A Review of Remote Image Fusion
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The fusion of multispectral image and panchromatic image is an important technique in the domain of image fusion. The desired result is to obtain an image with the spatial resolution and quality of the panchromatic image and the spectral resolution and quality of the multispectral image in order to the next application. An introduction to an overview of image fusion methods are given, which include basic fusion methods and different models for injecting spatial detail information, as well as schemes that combine standard methods with wavelet transforms. Then, the advantages and disadvantages of the methods are given.Keywords:
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The information content of a single image is mainly limited by the spatial and spectral resolution of the imaging system. Current imaging systems offer a trade-off between high spatial and high spectral resolutions. No single system offers both of these characteristics. In order to obtain the both characteristics in a single image, that is high spatial and spectral resolutions, image fusion technique can be employed. Image fusion is a process of combining two or more images into an image. It can extract features from source images, and provide more information than one image can. In this research, we propose a novel method for multimodality remote sensing image fusion. As a possible remedy for this problem we propose a technique for the fusion of Panchromatic and Multispectral images based on Curvelet transform. This paper introduces new application of the curvelet in multispectral remote sensing image fusion. The methodological approaches proposed in this paper result in merged images with improved quality with respect to those obtained by IHS, DWT, wavelet à trous algorithm and ridgelet and curvelet transform. Results show proposed method preserves more spectral features with less spatial distortion.
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Recent developments in remote sensing technologies have provided various images with high spatial and spectral resolutions. However, multispectral images have low spatial resolution and panchromatic images have low spectral resolution. Therefore, image fusion techniques are necessary to improve the spatial resolution of spectral images by injecting spatial details of high-resolution panchromatic images. The objective of image fusion is to provide useful information by improving the spatial resolution and the spectral information of the original images. The fusion results can be utilized in various applications, such as military, medical imaging, and remote sensing. This paper addresses two issues in image fusion: i) image fusion method and ii) quality analysis of fusion results. First, a new contourlet-based image fusion method is presented, which is an improvement over the wavelet-based fusion. This fusion method is then applied to a case study to demonstrate its fusion performance. Fusion framework and scheme used in the study are discussed in detail. Second, quality analysis for the fusion results is discussed. We employed various quality metrics in order to analyze the fusion results both spatially and spectrally. Our results indicate that the proposed contourlet-based fusion method performs better than the conventional wavelet-based fusion methods.
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Panchromatic film
Sensor Fusion
Fusion rules
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The multi-spectral and panchromatic image fusion is discussed. A new wavelet transform based fusion method is presented after analyzing the basic principles of remote sensing imaging and fusion target. The experiments show that the fusion method can efficiently fuse the multi-spectral and panchromatic image with a satisfactory result.
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Satellite image fusion plays a key role in providing researchers with more precise and abundant information by injecting spatial details of a panchromatic image into a multispectral image. However, pre-processing of source images is required prior to the fusion process. Without well-performed pre-processing, the main fusion process cannot produce high-quality fusion results because incorrect registration of the source images can significantly degrade the fusion results either spatially or spectrally. Therefore, we analyze the effect of preprocessing on satellite image fusion in this study, using four differently pre-processed data sets. In addition, fusion framework and fusion scheme are based on the contourlet transform which outperforms the wavelet transform in terms of representing salient features of the source images. As a result of both spatial and spectral analyses, we conclude that co-registration and pre-processing of the source images can significantly improve the quality of the fusion results.
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Sensor Fusion
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A new wavelet reconstruction implementation for image fusion is proposed in this study. This reconstruction implementation is derived from Mallat's algorithms. Using the reconstruction implementation, components for a panchromatic image can easily be modified through various image enhancement or filtering operations in order to improve the consistency with a multispectral image, prior to the fusion of images. In addition, more detailed components from the panchromatic image can be extracted and fused with the multispectral image thereby improving its spatial resolution. Visual and statistical analysis indicates that the new wavelet based image fusion method performs better at improving the spatial resolution while preserving the spectral characteristics of the multispectral imagery, when compared to conventional wavelet based image fusion methods.
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Multiresolution analysis
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The definition of multi source remote sensing information fusion is described. Image fusion methods are proposed by different application purposes, which include high resolution panchromatic image fuse with low resolution multi spectral image and SAR image fuse with optical image. The wavelet transformation method is introduced to image fusion. These fusion algorithms are based on spectral characteristics and geometrical features, using wavelet technique to improve the whole processing. Wavelet analysis can be used to decompose the image orthogonal while keeping the information of the original image. The results show that the fusion methods based on spectral information characteristics and geometrical features facilitate to integrate all necessary data and to improve the quality of visual interpretation. The details of high resolution image are added to the low resolution multi spectral images for visual effect improvement while keeping the spectral characteristics of the original image.
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Pan sharpening is the process of fusion of panchromatic and multispectral image to obtain an output image of high spatial and spectral resolution. It is very important for various remote sensing applications such as image segmentation studies, image classification, temporal change detection etc. The present work demonstrates the application of Empirical Wavelet Transform for the fusion of panchromatic image and multispectral image by simple average fusion rule. The Proposed method is experimented on panchromatic and multispectral images captured by high resolution earth observation satellites such as GeoEye-1, QuickBird, WorldView-2 and World View-3. The effectiveness of our proposed method is evaluated by visual perception and quantitative assessment measures. The experimental analysis shows that the proposed method performs comparable to the existing fusion algorithms such as Multi-resolution Singular Value Decomposition and Discrete Wavelet Transform.Keywords: Discrete Wavelet Transform, Empirical Wavelet Transform, Image Fusion, Multi-resolution Singular Value Decomposition, Pan sharpening, Quality metrics
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The advancement in technology had led to the requirement for a single image with high spatial and good spectral resolution in various applications such as remote sensing, surveillance and medical diagnosis. Most of the technology and equipment available has already reached the level of saturation in providing such convincing image or data. Image fusion techniques allow the integration of different information sources. From a pair of hyper-spectral image with low spatial and a pan image with high spatial resolutions, the aim is to synthesize a single image with highest spatial resolution and spectral content. However, apart from the standard image fusion techniques, in this paper we propose multi-sensor image fusion using empirical wavelet transform. In this image fusion scheme, the empirical wavelet transforms of the input images are appropriately combined, and the new image is obtained by taking the inverse empirical wavelet transform of the fused wavelet coefficients. With the necessity of multi-sensor data in many fields such as remote sensing, medical imaging, machine vision, military applications, sensor fusion has emerged as an upcoming and promising research area. The main objective of image fusion is to reconstruct new images that are more convincing for human visual perception, highly suitable for object detection and target recognition.
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Stationary wavelet transform
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Data Fusion is the process of combining multiple image layers into a single composite image during the procedure of the remote sensing data handling. It is commonly used to enhance the spatial resolution of multispectral datasets using higher spatial resolution panchromatic data. Firstly, the platform and data of remote sensing is generalized. Based on the model of wavelet transforms, remote sensing image fusion is discussed by wavelet transition. Meantime, the flow chart is also given as the detailed explanation. With the fusion method, higher spatial resolution SPOT panchromatic image is fused with the Landsat TM multispectral image. A case study is also presented to achieve the result of the data fusion with wavelet transition as a practical application while its feasibility is proven.
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Sensor Fusion
Multispectral pattern recognition
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An adaptive multi-spectral and panchromatic remote sensing images fusion method is discussed. High spatial resolution panchromatic image can provide detail geometric features, while multi-spectral image can provide very good spectral information. A high spatial resolution multi-spectral image can be obtained by combining these two images. Multi-wavelet transform based fusion method is better than ordinary wavelet transform based fusion method, IHS fusion method and PCA fusion method. Different decomposition levels of multi-wavelet transform may lead to different images fusion performance. An adaptive multi-wavelet transform based fusion scheme is presented in this paper. Experiment results show that the adaptive multi-wavelet transform based fusion method can determine the best multi-wavelet decomposition level and provide the best fusion results.
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Stationary wavelet transform
Second-generation wavelet transform
Lifting Scheme
Harmonic wavelet transform
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