion is not of much concern, as the goal is to produce images with high contrast. To solve the color distortion problem, methods based on spatial frequency domain have been introduced and have demonstrated superior performance in terms of producing high spectral fidelity pan-sharpened images over spatial-scale methods.

“…Non subsampled contour let transform (NSCT) was proposed to get around the constraints of DWT and SWT. Nevertheless, blocking artefacts and complex computation are produced by NSCT [3,[5][6] To our knowledge, panchromatic or multispectral images have not been sharpened using PWD. PWD is being used in the study to sharpen PAN and MS images.…”

Section: Introductionmentioning

confidence: 99%

Comparison between Multi-Scale Decomposition and Spatial Frequency based Methods for the Integration of Panchromatic and Multispectral Pictures

Rajput

2023

Preprint

A technique for merging a high-resolution PAN image with a low-resolution multispectral image from LISS-III and World View-2 is suggested in this paper. The spatial-frequency technique, in particular the pseudo-Wigner distribution (PWD), offers pixel-by-pixel analysis, shift invariance, and characterization of local spectral features of nonstationary images. The suggested approach is compared with multi-scale decomposition techniques like NSCT and SWT. Using root mean square error (RMSE), peak signal to noise ratio (PSNR), correlation coefficient (CC), relative dimensionless global error (ERGAS), and RASE, the performance of the suggested technique, the non-subsampled contour-let transform, and the stationary wavelet transform (SWT), is compared. Based on thorough tests using the LISS-III and World View-2 datasets, it is observed that the suggested method is statistically and visually more effective than traditional picture fusion algorithms. The suggested approach enhances spatial resolution while retaining spectral data.