scholarly journals Image Fusion for High-Resolution Optical Satellites Based on Panchromatic Spectral Decomposition

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2619 ◽  
Author(s):  
Luxiao He ◽  
Mi Wang ◽  
Ying Zhu ◽  
Xueli Chang ◽  
Xiaoxiao Feng
Keyword(s):  
High Resolution ◽  
Image Fusion ◽  
Spectral Decomposition ◽  
Spectral Image ◽  
Zero Bias ◽  
Spectral Bands ◽  
Fused Image ◽  
Transformation Methods ◽  
Spectral Ranges ◽  
Bias Terms

Ratio transformation methods are widely used for image fusion of high-resolution optical satellites. The premise for the use the ratio transformation is that there is a zero-bias linear relationship between the panchromatic band and the corresponding multi-spectral bands. However, there are bias terms and residual terms with large values in reality, depending on the sensors, the response spectral ranges, and the land-cover types. To address this problem, this paper proposes a panchromatic and multi-spectral image fusion method based on the panchromatic spectral decomposition (PSD). The low-resolution panchromatic and multi-spectral images are used to solve the proportionality coefficients, the bias coefficients, and the residual matrixes. These coefficients are substituted into the high-resolution panchromatic band and decompose it into the high-resolution multi-spectral bands. The experiments show that this method can make the fused image acquire high color fidelity and sharpness, it is robust to different sensors and features, and it can be applied to the panchromatic and multi-spectral fusion of high-resolution optical satellites.

WAVELET-BASED WEIGHTED AVERAGE AND HUMAN VISION SYSTEM IMAGE FUSION

2006 ◽  
Vol 04 (01) ◽  
pp. 97-103 ◽  
Author(s):  
HONG LI
Keyword(s):  
Image Fusion ◽  
Vision System ◽  
Weighted Average ◽  
Human Vision ◽  
The Novel ◽  
Human Vision System ◽  
Spectral Image ◽  
Multi Scale ◽  
System Method ◽  
Fused Image

Image fusion is one of the most important techniques to enhance image information. The fused image is more suitable for image processing. The novel pixel level image fusion schemes are presented based on multi-scale decomposition. The wavelet coefficients of image are chosen according to the image fusion operators and different fusion rules. Experiments of multi-spectral image and high-resolution panchromatic images are given. It shows that the wavelet-based human visual system method can achieve better fusion performance than others.

An Improved Image Fusion Algorithm Based on IHS and Wavelet Transform

2013 ◽  
Vol 427-429 ◽  
pp. 1641-1644
Author(s):  
Liu Ming ◽  
Shu Hui Li
Keyword(s):  
Wavelet Transform ◽  
High Resolution ◽  
Image Fusion ◽  
Wavelet Transformation ◽  
Experiment Research ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Spectral Image ◽  
Panchromatic Image ◽  
Ihs Transform

A new improved image fusion algorithm is proposed for multi-spectral image (MUL) and the high-resolution panchromatic image (PAN) based on intensity-hue-saturation (IHS) transform combined with wavelet transformation (WT). Firstly, the multi-spectral image is transformed into the IHS space for getting the intensity component (I).Then the high-resolution panchromatic image and I were matched with histogram. Secondly, the PAN and I were decomposed respectively by WT and fused to obtain the new I by the inverse WT. Finally, the fusion image was obtained by inverse IHS transform. four evaluate indicators are defined in this paper. By experiment research, the results show that this new method can effectively improve the fusion effect.

scholarly journals FUSION OF HYPERSPECTRAL AND MULTISPECTRAL IMAGERY WITH REGRESSION KRIGING AND THE LULU OPERATORS; A COMPARISON

2018 ◽  
Vol XLII-5 ◽  
pp. 583-588 ◽  
Author(s):  
N. Jeevanand ◽  
P. A. Verma ◽  
S. Saran
Keyword(s):  
High Resolution ◽  
Image Fusion ◽  
Satellite Image ◽  
Relevant Information ◽  
Regression Kriging ◽  
Digital World ◽  
Quality Aspects ◽  
Fused Image ◽  
Liss Iii ◽  
Hyperion Image

<p><strong>Abstract.</strong> In this digital world, there is a large requirement of high resolution satellite image. Images at a low resolution may contain relevant information that has to be integrated with the high resolution image to obtain the required information. This is being fulfilled by image fusion. Image fusion is merging of different resolution images into a single image. The output image contains more information, as the information is integrated from both the images Image fusion was conducted with two different algorithms: regression kriging and the LULU operators. First, regression Kriging estimates the value of a dependent variable at unsampled location with the help of auxiliary variables. Here we used regression Kriging with the Hyperion image band as the response variables and the LISS III image bands are the explanatory variables. The fused image thus has the spectral variables from Hyperion image and the spatial variables from the LISS III image. Second, the LULU operator is an image processing methods that can be used as well in image fusion technique. Here we explored to fuse the Hyperion and LISS III image. The LULU operators work in three stages of the process, viz the decomposition stage, the fusion and the reconstruction stage. Quality aspects of the fused image for both techniques have been compared for spectral quality (correlation) and spatial quality (entropy). The study concludes that the quality of the fused image obtained with regression kriging is better than that obtained with the LULU operator.</p>

scholarly journals Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 863
Author(s):  
Vidas Raudonis ◽  
Agne Paulauskaite-Taraseviciene ◽  
Kristina Sutiene
Keyword(s):  
Deep Learning ◽  
Image Fusion ◽  
Early Stage ◽  
Image Data ◽  
Cell Detection ◽  
Processing Times ◽  
Fused Image ◽  
Stage Embryo ◽  
Early Stage Embryo

Background: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time. Methods: Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image. Results: The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques—Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization. Conclusion: Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image.

Hyperspectral and multispectral image fusion techniques for high resolution applications: a review

2021 ◽  
Author(s):  
Dioline Sara ◽  
Ajay Kumar Mandava ◽  
Arun Kumar ◽  
Shiny Duela ◽  
Anitha Jude
Keyword(s):  
High Resolution ◽  
Image Fusion ◽  
Multispectral Image

Fused Image Quality Measure Based on Structural Similarity

2011 ◽  
Vol 255-260 ◽  
pp. 2072-2076
Author(s):  
Yi Yong Han ◽  
Jun Ju Zhang ◽  
Ben Kang Chang ◽  
Yi Hui Yuan ◽  
Hui Xu
Keyword(s):  
Image Fusion ◽  
Structural Information ◽  
Similarity Index ◽  
Structural Similarity ◽  
Reference Image ◽  
Human Visual Perception ◽  
Subjective Evaluations ◽  
Fused Image ◽  
Fusion Methods ◽  
Image Quality Measure

Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we present a new approach using structural similarity index for assessing quality in image fusion. The advantages of our measures are that they do not require a reference image and can be easily computed. Numerous simulations demonstrate that our measures are conform to subjective evaluations and can be able to assess different image fusion methods.

scholarly journals How can remote sensing techniques help monitoring the vine and maximize the terroir potential?

2018 ◽  
Vol 50 ◽  
pp. 02007
Author(s):  
Cecile Tondriaux ◽  
Anne Costard ◽  
Corinne Bertin ◽  
Sylvie Duthoit ◽  
Jérôme Hourdel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Sampling Strategy ◽  
Wine Quality ◽  
Wine Production ◽  
Spectral Bands ◽  
High Resolution Satellite Images ◽  
Remote Sensing Techniques ◽  
Set Up

In each winegrowing region, the winegrower tries to value its terroir and the oenologists do their best to produce the best wine. Thanks to new remote sensing techniques, it is possible to implement a segmentation of the vineyard according to the qualitative potential of the vine stocks and make the most of each terroir to improve wine quality. High resolution satellite images are processed in several spectral bands and algorithms set-up specifically for the Oenoview service allow to estimate vine vigour and a heterogeneity index that, used together, directly reflect the vineyard oenological potential. This service is used in different terroirs in France (Burgundy, Languedoc, Bordeaux, Anjou) and in other countries (Chile, Spain, Hungary and China). From this experience, we will show how remote sensing can help managing vine and wine production in all covered terroirs. Depending on the winegrowing region and its specificities, its use and results present some differences and similarities that we will highlight. We will give an overview of the method used, the advantage of implementing field intra-or inter-selection and how to optimize the use of amendment and sampling strategy as well as how to anticipate the whole vineyard management.

Sign in / Sign up

Export Citation Format

Share Document