Spatial and Spectral Quality Evaluation Based on Edges Regions of Satellite: Image Fusion

Al-Wassai, Firouz Abdullah; Kalyankar, N. V.; Al-Zaky, Ali A.

doi:10.1109/acct.2012.107

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…Feature level fusion uses a group of image pixel to form contiguous regions and requires extraction of different features from source data [7,21]. Features can be pixel intensities, edges or texture features [22,24]. iv.…”

Section: Fusion Levelsmentioning

confidence: 99%

Multimodal Medical Image Fusion in Cardiovascular Applications

Pohl

Nazirun

Hamzah

et al. 2015

Lecture Notes in Bioengineering

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Recent publications in the field of medical image fusion point out the value of multi-modality in diagnosis, pre-surgical planning and surgical intervention. The integration of multiple data sources including medical images from different devices or sensors strongly increases reliability and information content. Successfully fused multi-modal data should not contain any artefacts, not remove any relevant information from the original data and minimize redundancy. Image and data fusion aims at providing supplementary clinical information that is not apparent in the individual images alone. Image and data fusion finds many different applications in the fields of remote sensing, military, biometrics, machine vision and medical imaging. The scientific community has established three levels of fusion rules, namely pixel, feature and decision level. Depending on the application, processing technique or available data each level has its importance and proven significance in medical data processing. Each level provides a set of rules that can be applied. The selection of the fusion operator has a strong impact on the quality of the result. It becomes apparent that the selection of level and technique must vary according to the information that needs to be extracted for a certain application. Each technique has its advantages and disadvantages which have to be carefully evaluated. Based on the availability of multimodal devices, such as ultrasound (US), magnetic resonance imaging (MRI) and computed tomography (CT), different images and data of the same object are obtained. The multiple images, the variety of fusion levels and rules lead to an uncountable number of possible combinations. This makes it very difficult for the user to select the most beneficial solution without losing valuable time and resources.

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Section: Fusion Levelsmentioning

confidence: 99%

Multimodal Medical Image Fusion in Cardiovascular Applications

Pohl

Nazirun

Hamzah

et al. 2015

Lecture Notes in Bioengineering

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“…Then the imagery is back-transformed to the normal image space. Common image fusion approaches classified as CS techniques are the intensity-hue-saturation [16] [17] [18], principal component analysis [14], and Gram-Schmidt [19] [20] [21] spectral sharpening methods. Adaptive component substitution methods have also been developed for fusing imagery [22] [23].…”

Section: Introductionmentioning

confidence: 99%

Comparing Pan-Sharpening Algorithms to Access an Agriculture Area: A Mississippi Case Study

Fletcher

2023

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Numerous satellites collect imagery of the Earth's surface daily, providing information to the public and private sectors. The fusion (pan-sharpening) of high-resolution panchromatic satellite imagery with lower-resolution multispectral satellite imagery has shown promise for monitoring natural resources and farming areas. It results in new imagery with more detail than the original multispectral or panchromatic images. In agricultural areas in Mississippi, landscapes can range from complex mixtures of vegetation and built-up areas to dense vegetative regions. More information is needed on pan-sharpened imagery for assessing landscapes in rural areas of Mississippi. WorldView 3 satellite imagery consisting of landscapes commonly found in rural areas of Mississippi was subjected to 17 pan-sharpening algorithms. The pan-sharpened images were compared qualitatively and quantitatively with three quality indices: 1) Erreur Relative Globale Addimensionelle de Synthese; 2) Universal Image Quality Index; 3) Bias. À trous wavelet transform with the injection model 3 and hyperspherical color spaced fusion methods were ranked among the best for maintaining image integrity for qualitative and quantitative analyses. The optimized high-pass filter method was often ranked last by the quality indices. The smoothing filter-based intensity modulation algorithm and the gaussian modulation transfer function match filtered with high-pass modulation injection model added artifacts to the images. Pansharpened satellite imagery has great potential to enhance the survey of Mississippi's agricultural areas. The key to success is selecting an image fusion process that increases spatial content while not compromising the image integrity.

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“…Spatial are basic whereas, temporal techniques are advanced and computationally extensive . For instance, pair wise histogram matching, intensity hue saturation (IHS), transformed based image fusion are spatial techniques . On the other hand, wavelet transform and discrete cosine transform (DCT) based image fusions fall in the category of temporal techniques.…”

Section: Introductionmentioning

confidence: 99%

Neuro‐wavelet based intelligent medical image fusion

Hassan

Murtza

Khan

et al. 2019

Int J Imaging Syst Tech

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Imaging based sensitive clinical diagnosis is critically depending on image quality. In this article, the problem of enhancing fundus images is addressed by a novel fusion technique. The proposed approach utilizes the representation capability of wavelet transform and the learning ability of artificial neural networks. In this approach, input images are decomposed into wavelet transform followed by appropriate feature extraction for training of neural networks to obtain fused image. To ensure homogeneity, it employs consistency verification for minimizing the fusion artifacts. The visual and quantitative performance of the proposed approach is assessed using a number of experiments performed on the standard datasets of DRIVE and DRION‐DB. The experimental results demonstrate that the proposed fusion technique offers high average structural similarity of “0.99.” The proposed approach outperforms state‐of‐the‐art techniques which validates its effectiveness. The developed approach might be applied by the clinical diagnosis system for fundus related diseases.

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Spatial and Spectral Quality Evaluation Based on Edges Regions of Satellite: Image Fusion

Cited by 9 publications

References 14 publications

Multimodal Medical Image Fusion in Cardiovascular Applications

Multimodal Medical Image Fusion in Cardiovascular Applications

Comparing Pan-Sharpening Algorithms to Access an Agriculture Area: A Mississippi Case Study

Neuro‐wavelet based intelligent medical image fusion

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