A wavelet-based image fusion tutorial

Pajares, Gonzalo; Cruz, J.M. de la

doi:10.1016/j.patcog.2004.03.010

Cited by 1,129 publications

(234 citation statements)

References 34 publications

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“…Merged coefficients using inverse wavelet transformation make a final fused image (Pajares & de la Cruz, 2004). The inputs for image fusion using wavelet transforms have same or different spatial resolution.…”

Section: )mentioning

confidence: 99%

“…Figure 2 shows decomposition of different information with horizontal, vertical and diagonal lines for each sub images. Also, dark rectangles in sub images shows the determined coefficients for each decomposed sub images (Pajares & de la Cruz, 2004). The complete procedure of image decomposition explained at (Transactions, Pattern, & Vol, 1989).…”

Section: )mentioning

confidence: 99%

“…Decomposition process in first level produces four sub images like IHH(x,y) that contains spectral information of original images. Another sub images like IHH(x,y),IHL (x,y) and ILH (x,y) contain spatial details (Pajares & de la Cruz, 2004). Wavelet based image fusion based on Haar algorithm is the reference method for MR approach.…”

Section: )mentioning

confidence: 99%

“…For more information about the nature of satellite images see (Schowengerdt Robert A., 2009). Different portions of EM spectrum (EMS) enable users to see different phenomenon with different states (Pajares & de la Cruz, 2004). Each portion of EMS has unique characteristics and shows special properties of phenomenon more different than other portions.…”

Section: Introductionmentioning

confidence: 99%

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Thermal and Visible Satellite Image Fusion Using Wavelet in Remote Sensing and Satellite Image Processing

Ahrari

Kiavarz

Hasanlou

et al. 2017

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

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ABSTRACT: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.

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Section: )mentioning

confidence: 99%

Section: )mentioning

confidence: 99%

Section: )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Thermal and Visible Satellite Image Fusion Using Wavelet in Remote Sensing and Satellite Image Processing

Ahrari

Kiavarz

Hasanlou

et al. 2017

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

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“…In the paper multistage image fusion scheme was used to generate partially and final aggregated image. The fusion performed using simple method based on consecutive levels of Laplace (LAP) [8] and more sophisticated solution using discrete wavelet transformations (DWT) [6,5]. …”

Section: Fusion Of Partial Imagesmentioning

confidence: 99%

The method of Compression of High-Dynamic-Range Infrared Images using image aggregation algorithms

Fidali¹,

Jamrozik²

2016

Proceedings of the 2016 International Conference on Quantitative InfraRed Thermography

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In the article a method of compression of High Dynamic Range of Infrared images and its details enhancement was proposed. The method is based on aggregation of partial images with different ranges of pixel values generated on the basis of weighting function. Aggregation of images could be performed according to proposed scheme with use of the optimal algorithm selected by user. Preliminary results shown that the method can reveal previously hidden details in the original image.

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References

2010

Computer Processing of Remotely‐Sensed Images

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A wavelet-based image fusion tutorial

Cited by 1,129 publications

References 34 publications

Thermal and Visible Satellite Image Fusion Using Wavelet in Remote Sensing and Satellite Image Processing

Thermal and Visible Satellite Image Fusion Using Wavelet in Remote Sensing and Satellite Image Processing

The method of Compression of High-Dynamic-Range Infrared Images using image aggregation algorithms

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