Fusion of pan images ms and preserving spectral quality
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Fusion of PAN Images MS and Preserving Spectral Quality The document presents a new method for fusing multispectral (MS) and panchromatic (PAN) images that aims to improve both the spatial details and spectral information in the fused image. Filter-based fusion approaches are discussed that extract spatial information from the PAN image and inject it into the MS images. The document proposes designing an optimal filter using statistical properties of the images to extract relevant information from the PAN image. Visual and statistical assessments show the new method improves fusion quality metrics compared to other existing fusion methods like improved IHS, multiscale Kalman filter, and sparse image fusion.
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sing modularity Q, responsiveness is used instead o
f
modularity Q as similarity measure. As a result of
this analysis, "regional information cluster" which
refers
to similar "Theinformation of purchase history" nod
es group is generated. Finally, similar regions are
visualized by mapping the regional information clus
ter on the map. This visualization system is expect
ed to
contribute as an analytical tool for customers’ pur
chasing behaviour and so on.
snower malik
This document discusses using graph convolutional neural networks (GCNNs) for classifying building patterns from spatial vector data. GCNNs generalize CNNs to non-grid data structures like graphs. The document introduces applying GCNNs to spatial vector data by converting the convolution operation from the vertex domain to a point-wise product in the Fourier domain, overcoming the problem that spatial vector data can only be modeled as graphs which do not satisfy CNNs' regularity requirements. It concludes that GCNNs provide a way to perform pattern analysis and knowledge mining of spatial vector data using powerful convolutional networks.
Image Enhancement and Restoration by Image Inpainting
Inpainting is the process of reconstructing lost or deteriorated part of images based on the background information. i. e .it fills the missing or damaged region in an image utilizing spatial information of its neighboring region. Inpainting algorithm have numerous applications. It is helpfully used for restoration of old films and object removal in digital photographs. The main goal of the algorithm is to modify the damaged region in an image in such a way that the inpainted region is undetectable to the ordinary observers who are not familiar with the original image. This proposed work presents image inpainting process for image enhancement and restoration by using structural, texture and exemplar techniques. This paper presents efficient algorithm that combines the advantages of these two approaches. We first note that exemplar-based texture synthesis contains the essential process required to replicate both texture and structure; the success of structure propagation, however, is highly dependent on the order in which the filling proceeds. We propose a best-first algorithm in which the confidence in the synthesized pixel values is propagated in a manner similar to the propagation of information in inpainting. The actual color values are computed using exemplar-based synthesis. Computational efficiency is achieved by a blockbased sampling process.
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What's hot (18)
Modeling Call Holding Times Of Public Safety Network
Modeling Call Holding Times Of Public Safety Network
Secure spin with cluster for data centric wireless sensor network
Secure spin with cluster for data centric wireless sensor network
Juha vesanto esa alhoniemi 2000:clustering of the som
Juha vesanto esa alhoniemi 2000:clustering of the som
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Extended hybrid region growing segmentation of point clouds with different re...
CLUSTERING DATA STREAMS BASED ON SHARED DENSITY BETWEEN MICRO-CLUSTERS
CLUSTERING DATA STREAMS BASED ON SHARED DENSITY BETWEEN MICRO-CLUSTERS
Content Based Image Retrieval Using Dominant Color and Texture Features
Content Based Image Retrieval Using Dominant Color and Texture Features
Clustering data streams based on shared density between micro clusters
Clustering data streams based on shared density between micro clusters
APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN ESTIMATING PARTICIPATION IN ELEC...
APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN ESTIMATING PARTICIPATION IN ELEC...
Illustration of Medical Image Segmentation based on Clustering Algorithms
Illustration of Medical Image Segmentation based on Clustering Algorithms
Semi-Supervised Discriminant Analysis Based On Data Structure
Semi-Supervised Discriminant Analysis Based On Data Structure
The study about the analysis of responsiveness pair clustering tosocial netwo...
The study about the analysis of responsiveness pair clustering tosocial netwo...
Image Enhancement and Restoration by Image Inpainting
Image Enhancement and Restoration by Image Inpainting
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Similar to Fusion of pan images ms and preserving spectral quality
A decomposition framework for image denoising algorithms
This paper proposes a novel framework for image denoising using an image decomposition model. The model decomposes images into components in a local moving frame that encodes the image's geometry. The key strategy is to denoise the image components in this moving frame in order to better preserve the local geometry, rather than directly denoising the original image. Experiments on a database of images tested with multiple denoising methods show that this framework provides better results than directly denoising images, according to peak signal-to-noise ratio and structural similarity index metrics.
Facial image retrieval on semantic features using adaptive mean genetic algor...
The emergence of larger databases has made image retrieval techniques an essential component and has led to the development of more efficient image retrieval systems. Retrieval can either be content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET database. Input query images are subjected to several processing techniques in the database before computing the squared Euclidean distance (SED) between them. The images with the shortest Euclidean distance are considered as a match and are retrieved. The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Adaptive Mean Genetic Algorithm (AMGA). In this study, the average PSNR value obtained after applying the Wiener filter was 45.29. The performance of the AMGA was evaluated based on its precision, F-measure, and recall, and the obtained average values were respectively 0.75, 0.692, and 0.66. The performance matrix of the AMGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.
Implementation of Fuzzy Logic for the High-Resolution Remote Sensing Images w...
This document describes an implementation of fuzzy logic for high-resolution remote sensing image classification with improved accuracy. It discusses using an object-based approach with fuzzy rules to classify urban land covers in a satellite image. The approach involves image segmentation using k-means clustering or ISODATA clustering. Features are then extracted from the image objects and fuzzy logic is applied to classify the objects based on membership functions. The method was tested on different sensor and resolution images in MATLAB and showed improved classification accuracy over other techniques, achieving lower entropy in results. Future work planned includes designing an unsupervised classification model combining k-means clustering and fuzzy-based object orientation.
Journal Publishers
Alinteri Journal of Agriculture Sciences aims to create an environment for researchers to introduce, share, read, and discuss recent scientific progress. We adopt the policy of providing open access to readers who may be interested in recent developments. Alinteri Journal of Agriculture Sciences is being published online biannually as of 2007. The journal is an open access, international, double-blind peer-reviewed journal publishing research articles, Invited reviews, short communications, and letters to the Editor in the fields of agriculture, fisheries, veterinary, biology, and closely related disciplines.
C OMPARATIVE S TUDY OF D IMENSIONALITY R EDUCTION T ECHNIQUES U SING PCA AND ...
The aim of this paper is to present a comparative s
tudy of two linear dimension reduction
methods namely PCA (Principal Component Analysis) a
nd LDA (Linear Discriminant Analysis).
The main idea of PCA is to transform the high dimen
sional input space onto the feature space
where the maximal variance is displayed. The featur
e selection in traditional LDA is obtained
by maximizing the difference between classes and mi
nimizing the distance within classes. PCA
finds the axes with maximum variance for the whole
data set where LDA tries to find the axes
for best class seperability. The proposed method is
experimented over a general image database
using Matlab. The performance of these systems has
been evaluated by Precision and Recall
measures. Experimental results show that PCA based
dimension reduction method gives the
better performance in terms of higher precision and
recall values with lesser computational
complexity than the LDA based method.
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
Semantic annotation of images is an important research topic on both image understanding and database
or web image search. Image annotation is a technique to choosing appropriate labels for images with
extracting effective and hidden feature in pictures. In the feature extraction step of proposed method, we
present a model, which combined effective features of visual topics (global features over an image) and
regional contexts (relationship between the regions in Image and each other regions images) to automatic
image annotation.In the annotation step of proposed method, we create a new ontology (base on WordNet
ontology) for the semantic relationships between tags in the classification and improving semantic gap
exist in the automatic image annotation.Experiments result on the 5k Corel dataset show the proposed
method of image annotation in addition to reducing the complexity of the classification, increased accuracy
compared to the another methods.
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
This document summarizes a research paper that proposes a new ontology-based method for automatic image retrieval and annotation using 5,000 images from the Corel dataset. The method combines global and regional visual features with contextual relationships defined in an ontology. It creates a new ontology based on WordNet to semantically relate tags and reduce gaps between low-level features and high-level concepts. Experimental results show the proposed method increases annotation accuracy compared to other methods.
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
This document summarizes a research paper that proposes a new ontology-based method for automatic image retrieval and annotation using 5,000 images from the Corel dataset. The method combines global and regional visual features with contextual relationships defined in an ontology. It creates a new ontology based on WordNet to semantically relate tags and reduce gaps between low-level features and high-level concepts. Experimental results show the proposed method increases annotation accuracy compared to other methods.
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
Semantic annotation of images is an important research topic on both image understanding and database or web image search. Image annotation is a technique to choosing appropriate labels for images with
extracting effective and hidden feature in pictures. In the feature extraction step of proposed method, we present a model, which combined effective features of visual topics (global features over an image) and regional contexts (relationship between the regions in Image and each other regions images) to automatic
image annotation. In the annotation step of proposed method, we create a new ontology (base on WordNet ontology) for the semantic relationships between tags in the classification and improving semantic gap exist in the automatic image annotation. Experiments result on the 5k Corel dataset show the proposed
method of image annotation in addition to reducing the complexity of the classification, increased accuracy compared to the another methods.
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
Semantic annotation of images is an important research topic on both image understanding and database or web image search. Image annotation is a technique to choosing appropriate labels for images with
extracting effective and hidden feature in pictures. In the feature extraction step of proposed method, we
present a model, which combined effective features of visual topics (global features over an image) and
regional contexts (relationship between the regions in Image and each other regions images) to automatic image annotation.In the annotation step of proposed method, we create a new ontology (base on WordNet ontology) for the semantic relationships between tags in the classification and improving semantic gap exist in the automatic image annotation.Experiments result on the 5k Corel dataset show the proposed
method of image annotation in addition to reducing the complexity of the classification, increased accuracy
compared to the another methods.
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
Semantic annotation of images is an important research topic on both image understanding and database
or web image search. Image annotation is a technique to choosing appropriate labels for images with
extracting effective and hidden feature in pictures. In the feature extraction step of proposed method, we
present a model, which combined effective features of visual topics (global features over an image) and
regional contexts (relationship between the regions in Image and each other regions images) to automatic
image annotation.In the annotation step of proposed method, we create a new ontology (base on WordNet
ontology) for the semantic relationships between tags in the classification and improving semantic gap
exist in the automatic image annotation.Experiments result on the 5k Corel dataset show the proposed
method of image annotation in addition to reducing the complexity of the classification, increased accuracy
compared to the another methods.
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
Semantic annotation of images is an important research topic on both image understanding and databaseor web image search. Image annotation is a technique to choosing appropriate labels for images with extracting effective and hidden feature in pictures. In the feature extraction step of proposed method, we
present a model, which combined effective features of visual topics (global features over an image) and regional contexts (relationship between the regions in Image and each other regions images) to automatic image annotation.In the nnotation step of proposed method, we create a new ontology (base on WordNet ontology) for the semantic relationships between tags in the classification and improving semantic gap exist in the automatic image
annotation.Experiments result on the 5k Corel dataset show the proposed method of image annotation in addition to reducing the complexity of the classification, increased accuracy compared to the another methods.
Generating images using generative adversarial networks based on text descrip...
Modern developments in the fields of natural language processing (NLP) and computer vision (CV) emphasize the increasing importance of generating images from text descriptions. The presented article analyzes and compares two key methods in this area: generative adversarial network with conditional latent semantic analysis (GAN-CLS) and ultra-long transformer network (XLNet). The main components of GAN-CLS, including the generator, discriminator, and text encoder, are discussed in the context of their functional tasks—generating images from text inputs, assessing the realism of generated images, and converting text descriptions into latent spaces, respectively. A detailed comparative analysis of the performance of GAN-CLS and XLNet, the latter of which is widely used in the organic light-emitting diode (OEL) field, is carried out. The purpose of the study is to determine the effectiveness of each method in different scenarios and then provide valuable recommendations for selecting the best method for generating images from text descriptions, taking into account specific tasks and resources. Ultimately, our paper aims to be a valuable research resource by providing scientific guidance for NLP and CV experts.
Text Extraction System by Eliminating Non-Text Regions
Text detection and recognition in scene images or natural images has applications in computer
vision systems like registration number plate detection, automatic traffic sign detection, image retrieval
and help for visually impaired people. Scene text, however, has complicated background, blur image,
partly occluded text, variations in font-styles, image noise and ranging illumination. Hence scene text
recognition could be a difficult computer vision problem. In this paper connected component method
is used to extract the text from background. In this work, horizontal and vertical projection profiles,
geometric properties of text, image binirization and gap filling method are used to extract the text from
scene images. Then histogram based threshold is applied to separate text background of the images.
Finally text is extracted from images.
Content based image retrieval project
This document provides a comprehensive review of recent developments in content-based image retrieval and feature extraction. It discusses various low-level visual features used for image retrieval, including color, texture, shape, and spatial features. It also reviews approaches that fuse low-level features and use local features. Machine learning and deep learning techniques for content-based image retrieval are also summarized. The document concludes by discussing open challenges and directions for future research in this area.
Feature integration for image information retrieval using image mining techni...
This document discusses feature extraction techniques for image information retrieval. It proposes integrating features using image mining to generate a super set of features. It describes extracting primitive features of color, texture, and shape. Color is extracted using histograms in RGB color space. Texture is extracted statistically using co-occurrence matrices and wavelet transforms. Shape is extracted using boundary-based and region-based methods like Canny edge detection. The document asserts that integrating features, such as color and texture or texture and shape, results in better performance than using features individually for image retrieval.
Face Recognition System using Self Organizing Feature Map and Appearance Base...
Face Recognition has develop one of the most effective presentations of image analysis. This area of research is important not only for the applications in human computer interaction, biometric and security but also in other pattern classification problem. To improve face recognition in this system, two methods are used PCA Principal component analysis and SOM Self organizing feature Map .PCA is a subspace projection method is used compress the input face image. SOM method is used to classify DCT based feature vectors into groups to identify if the subject in the input image is "present" or "not present" in the image database. The aim of this system is that input image has to compare with stored images in the database using PCA and SOM method. An image database of 100 face images is evaluated containing 10 subjects and each subject having 10 images with different facial expression. This system is evaluated by measuring the accuracy of recognition rate. This system has been implemented by MATLAB programming. Thaung Yin | Khin Moh Moh Than | Win Tun "Face Recognition System using Self-Organizing Feature Map and Appearance-Based Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26691.pdfPaper URL: https://www.ijtsrd.com/computer-science/cognitive-science/26691/face-recognition-system-using-self-organizing-feature-map-and-appearance-based-approach/thaung-yin
Ijarcet vol-2-issue-7-2287-2291
This document discusses image mining techniques for image retrieval. It provides an overview of the image mining process which involves processing images, extracting features, and mining for information and knowledge. The document then surveys various feature extraction techniques used in image mining, including color, texture, and shape features. It discusses how features like color histograms, textures, and invariant moments can be extracted from images and used for content-based image retrieval. Finally, the document reviews several papers on image mining techniques and how they extract different features from images for applications like digital forensics and image retrieval.
Ijarcet vol-2-issue-7-2287-2291
This document discusses various techniques for image mining. It begins with an introduction to image mining and the typical image mining process. It then discusses several feature extraction techniques used for image mining, including color, texture, and shape features. Color features techniques discussed include color histograms and color space quantization. Texture feature techniques analyzed co-occurrence histograms. Shape feature techniques used edge detection and invariant moments. The document concludes that combining simple, easily extracted features like color, texture and shape provides an efficient approach to image mining.
SIGNIFICANCE OF DIMENSIONALITY REDUCTION IN IMAGE PROCESSING
The aim of this paper is to present a comparative study of two linear dimension reduction methods namely
PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The main idea of PCA is to
transform the high dimensional input space onto the feature space where the maximal variance is
displayed. The feature selection in traditional LDA is obtained by maximizing the difference between
classes and minimizing the distance within classes. PCA finds the axes with maximum variance for the
whole data set where LDA tries to find the axes for best class seperability. The neural network is trained
about the reduced feature set (using PCA or LDA) of images in the database for fast searching of images
from the database using back propagation algorithm. The proposed method is experimented over a general
image database using Matlab. The performance of these systems has been evaluated by Precision and
Recall measures. Experimental results show that PCA gives the better performance in terms of higher
precision and recall values with lesser computational complexity than LDA
Similar to Fusion of pan images ms and preserving spectral quality (20)
A decomposition framework for image denoising algorithms
A decomposition framework for image denoising algorithms
Facial image retrieval on semantic features using adaptive mean genetic algor...
Facial image retrieval on semantic features using adaptive mean genetic algor...
Implementation of Fuzzy Logic for the High-Resolution Remote Sensing Images w...
Implementation of Fuzzy Logic for the High-Resolution Remote Sensing Images w...
C OMPARATIVE S TUDY OF D IMENSIONALITY R EDUCTION T ECHNIQUES U SING PCA AND ...
C OMPARATIVE S TUDY OF D IMENSIONALITY R EDUCTION T ECHNIQUES U SING PCA AND ...
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES
Generating images using generative adversarial networks based on text descrip...
Generating images using generative adversarial networks based on text descrip...
Text Extraction System by Eliminating Non-Text Regions
Text Extraction System by Eliminating Non-Text Regions
Feature integration for image information retrieval using image mining techni...
Feature integration for image information retrieval using image mining techni...
Face Recognition System using Self Organizing Feature Map and Appearance Base...
Face Recognition System using Self Organizing Feature Map and Appearance Base...
SIGNIFICANCE OF DIMENSIONALITY REDUCTION IN IMAGE PROCESSING
SIGNIFICANCE OF DIMENSIONALITY REDUCTION IN IMAGE PROCESSING
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Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
Multi-Class Stress Detection Through Heart Rate Variability A Deep Neural Net...
Multi-Class Stress Detection Through Heart Rate Variability A Deep Neural Network Based Study.
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
Identifying Hot Topic Trends in Streaming Text Data Using News Sequential Evo...
Identifying Hot Topic Trends in Streaming Text Data Using News Sequential Evolution Model Based on Distributed Representations.
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
Fighting Money Laundering With Statistics and Machine Learning.docx
Fighting Money Laundering With Statistics and Machine Learning.
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
Explainable Artificial Intelligence for Patient Safety A Review of Applicatio...
Explainable Artificial Intelligence for Patient Safety A Review of Application in Pharmacovigilance.
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
Ensemble Deep Learning-Based Prediction of Fraudulent Cryptocurrency Transact...
Ensemble Deep Learning-Based Prediction of Fraudulent Cryptocurrency Transactions.
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
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A Comparative Analysis of Sampling Techniques for Click-Through Rate Predicti...
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EMOTION RECOGNITION BY TEXTUAL TWEETS CLASSIFICATION USING VOTING CLASSIFIER ...
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Nature-Based Prediction Model of Bug Reports Based on Ensemble Machine Learni...
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https://bit.ly/Automation_Student_Kickstart
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Fusion of pan images ms and preserving spectral quality
- 1. 2020 – 2021 #13/ 19, 1st Floor, Municipal Colony, Kangayanellore Road, Gandhi Nagar, Vellore – 6. Off: 0416-2247353 Mo: +91 9500218218 / +91 8220150373 Website: www.shakastech.com, Email - id: shakastech@gmail.com, info@shakastech.com Fusion of PAN Images MS and Preserving Spectral Quality Abstract: Image fusion aims at improving spectral information in a fused image as well as adding spatial details to it. Among the existing fusion algorithms, filter-based fusion methods are the most frequently discussed cases in recent publications due to their ability to improve spatial and spectral information of multispectral (MS) and panchromatic (PAN) images. Filter-based approaches extract spatial information from the PAN image and inject it into MS images. Designing an optimal filter that is able to extract relevant and nonredundant information from the PAN image is presented in this letter. The optimal filter coefficients extracted from statistical properties of the images are more consistent with type and texture of the remotely sensed images compared with other kernels such as wavelets. Visual and statistical assessments show that the proposed algorithm clearly improves the fusion quality in terms of correlation coefficient, relative dimensionless global error in synthesis, spectral angle mapper, universal image quality index, and quality without reference, as compared with fusion methods, including improved intensity-hue-saturation, multiscale Kalman filter, Bayesian, improved nonsubsampled contourlet transform, and sparse fusion of image.