scholarly journals A Learning-Based Image Fusion for High-Resolution SAR and Panchromatic Imagery

2020 ◽  
Vol 10 (9) ◽  
pp. 3298
Author(s):  
Dae Kyo Seo ◽  
Yang Dam Eo
Keyword(s):  
Image Fusion ◽  
Spatial Information ◽  
Sufficient Information ◽  
Random Forest Regression ◽  
Complementary Method ◽  
Linear Relationships ◽  
Source Data ◽  
Non Linear ◽  
Fusion Methods ◽  
Imaging Mechanisms

Image fusion is an effective complementary method to obtain information from multi-source data. In particular, the fusion of synthetic aperture radar (SAR) and panchromatic images contributes to the better visual perception of objects and compensates for spatial information. However, conventional fusion methods fail to address the differences in imaging mechanism and, therefore, they cannot fully consider all information. Thus, this paper proposes a novel fusion method that both considers the differences in imaging mechanisms and sufficiently provides spatial information. The proposed method is learning-based; it first selects data to be used for learning. Then, to reduce the complexity, classification is performed on the stacked image, and the learning is performed independently for each class. Subsequently, to consider sufficient information, various features are extracted from the SAR image. Learning is performed based on the model’s ability to establish non-linear relationships, minimizing the differences in imaging mechanisms. It uses a representative non-linear regression model, random forest regression. Finally, the performance of the proposed method is evaluated by comparison with conventional methods. The experimental results show that the proposed method is superior in terms of visual and quantitative aspects, thus verifying its applicability.

scholarly journals Hyperspectral and Multispectral Remote Sensing Image Fusion Based on Endmember Spatial Information

2020 ◽  
Vol 12 (6) ◽  
pp. 1009
Author(s):  
Xiaoxiao Feng ◽  
Luxiao He ◽  
Qimin Cheng ◽  
Xiaoyi Long ◽  
Yuxin Yuan
Keyword(s):  
Image Fusion ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
Spatial Information ◽  
Spectral Unmixing ◽  
High Spectral Resolution ◽  
Remote Sensing Image Fusion ◽  
Fusion Methods ◽  
Invariant Regions ◽  
The Difference

Hyperspectral (HS) images usually have high spectral resolution and low spatial resolution (LSR). However, multispectral (MS) images have high spatial resolution (HSR) and low spectral resolution. HS–MS image fusion technology can combine both advantages, which is beneficial for accurate feature classification. Nevertheless, heterogeneous sensors always have temporal differences between LSR-HS and HSR-MS images in the real cases, which means that the classical fusion methods cannot get effective results. For this problem, we present a fusion method via spectral unmixing and image mask. Considering the difference between the two images, we firstly extracted the endmembers and their corresponding positions from the invariant regions of LSR-HS images. Then we can get the endmembers of HSR-MS images based on the theory that HSR-MS images and LSR-HS images are the spectral and spatial degradation from HSR-HS images, respectively. The fusion image is obtained by two result matrices. Series experimental results on simulated and real datasets substantiated the effectiveness of our method both quantitatively and visually.

scholarly journals Hyperspectral and Multispectral Image Fusion Techniques

2020 ◽  
Vol 8 (5) ◽  
pp. 467-471
Keyword(s):  
Image Fusion ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Qualitative Comparative Analysis ◽  
Infrared Region ◽  
Multispectral Image ◽  
Electromagnetic Spectrum ◽  
Fusion Methods

The hyper spectral image covers a broad range of wavelengths in electromagnetic spectrum, spanning from visible to near-infrared region. The basic objective of hyperspectral imaging is to attain the spectrum for each pixel in the image of a scene, with the aim of identifying objects in the scene and its classification. The hyperspectral images give detailed spectral information but their spatial resolution is very poor. So to enhance the visual quality of the hyperspectral image, we can perform image fusion with high spatial information multispectral image. This paper provides a complete description of hyperspectral imaging and image fusion methods of hyperspectral and multispectral images. A quantitative and qualitative comparative analysis on performance of various hyperspectral and multispectral image fusion techniques are also done.

scholarly journals The Fusion of MRI and CT Medical Images Using Variational Mode Decomposition

2021 ◽  
Vol 11 (22) ◽  
pp. 10975
Author(s):  
Srinivasu Polinati ◽  
Durga Prasad Bavirisetti ◽  
Kandala N V P S Rajesh ◽  
Ganesh R Naik ◽  
Ravindra Dhuli
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Spatial Information ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Medical Image Fusion ◽  
Mode Decomposition ◽  
Fused Image ◽  
Fusion Methods ◽  
Magnetic Resonance Imaging Mri

In medical image processing, magnetic resonance imaging (MRI) and computed tomography (CT) modalities are widely used to extract soft and hard tissue information, respectively. However, with the help of a single modality, it is very challenging to extract the required pathological features to identify suspicious tissue details. Several medical image fusion methods have attempted to combine complementary information from MRI and CT to address the issue mentioned earlier over the past few decades. However, existing methods have their advantages and drawbacks. In this work, we propose a new multimodal medical image fusion approach based on variational mode decomposition (VMD) and local energy maxima (LEM). With the help of VMD, we decompose source images into several intrinsic mode functions (IMFs) to effectively extract edge details by avoiding boundary distortions. LEM is employed to carefully combine the IMFs based on the local information, which plays a crucial role in the fused image quality by preserving the appropriate spatial information. The proposed method’s performance is evaluated using various subjective and objective measures. The experimental analysis shows that the proposed method gives promising results compared to other existing and well-received fusion methods.

scholarly journals Satellite image fusion using fuzzy logic

2016 ◽  
Vol 8 (2) ◽  
pp. 241-253 ◽  
Author(s):  
Suda Kumaraswamy ◽  
Dammavalam Srinivasa Rao ◽  
Nuthanapati Naveen Kumar
Keyword(s):  
Fuzzy Logic ◽  
Wavelet Transform ◽  
Side Effects ◽  
Image Fusion ◽  
Spatial Information ◽  
Satellite Image ◽  
Principal Component ◽  
Input Image ◽  
Fusion Methods ◽  
Fuzzy Logic Technique

Abstract Image fusion is a method of combining the Multispectral (MS) and Panchromatic (PAN) images into one image contains more information than any of the input. Image fusion aim is to decrease unknown and weaken common data in the fused output image at the same time improving necessary information. Fused images are helpful in various applications like, remote sensing, computer vision, biometrics, change detection, image analysis and image classification. Conventional fusion methods are having some side effects like assertive spatial information and uncertain color information is an usually the problem in PCA and wavelet transform based fusion is a computationally in depth process. In order to overcome these side effects and to propose alternative soft computing fusion approach for conventional fusion methods we exploit image fusion using fuzzy logic technique to fuse two source images obtained from different sensors to enhance both spectral and spatial information. The proposed work here further compared with two common fusion methods like, principal component analysis (PCA) and wavelet transform along with quality assessment metrics. Exploratory outputs demonstrated in order that fuzzy based image fusion technique can actively retains more information compared to PCA and wavelet transform approaches while enhancing the spatial and spectral resolution of the satellite images.

scholarly journals Fusion of SAR and Multispectral Images Using Random Forest Regression for Change Detection

2018 ◽  
Vol 7 (10) ◽  
pp. 401 ◽  
Author(s):  
Dae Kyo Seo ◽  
Yong Hyun Kim ◽  
Yang Dam Eo ◽  
Mi Hee Lee ◽  
Wan Yong Park
Keyword(s):  
Random Forest ◽  
Change Detection ◽  
Image Fusion ◽  
Optical Image ◽  
Sar Image ◽  
Sar Images ◽  
Adaptive Boosting ◽  
Stochastic Gradient Boosting ◽  
Fusion Methods ◽  
Imaging Mechanisms

In order to overcome the insufficiency of single remote sensing data in change detection, synthetic aperture radar (SAR) and optical image data can be used together for supplementation. However, conventional image fusion methods fail to address the differences in imaging mechanisms and cannot overcome some practical limitations such as usage in change detection or temporal requirement of the optical image. This study proposes a new method to fuse SAR and optical images, which is expected to be visually helpful and minimize the differences between two imaging mechanisms. The algorithm performs the fusion by establishing relationships between SAR and multispectral (MS) images by using a random forest (RF) regression, which creates a fused SAR image containing the surface roughness characteristics of the SAR image and the spectral characteristics of the MS image. The fused SAR image is evaluated by comparing it to those obtained using conventional image fusion methods and the proposed method shows that the spectral qualities and spatial qualities are improved significantly. Furthermore, for verification, other ensemble approaches such as stochastic gradient boosting regression and adaptive boosting regression are compared and overall it is confirmed that the performance of RF regression is superior. Then, change detection between the fused SAR and MS images is performed and compared with the results of change detection between MS images and between SAR images and the result using fused SAR images is similar to the result with MS images and is improved when compared to the result between SAR images. Lastly, the proposed method is confirmed to be applicable to change detection.

Fuzzy and Neuro Fuzzy Based Iterative Image Fusion

2019 ◽  
Vol 12 ◽  
Author(s):  
Srinivasa Rao Dammavalam ◽  
Shadi A. Aljawarneh ◽  
N. Rajasekhar
Keyword(s):  
Neural Networks ◽  
Image Fusion ◽  
Spatial Information ◽  
Principal Component ◽  
Fuzzy Methods ◽  
Neuro Fuzzy ◽  
Fused Image ◽  
Fusion Methods ◽  
The One ◽  
Networks Structure

Image fusion is to converge Multispectral (MS) and Panchromatic (PAN) images into a fused image which is further enlightened. Soft computing based image fusion techniques are fuzzy and neuro-fuzzy are exploited to lessening the severance and vagueness in the output. Fused images achieved after the synthesis is utilized in image analysis, medical applications, armed province, and computer revelation. In this research, we convey iterative image fusion based on fuzzy and neuro-fuzzy methods on source images attained from different sources to improve visualization proficiency. We also compared the proposed techniques with principal component analysis (PCA) and wavelets transform based image fusion. Fused outcomes accomplished from image fusion methods are assessed through typical eminence evaluation parameters. The resulting outcome obtained from iterative fusion is improved in terms of spectral and spatial information when compared to the one-time fused image. Due to neural networks structure, applied sorts of biological neural networks and potentiality of the fuzzy and neuro-fuzzy logic, the proposed method overtakes the conventional methods. The complete investigational consequences formed from anticipated methodology established that the utilization of proposed approaches enhanced image content.

scholarly journals Relationship between Age and Value of Information for a Noisy Ornstein–Uhlenbeck Process

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 940
Author(s):  
Zijing Wang ◽  
Mihai-Alin Badiu ◽  
Justin P. Coon
Keyword(s):  
Value Of Information ◽  
Markov Models ◽  
Upper Bounds ◽  
Distribution Functions ◽  
Lower And Upper Bounds ◽  
Uhlenbeck Process ◽  
Source Data ◽  
Non Linear ◽  
Queue Model ◽  
Selection Of

The age of information (AoI) has been widely used to quantify the information freshness in real-time status update systems. As the AoI is independent of the inherent property of the source data and the context, we introduce a mutual information-based value of information (VoI) framework for hidden Markov models. In this paper, we investigate the VoI and its relationship to the AoI for a noisy Ornstein–Uhlenbeck (OU) process. We explore the effects of correlation and noise on their relationship, and find logarithmic, exponential and linear dependencies between the two in three different regimes. This gives the formal justification for the selection of non-linear AoI functions previously reported in other works. Moreover, we study the statistical properties of the VoI in the example of a queue model, deriving its distribution functions and moments. The lower and upper bounds of the average VoI are also analysed, which can be used for the design and optimisation of freshness-aware networks. Numerical results are presented and further show that, compared with the traditional linear age and some basic non-linear age functions, the proposed VoI framework is more general and suitable for various contexts.

scholarly journals Infrared and visible image fusion via octave Gaussian pyramid framework

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lei Yan ◽  
Qun Hao ◽  
Jie Cao ◽  
Rizvi Saad ◽  
Kun Li ◽  
...  
Keyword(s):  
Image Fusion ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Visible Image ◽  
Composite Image ◽  
Multiscale Decomposition ◽  
Gaussian Pyramid ◽  
Fusion Methods ◽  
Scale Spaces ◽  
Fusion Framework

AbstractImage fusion integrates information from multiple images (of the same scene) to generate a (more informative) composite image suitable for human and computer vision perception. The method based on multiscale decomposition is one of the commonly fusion methods. In this study, a new fusion framework based on the octave Gaussian pyramid principle is proposed. In comparison with conventional multiscale decomposition, the proposed octave Gaussian pyramid framework retrieves more information by decomposing an image into two scale spaces (octave and interval spaces). Different from traditional multiscale decomposition with one set of detail and base layers, the proposed method decomposes an image into multiple sets of detail and base layers, and it efficiently retains high- and low-frequency information from the original image. The qualitative and quantitative comparison with five existing methods (on publicly available image databases) demonstrate that the proposed method has better visual effects and scores the highest in objective evaluation.

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