Wavelet based image fusion techniques — An introduction, review and comparison

Amolins, Krista; Zhang, Yun; Dare, Peter

doi:10.1016/j.isprsjprs.2007.05.009

Cited by 469 publications

(232 citation statements)

References 13 publications

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“…Previous studies [10] [19] have shown that using IHS transform and multi-scale analysis jointly to fuse MSI images and pan image is better than using IHS …”

Section: Bidimensional Empirical Mode Decompositionmentioning

confidence: 99%

Remote Sensing Image Fusion Using Bidimensional Empirical Mode Decomposition and the Least Squares Theory

Huang¹,

Yang²,

Li³

et al. 2017

JCC

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Due to the data acquired by most optical earth observation satellite such as IKONOS, QuickBird-2 and GF-1 consist of a panchromatic image with high spatial resolution and multiple multispectral images with low spatial resolution. Many image fusion techniques have been developed to produce high resolution multispectral image. Considering panchromatic image and multispectral images contain the same spatial information with different accuracy, using the least square theory could estimate optimal spatial information. Compared with previous spatial details injection mode, this mode is more accurate and robust. In this paper, an image fusion method using Bidimensional Empirical Mode Decomposition (BEMD) and the least square theory is proposed to merge multispectral images and panchromatic image. After multi-spectral images were transformed from RGB space into IHS space, next I component and Panchromatic are decomposed by BEMD, then using the least squares theory to evaluate optimal spatial information and inject spatial information, finally completing fusion through inverse BEMD and inverse intensity-hue-saturation transform. Two data sets are used to evaluate the proposed fusion method, GF-1 images and QuickBird-2 images. The fusion images were evaluated visually and statistically. The evaluation results show the method proposed in this paper achieves the best performance compared with the conventional method.

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“…Previous studies [10] [19] have shown that using IHS transform and multi-scale analysis jointly to fuse MSI images and pan image is better than using IHS …”

Section: Bidimensional Empirical Mode Decompositionmentioning

confidence: 99%

Remote Sensing Image Fusion Using Bidimensional Empirical Mode Decomposition and the Least Squares Theory

Huang¹,

Yang²,

Li³

et al. 2017

JCC

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“…Mathematically, wavelets are described using two types of functions: the wavelet function (the mother function denoted with ψ(t)) and the scaling function (the father function denoted with φ(t)) [16]. These functions have to satisfy the following conditions:…”

Section: Discrete Wavelet Transformsmentioning

confidence: 99%

Time Series Classification with Discrete Wavelet Transformed Data: Insights from an Empirical Study

Bissyandé

Klein

et al. 2016

International Conferences on Software Engineering and Knowledge Engineering

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Time series mining has become essential for extracting knowledge from the abundant data that flows out from many application domains. To overcome storage and processing challenges in time series mining, compression techniques are being used. In this paper, we investigate the loss/gain of performance of time series classification approaches when fed with lossy-compressed data. This empirical study is essential for reassuring practitioners, but also for providing more insights on how compression techniques can even be effective in reducing noise in time series data. From a knowledge engineering perspective, we show that time series may be compressed by 90% using discrete wavelet transforms and still achieve remarkable classification accuracy, and that residual details left by popular wavelet compression techniques can sometimes even help achieve higher classification accuracy than the raw time series data, as they better capture essential local features.

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“…In addition, we perform fusion in the wavelet domain [3]. To this end, each sensor image is transformed by the shiftinvariant 2d wavelet transform, using five decomposition levels and the db2 mother wavelet, thus obtaining details coefficients at each of the four scales and the residual image containing the approximation coefficients.…”

Section: Fusion Rule Definitionmentioning

confidence: 99%

Fusion of multi-sensory NDT data for reliable detection of surface cracks: Signal-level vs. decision-level

Heideklang¹,

Shokouhi²

2016

AIP Conference Proceedings

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Abstract. We present and compare two different approaches for NDT multi-sensor data fusion at signal (low) and decision (high) levels. Signal-level fusion is achieved by applying simple algebraic rules to strategically post-processed images. This is done in the original domain or in the domain of a suitable signal transform. The importance of signal normalization for low-level fusion applications is emphasized in regard to heterogeneous NDT data sets. For fusion at decision level, we develop a procedure based on assembling joint kernel density estimation (KDE). The procedure involves calculating KDEs for individual sensor detections and aggregating them by applying certain combination rules. The underlying idea is that if the detections from more than one sensor fall spatially close to one another, they are likely to result from the presence of a defect. On the other hand, single-senor detections are more likely to be structural noise or false alarm indications. To this end, we design the KDE combination rules such that it prevents single-sensor domination and allows data-driven scaling to account for the influence of individual sensors. We apply both fusion rules to a three-sensor dataset consisting in ET, MFL/GMR and TT data collected on a specimen with built-in surface discontinuities. The performance of the fusion rules in defect detection is quantitatively evaluated and compared against those of the individual sensors. Both classes of data fusion rules result in a fused image of fewer false alarms and thus improved defect detection. Finally, we discuss the advantages and disadvantages of low-level and high-level NDT data fusion with reference to our experimental results.

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Wavelet based image fusion techniques — An introduction, review and comparison

Cited by 469 publications

References 13 publications

Remote Sensing Image Fusion Using Bidimensional Empirical Mode Decomposition and the Least Squares Theory

Remote Sensing Image Fusion Using Bidimensional Empirical Mode Decomposition and the Least Squares Theory

Time Series Classification with Discrete Wavelet Transformed Data: Insights from an Empirical Study

Fusion of multi-sensory NDT data for reliable detection of surface cracks: Signal-level vs. decision-level

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