Quality Assessment of Image Fusion Methods in Transform Domain

Choi, Yoonsuk; Sharifahmadian, Ershad; Latifi, Shahram

doi:10.5121/ijit.2014.3102

Cited by 15 publications

(8 citation statements)

References 22 publications

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“…Because the multiresolution images may have different sizes, many of the image fusion performance evaluation indices, e.g., mutual information [17], correlation information entropy [18], [19], and edge strength [20], and quality assessment of image fusion methods in transform domain [21], etc., fail to work. In view of this, the root mean square error (RMSE) [1] between the reference image and the fused image and the source image with the highest resolution will be computed.…”

Section: Resultsmentioning

confidence: 99%

“…(iv) Using (21) and (22) to reconstruct images from vectorŝ x(KJ), we haveX. It is the image that has fused all the source images taken by sensors j(1 ≤ j ≤ J) during time k = 1, 2, · · · , K. The illustration of the image fusion sequence and the image fusion process are shown in Fig.1 and Fig.2 …”

Section: Multisensor Multiresolution Image Fusion Algorithm Based On mentioning

confidence: 99%

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The fusion of multisensor images with multi-resolutions

Yan

Xiao

Xia

et al. 2014

Proceedings of the 33rd Chinese Control Conference

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Images taken by different sensors at different time instant with different resolutions are formulated by state space models, and are fused by use of Multiscale Kalman Filter (MKF). The effectiveness of the presented algorithm is shown by comparing it with the wavelet based method through experiments, where four performance measures are used. The performance evaluation indices are the root mean square errors (RMSE), the information entropy (Entropy), the space frequency (SF) and the space visibility (SV). Theretical analysis and experimental results show the effectiveness of the presented algorithm.

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Section: Resultsmentioning

confidence: 99%

Section: Multisensor Multiresolution Image Fusion Algorithm Based On mentioning

confidence: 99%

The fusion of multisensor images with multi-resolutions

Yan

Xiao

Xia

et al. 2014

Proceedings of the 33rd Chinese Control Conference

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“…Topographic earth observation satellites, such as IKO-NOS, QuickBird and GeoEye, provide both panchromatic images at a higher spatial resolution and multispectral (MS) images at a lower spatial resolution but rich spectral information (Thomas and Wald, 2004;Choi et al, 2014). Due to several technological limitations, it is impossible to have a single sensor with both high spatial and spectral resolution (Thomas and Wald, 2004;Yuhendra et al 2012).…”

Section: Introductionmentioning

confidence: 98%

An object-level strategy for pan-sharpening quality assessment of high-resolution satellite imagery

Dadrasjavan

Samadzadegan

2014

Advances in Space Research

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“…Image fusion techniques have been utilized in various applications, such as remote sensing. Combining two or more images of the same scene usually produces a better application-wise visible image [6]- [10].…”

Section: Introductionmentioning

confidence: 99%

Modeling techniques for target detection

2014

Self Cite

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To better study hyperspectral imaging sensors and remote sensing system performance, different parts of the remote sensing system can be modeled.Here, a scientific overview of recent modeling techniques is presented to come up with an appropriate approach for modeling of a target detection system. In particular, this study focuses on the development in modeling of scene, sensors, and processing algorithms. Moreover, the parallelization of the detection methods is emphasized for which the process of target detection accelerates. In conclusion, an appropriate model to evaluate a target detection system can be a hybrid model in which hyperspectral sensors, radars, and local sensors have been modeled.

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Quality Assessment of Image Fusion Methods in Transform Domain

Abstract: ABSTRACT

Cited by 15 publications

References 22 publications

The fusion of multisensor images with multi-resolutions

The fusion of multisensor images with multi-resolutions

An object-level strategy for pan-sharpening quality assessment of high-resolution satellite imagery

Modeling techniques for target detection

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