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Open AccessJournal ArticleDOI

Image Fusion in Remote Sensing Applications: A Review

Vaibhav R. Pandit, +1 more
- 18 Jun 2015 - 
- Vol. 120, Iss: 10, pp 22-32
TLDR
This paper is an honest attempt to collectively discuss all possible algorithms along with quality metrics following two assessment procedures i.e. at full and reduced scale resolutions to evaluate performance of these algorithms.
Abstract
Major technical constraints like minimum data storage at satellite platform in space, less bandwidth for communication with earth station, etc. limits the satellite sensors from capturing images with high spatial and high spectral resolutions simultaneously. To overcome this limitation, image fusion has proved to be a potential tool in remote sensing applications which integrates the information from combinations of panchromatic, multispectral or hyperspectral images; intended to result in a composite image having both higher spatial and higher spectral resolutions. The research in this area cites date back to last few decades, but the diverse approaches proposed so far by different researchers have been rarely discussed at one place. This paper is an honest attempt to collectively discuss all possible algorithms along with quality metrics following two assessment procedures i.e. at full and reduced scale resolutions to evaluate performance of these algorithms.

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Citations
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Book ChapterDOI

Multi-sensor Image Fusion Using Intensity Hue Saturation Technique

TL;DR: The objective of this paper is to combine higher spectral information in one image with higher spatial information of another image to sharpen image resolution (display) and improved classification.
Journal ArticleDOI

Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery

TL;DR: In this study, the widely used modified intensity-hue-saturation (mIHS) method better preserved the spectral information and spatial autocorrelation compared with the other methods and produced results superior to those of the GS, CN, and PC methods by preserving image colors.
Proceedings ArticleDOI

Pixel-level Multi-focus Image Fusion Algorithm Based on 2DPCA

TL;DR: A pixel-level multi-focus image fusion algorithm that uses two-dimensional Principal Component Analysis (2DPCA) to extract features from source images and result is compared visually and qualitatively with two existing algorithms of same class using objective quality assessment method.
Journal ArticleDOI

Hybrid fusion approach for synthetic aperture radar and multispectral imagery for improvement in land use land cover classification

TL;DR: A hybrid fusion approach to integrate information from synthetic aperture radar (SAR) and multispectral (MS) imagery to improve land use land cover (LULC) classification is presented and it is proved that the proposed hybrid approach is superior to conventional approaches.
References
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Journal ArticleDOI

Multisensor image fusion using the wavelet transform

TL;DR: In this article, an image fusion scheme based on the wavelet transform is presented, where wavelet transforms of the input images are appropriately combined, and the new image is obtained by taking the inverse wavelet transformation of the fused wavelet coefficients.
Journal ArticleDOI

A Critical Comparison Among Pansharpening Algorithms

TL;DR: The authors attempt to fill the gap by providing a critical description and extensive comparisons of some of the main state-of-the-art pansharpening methods by offering a detailed comparison of their performances with respect to the different instruments.
Journal ArticleDOI

A comparative analysis of image fusion methods

TL;DR: This paper presents a comprehensive framework, the general image fusion (GIF) method, which makes it possible to categorize, compare, and evaluate the existing image fusion methods.
Journal ArticleDOI

Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification

TL;DR: The proposed framework employs local binary patterns to extract local image features, such as edges, corners, and spots, and employs the efficient extreme learning machine with a very simple structure as the classifier.
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