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Journal ArticleDOI

Image Fusion in Remote Sensing Applications: A Review

18 Jun 2015-International Journal of Computer Applications (Foundation of Computer Science (FCS))-Vol. 120, Iss: 10, pp 22-32
TL;DR: 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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Journal ArticleDOI
TL;DR: A comprehensive survey of multi-scale and non-multi-scale decomposition-based image fusion methods in detail is demonstrated and would form basis for stimulating and nurturing advanced research ideas in image fusion.
Abstract: Image fusion is a well-recognized and a conventional field of image processing. Image fusion provides an efficient way of enhancing and combining pixel-level data resulting in highly informative data for human perception as compared with individual input source data. In this paper, we have demonstrated a comprehensive survey of multi-scale and non-multi-scale decomposition-based image fusion methods in detail. The reference-based and non-reference-based image quality evaluation metrics are summarized together with recent trends in image fusion. Several image fusion applications in various fields have also been reported. It has been stated that though a lot of singular fusion techniques seemed to have given optimum results, the focus of researchers is shifting toward amalgamated or hybrid fusion techniques, which could harness the attributes of both multi-scale and non-multi-scale decomposition methods. Toward the end, the review is concluded with various open challenges for researchers. Thus, the descriptive study in this paper would form basis for stimulating and nurturing advanced research ideas in image fusion.

127 citations


Cites background from "Image Fusion in Remote Sensing Appl..."

  • ...Where Kr (i, j) and Kf (i, j) are the image pixel values of the reference and the fused image respectively [255]....

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  • ...MS i is the mean radiance value of theMS image for the ith band [255]....

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  • ...The Q4 index can take up values from 0 to 1 [255]....

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Journal ArticleDOI
TL;DR: It is concluded that there is scope for further research of fusion of SAR and optical images due to various microwave and optical sensors with the improved resolution being launched regularly.

121 citations

Journal ArticleDOI
TL;DR: The Sentinel-2 satellite currently provides freely available multispectral bands at relatively high spatial resolution but does not acquire the panchromatic band as discussed by the authors, which is needed to improve the resolution.
Abstract: The Sentinel-2 satellite currently provides freely available multispectral bands at relatively high spatial resolution but does not acquire the panchromatic band. To improve the resolution ...

86 citations


Cites methods from "Image Fusion in Remote Sensing Appl..."

  • ...Like the BT, it is one of the most often used and computationally efficient methods but is limited to three input multispectral bands and sometimes distorts spectral values (Pandit and Bhiwani 2015)....

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  • ...Five standard pansharpening methods (Brovey, IHS, PCA, P + XS, and Wavelet) were adapted to upgrade the resolution of 20 m bands to 10 m using selected or synthesized bands instead of a panchromatic band....

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  • ...The CS methods involve transformations, such as principal component analysis (PCA) (Chavez, Sides, and Anderson 1991), or a spectral transformation, such as intensity– hue–saturation (IHS) transformation (Carper, Lillesand, and Kiefer 1990)....

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  • ...The selection process was constrained by the fact that traditional image fusion methods (e.g. BT, IHS) are limited to a maximum of three input bands of a lower resolution at a time, whereas Sentinel-2 provides six bands at 20 m resolution for fusion, which required them to be divided into two groups (Figure 1)....

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  • ...Conversely, when the higher resolution bands were synthesized or 8, wavelet had slightly lower correlation than the BT, PCA, and IHS methods....

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Journal ArticleDOI
TL;DR: Pan-sharpening methods are commonly used to synthesize multispectral and panchromatic images and a wide range of algorithms are investigated, including 41 methods investigated, which indicate that MRA-based methods performed better in terms of spectral quality, whereas most Hybrid-based method had the highest spatial quality and CS- based methods had the lowest results both spectrally and spatially.
Abstract: Pan-sharpening methods are commonly used to synthesize multispectral and panchromatic images. Selecting an appropriate algorithm that maintains the spectral and spatial information content of input images is a challenging task. This review paper investigates a wide range of algorithms, including 41 methods. For this purpose, the methods were categorized as Component Substitution (CS-based), Multi-Resolution Analysis (MRA), Variational Optimization-based (VO), and Hybrid and were tested on a collection of 21 case studies. These include images from WorldView-2, 3 & 4, GeoEye-1, QuickBird, IKONOS, KompSat-2, KompSat-3A, TripleSat, Pleiades-1, Pleiades with the aerial platform, and Deimos-2. Neural network-based methods were excluded due to their substantial computational requirements for operational mapping purposes. The methods were evaluated based on four Spectral and three Spatial quality metrics. An Analysis Of Variance (ANOVA) was used to statistically compare the pan-sharpening categories. Results indicate that MRA-based methods performed better in terms of spectral quality, whereas most Hybrid-based methods had the highest spatial quality and CS-based methods had the lowest results both spectrally and spatially. The revisited version of the Additive Wavelet Luminance Proportional Pan-sharpening method had the highest spectral quality, whereas Generalized IHS with Best Trade-off Parameter with Additive Weights showed the highest spatial quality. CS-based methods generally had the fastest run-time, whereas the majority of methods belonging to MRA and VO categories had relatively long run times.

75 citations

Journal ArticleDOI
TL;DR: In this paper, the authors examined the history and progress of various geospatial techniques applied to monitor and evaluate karst vegetation conditions and reviewed the techniques used to as well.
Abstract: The karst region in southwestern China, one of the largest continuous karst areas in the world, is special for its high landscape heterogeneity, unique hydrology, high endemism among vegetation species and high intensity of human disturbance. The region had experienced severe degradation through karst rocky desertification (KRD) between the 1950s and 1990s. Starting in the late 1990s, various levels of the Chinese government conducted several ecological projects to recover degraded karst ecosystems. It was reported that the implementation of these projects had been successful in facilitating the recovery of karst vegetation in many areas. However, global climate changes may compromise the efficacy of recovery. Geospatial techniques had been employed to map and monitor karst ecosystem conditions during the recovery process. We examined the history and progress of the various geospatial techniques applied to monitor and evaluate karst vegetation conditions. In addition, we reviewed the techniques used to as...

52 citations

References
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Book
01 Jan 1986

3,039 citations

Journal ArticleDOI
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.

1,532 citations

Journal ArticleDOI
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.
Abstract: Pansharpening aims at fusing a multispectral and a panchromatic image, featuring the result of the processing with the spectral resolution of the former and the spatial resolution of the latter. In the last decades, many algorithms addressing this task have been presented in the literature. However, the lack of universally recognized evaluation criteria, available image data sets for benchmarking, and standardized implementations of the algorithms makes a thorough evaluation and comparison of the different pansharpening techniques difficult to achieve. In this paper, the authors attempt to fill this gap by providing a critical description and extensive comparisons of some of the main state-of-the-art pansharpening methods. In greater details, several pansharpening algorithms belonging to the component substitution or multiresolution analysis families are considered. Such techniques are evaluated through the two main protocols for the assessment of pansharpening results, i.e., based on the full- and reduced-resolution validations. Five data sets acquired by different satellites allow for a detailed comparison of the algorithms, characterization of their performances with respect to the different instruments, and consistency of the two validation procedures. In addition, the implementation of all the pansharpening techniques considered in this paper and the framework used for running the simulations, comprising the two validation procedures and the main assessment indexes, are collected in a MATLAB toolbox that is made available to the community.

980 citations


"Image Fusion in Remote Sensing Appl..." refers background or methods in this paper

  • ...From a practical point of view, the perfect alignment between the interpolated version of the MS and the PAN images should be assured, to avoid the loss of meaning for this quality index [9] [48]....

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  • ...The main drawback of RMSE is that errors in each band are not related to the mean value of the band itself [6] [9] [42] [48]....

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  • ...Since ERGAS is composed by a sum of RMSE values, smaller ERGAS indicates better fusion results and its optimal value is 0 [9] [34] [35] [39] [44] [48]....

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  • ...The Q4 index takes values in [0,1] with one being the best value [4] [9]....

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  • ...in [9] focused some important points as: the lack of universally recognized evaluation criteria, unavailability of image data sets for benchmarking and absence of standardized implementations of the algorithms to make a thorough evaluation and comparison of the different pansharpening methods....

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Journal ArticleDOI
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.
Abstract: There are many image fusion methods that can be used to produce high-resolution multispectral images from a high-resolution panchromatic image and low-resolution multispectral images Starting from the physical principle of image formation, 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 Using the GIF method, it is shown that the pixel values of the high-resolution multispectral images are determined by the corresponding pixel values of the low-resolution panchromatic image, the approximation of the high-resolution panchromatic image at the low-resolution level Many of the existing image fusion methods, including, but not limited to, intensity-hue-saturation, Brovey transform, principal component analysis, high-pass filtering, high-pass modulation, the a/spl grave/ trous algorithm-based wavelet transform, and multiresolution analysis-based intensity modulation (MRAIM), are evaluated and found to be particular cases of the GIF method The performance of each image fusion method is theoretically analyzed based on how the corresponding low-resolution panchromatic image is computed and how the modulation coefficients are set An experiment based on IKONOS images shows that there is consistency between the theoretical analysis and the experimental results and that the MRAIM method synthesizes the images closest to those the corresponding multisensors would observe at the high-resolution level

793 citations

Journal ArticleDOI
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.
Abstract: It is of great interest in exploiting texture information for classification of hyperspectral imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich texture information of HSI is proposed. The proposed framework employs local binary patterns (LBPs) to extract local image features, such as edges, corners, and spots. Two levels of fusion (i.e., feature-level fusion and decision-level fusion) are applied to the extracted LBP features along with global Gabor features and original spectral features, where feature-level fusion involves concatenation of multiple features before the pattern classification process while decision-level fusion performs on probability outputs of each individual classification pipeline and soft-decision fusion rule is adopted to merge results from the classifier ensemble. Moreover, the efficient extreme learning machine with a very simple structure is employed as the classifier. Experimental results on several HSI data sets demonstrate that the proposed framework is superior to some traditional alternatives.

574 citations