Evaluation of Satellite Image Fusion Using Wavelet Transform

Journal of Applied Remote Sensing, 2014

A generalized wavelet domain image fusion method which imposes weights on each of the wavelet coefficients for improving the conventional wavelet domain approach is presented. The weights are controlled in the least-squares sense for enhancing the details while suppressing excessive high frequency components. In experiments with IKONOS and QuickBird satellite data, we demonstrated that the proposed method shows a comparable or better performance than conventional methods in terms of various objective quality metrics.

IRJET, 2020

In this project we applied implementation and evaluation of wavelet transformation based image fusion. Discrete wavelet transformation is presented concisely to facility the understanding of the wavelet based image fusion method. To best retain the quality of the input images, the strategy that minimizes the necessary resampling operations to limit potential image quality deterioration. In the proposed fusion approach, the wavelet coefficients for the fused images are selected based on the suggested maximum magnitude criterion. To evaluate the outcome images, other popular fusion methods including principal component transformation, Brovey and IHS transformation , DCT approaches are applied to the same images and the results are compared to the ones from the wavelet based approach. Fusion results are evaluated both visually and numerically. A quality matrix is calculated based on the correlation coefficients between the fused image and the original image. It is shown that this quality measure can indicate the information content of the fused image comparing to the input panchromatic and multispectral images. Our results clearly suggest that the wavelet based fusion can yield superior properties to other existing methods in terms of both spatial and spectral resolutions, and their visual appearance.

The development of new imaging methods in various fields arise the need of meaningful combination of all available image datasets. Image fusion is the process of integrating complementary information from multiple image sensor data to create a fused image output. The new image generated should contain a more accurate description of the scene than any of the individual source images and is more suitable for human visual and machine perception or further image processing and analysis tasks. This technique is used in satellite remote sensor images to fuse high resolution panchromatic (PAN) image with the low resolution multispectral (MS) image to form a single high resolution multispectral image. Among the existing fusion techniques, wavelet based methods have proved to produce improved results. This paper proposes a novel method to fuse PAN image and MS image by integrating Contrast based Discrete Wavelet Packet Transform (DWPT) and Intensity Hue Saturation (IHS) technique. Firstly, the advantages of using DWPT over DWT are given in brief. Then the proposed algorithm is explained. Finally, its performance is evaluated using various quality assessment metrics which shows that the proposed method is superior to the other existing methods.

Proceedings of The Second International Conference on Forensic Computer Science, 2007

Image fusion consists of an advanced set of digital image processing techniques, which are used to combine images of different sensors. The main objective is to obtain a synthetic image that gathers the best qualities of each original image. This paper presents a new image fusion method based on selection of coefficients that takes into account the image fusion from sensors of either different or same satellites. The method is based on wavelet transform and multiresolution analysis. It separates fusion and resampling in different stages, allowing the use of different filters or bases of wavelet transform, in each one of them, in order to get better results. The advantage of the image fusion is to join the best spatial resolution of an image with spectral data of other image in a synthetic one. The results obtained with this method allow us to infer that the synthetic image produced has better spectral and spatial quality than IHS.

This paper proposes a new fusion method for multiespectral (MULTI) and panchromatic (PAN) images that uses a highly anisotropic and redundant representation of images. This methodology join the simplicity of the Wavelet transform, calculated using the a trous algorithm, with the benefits of multidirectional transforms like Contourlet Transform. That has permitted an adequate extraction of information from the source images, in order to obtain fused images with high spatial and spectral quality simultaneously. The new method has been implemented through a directional low pass filter bank with low computational complexity. The source images correspond to those captured by the IKONOS satellite (panchromatic and multispectral). The influence of the filter bank parameters in the global quality of the fused images has been investigated. The results obtained indicate that the proposed methodology provides an objective control of the spatial and spectral quality trade-off of the fused images by the determination of an appropriate set of filter bank parameters.

Int J Remote Sens, 2006

The preservation of spectral characteristics and the retention of high spatial resolution are two key issues in image fusion. Different applications may require different balances between the two. However, the limitation is that only one fused result is possible for most of the commonly used fusion methods; users have no control over how much spatial detail or spectral information should be retained. Both quantitative and qualitative criteria, including mean, standard deviation, entropy, average gradient, spectral distortion, correlation coefficient and visual assessment, were used to evaluate the performance of the proposed fuser. A SPOT (Satellite Pour l'Observation de la Terre) panchromatic image and Landsat Thematic Mapper (TM) images were employed in the evaluation. The results show that the proposed method is slightly better than, or comparable to, commonly used image fusion methods, including Intensity-Hue-Saturation (IHS), principal component analysis (PCA), and two other wavelet-based fusion methods with replacement and with selection strategies, in terms of spectral characteristic preservation and high spatial resolution retention. Moreover, the proposed method can achieve a wide range of balances between spectral characteristic preservation and high spatial resolution retention, and thus is applicable in different applications. low spatial resolution images using the IHS system and then substituting the *Corresponding author.

Extracting more information from multi-source images is an attractive thing in remotely sensed image processing, which is recently called image fusion. In the paper, the image fusion algorithm based on wavelet transform is proposed to improve the geometric resolution of the images, in which two images to be processed are firstly decomposed into sub images with different frequency, and then the information fusion is performed using these images under certain criterion, and finally these sub-images are reconstructed into the result image with plentiful information.

Optical Engineering, 2005

Of the many image fusion methods, the discrete wavelet transform (DWT) and various pyramids (e.g., the Laplacian pyramid) are among the most common and effective. For quantitative evaluation of the quality of fused imagery, the root mean square error (RMSE) is the most reasonable measure of quality if a "ground truth" image is available; otherwise, the entropy, spatial frequency, or image quality index (0) can be calculated and evaluated. Here, an advanced discrete wavelet transform (aDWT) method that incorporates principal component analysis (PCA) and morphological processing into a regular DWT fusion algorithm is presented. Specifically, a principle vector is derived from two ' approximation co-input images and then applied to two of the images efficients at the highest DWT transform scale. For the detail coefficients at each transform scale, the larger absolute values are chosen and subjected to a neighborhood morphological processing procedure that serves to verify the selected pixels by using a "filling" and "cleaning" operation. Furthermore, the aDWT has two adjustable parameters-the number of DWT decomposition levels and the length of the selected wavelet that cleterminately affect the fusion result. An iterative fusion process that was optimized with the established metric-IQI-is then implemented. Experimental results tested on four types of inhomogeneous imagery show that the iterative aDWT achieves the best fusion compared to the pyramid or the DWT methods judged on both the IQI metric and visual inspection.

Image fusion is the process of combining multiple images into a single image without distortion or loss of information. The techniques related to image fusion are broadly classified as spatial and transform domain methods. In which, the transform domain based wavelet fusion techniques are widely used in different domains like medical, space and military for the fusion of multimodality or multi-focus images. In this paper, an overview of different wavelet transform based methods and its applications for image fusion are discussed and analysed.

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017

Multimodal remote sensing approach is based on merging different data in different portions of electromagnetic radiation that improves the accuracy in satellite image processing and interpretations. Remote Sensing Visible and thermal infrared bands independently contain valuable spatial and spectral information. Visible bands make enough information spatially and thermal makes more different radiometric and spectral information than visible. However low spatial resolution is the most important limitation in thermal infrared bands. Using satellite image fusion, it is possible to merge them as a single thermal image that contains high spectral and spatial information at the same time. The aim of this study is a performance assessment of thermal and visible image fusion quantitatively and qualitatively with wavelet transform and different filters. In this research, wavelet algorithm (Haar) and different decomposition filters (mean.linear,ma,min and rand) for thermal and panchromatic bands of Landast8 Satellite were applied as shortwave and longwave fusion method. Finally, quality assessment has been done with quantitative and qualitative approaches. Quantitative parameters such as Entropy, Standard Deviation, Cross Correlation, Q Factor and Mutual Information were used. For thermal and visible image fusion accuracy assessment, all parameters (quantitative and qualitative) must be analysed with respect to each other. Among all relevant statistical factors, correlation has the most meaningful result and similarity to the qualitative assessment. Results showed that mean and linear filters make better fused images against the other filters in Haar algorithm. Linear and mean filters have same performance and there is not any difference between their qualitative and quantitative results.