Evaluation of pansharpening algorithms in support of earth observation based rapid-mapping workflows

Witharana, Chandi; Civco, Daniel L.; Meyer, Thomas H.

doi:10.1016/j.apgeog.2012.10.008

Cited by 42 publications

(31 citation statements)

References 48 publications

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“…Generally, image fusion methods aim to improve the spatial information of the original MS images by using the spatial details of the VHR PAN image in situations where we cannot obtain ideal VHR MS images due to the technical limitations of certain satellite sensor [26]. Most of image fusion methods are based on a general protocol, which can be broadly summarized in two steps: (1) extraction of high-frequency spatial information from the PAN image; and (2) injection of such spatial details into the resized MS images by exploiting different models [27]. A general fusion framework protocol can be defined as [28] …”

Section: Gram-schmidt Adaptive (Gsa) Image Fusionmentioning

confidence: 99%

Image Fusion-Based Change Detection for Flood Extent Extraction Using Bi-Temporal Very High-Resolution Satellite Images

Byun¹,

Han

Chae

2015

Remote Sensing

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Abstract:Change detection based on satellite images acquired from an area at different dates is of widespread interest, according to the increasing number of flood-related disasters. The images help to generate products that support emergency response and flood management at a global scale. In this paper, a novel unsupervised change detection approach based on image fusion is introduced. The approach aims to extract the reliable flood extent from very high-resolution (VHR) bi-temporal images. The method takes an advantage of the spectral distortion that occurs during image fusion process to detect the change areas by flood. To this end, a change candidate image is extracted from the fused image generated with bi-temporal images by considering a local spectral distortion. This can be done by employing a universal image quality index (UIQI), which is a measure for local evaluation of spectral distortion. The decision threshold for the determination of changed pixels is set by applying a probability mixture model to the change candidate image based on expectation maximization (EM) algorithm. We used bi-temporal KOMPSAT-2 satellite images to detect the flooded area in the city of N′djamena in Chad. The performance of the proposed method was visually and quantitatively compared with existing change detection methods. The results showed that the proposed method achieved an overall accuracy (OA = 75.04) close OPEN ACCESSRemote Sens. 2015, 7 10348 to that of the support vector machine (SVM)-based supervised change detection method. Moreover, the proposed method showed a better performance in differentiating the flooded area and the permanent water body compared to the existing change detection methods.

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Section: Gram-schmidt Adaptive (Gsa) Image Fusionmentioning

confidence: 99%

Image Fusion-Based Change Detection for Flood Extent Extraction Using Bi-Temporal Very High-Resolution Satellite Images

Byun¹,

Han

Chae

2015

Remote Sensing

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“…A recent review of these techniques is given in [24], and evaluation and comparison of selected fusion algorithms are presented in [25][26][27][28][29][30][31][32]. A higher level image fusion for remote sensing, i.e., feature fusion, can be found in [33].…”

Section: A Short Overview Of Existing Pan-sharpening Algorithmsmentioning

confidence: 99%

A Parallel Computing Paradigm for Pan-Sharpening Algorithms of Remotely Sensed Images on a Multi-Core Computer

2014

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Pan-sharpening algorithms are data-and computation-intensive, and the processing performance can be poor if common serial processing techniques are adopted. This paper presents a parallel computing paradigm for pan-sharpening algorithms based on a generalized fusion model and parallel computing techniques. The developed modules, including eight typical pan-sharpening algorithms, show that the framework can be applied to implement most algorithms. The experiments demonstrate that if parallel strategies are adopted, in the best cases the fastest times required to finish the entire fusion operation (including disk input/output (I/O) and computation) are close to the time required to directly read and write the images without any computation. The parallel processing implemented on a workstation with two CPUs is able to perform these operations up to 13.9 times faster than serial execution. An algorithm in the framework is 32.6 times faster than the corresponding version in the ERDAS IMAGINE software. Additionally, no obvious differences in the fusion effects are observed between the fusion results of different implemented versions.

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“…Plenty of studies have dealt with a quality assessment of fusion algorithms [1,3,7,16,19,[27][28][29][30][31][32][33][34][35]. The crucial point of sharpened algorithm validation assessment is the existence of reference image.…”

Section: Validationmentioning

confidence: 99%

Testing a Modified PCA-Based Sharpening Approach for Image Fusion

et al. 2016

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Abstract:Image data sharpening is a challenging field of remote sensing science, which has become more relevant as high spatial-resolution satellites and superspectral sensors have emerged. Although the spectral property is crucial for mineral mapping, spatial resolution is also important as it allows targeted minerals/rocks to be identified/interpreted in a spatial context. Therefore, improving the spatial context, while keeping the spectral property provided by the superspectral sensor, would bring great benefits for geological/mineralogical mapping especially in arid environments. In this paper, a new concept was tested using superspectral data (ASTER) and high spatial-resolution panchromatic data (WorldView-2) for image fusion. A modified Principal Component Analysis (PCA)-based sharpening method, which implements a histogram matching workflow that takes into account the real distribution of values, was employed to test whether the substitution of Principal Components (PC1-PC4) can bring a fused image which is spectrally more accurate. The new approach was compared to those most widely used-PCA sharpening and Gram-Schmidt sharpening (GS), both available in ENVI software (Version 5.2 and lower) as well as to the standard approach-sharpening Landsat 8 multispectral bands (MUL) using its own panchromatic (PAN) band. The visual assessment and the spectral quality indicators proved that the spectral performance of the proposed sharpening approach employing PC1 and PC2 improve the performance of the PCA algorithm, moreover, comparable or better results are achieved compared to the GS method. It was shown that, when using the PC1, the visible-near infrared (VNIR) part of the spectrum was preserved better, however, if the PC2 was used, the short-wave infrared (SWIR) part was preserved better. Furthermore, this approach improved the output spectral quality when fusing image data from different sensors (e.g., ASTER and WorldView-2) while keeping the proper albedo scaling when substituting the second PC.

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Evaluation of pansharpening algorithms in support of earth observation based rapid-mapping workflows

Cited by 42 publications

References 48 publications

Image Fusion-Based Change Detection for Flood Extent Extraction Using Bi-Temporal Very High-Resolution Satellite Images

Image Fusion-Based Change Detection for Flood Extent Extraction Using Bi-Temporal Very High-Resolution Satellite Images

A Parallel Computing Paradigm for Pan-Sharpening Algorithms of Remotely Sensed Images on a Multi-Core Computer

Testing a Modified PCA-Based Sharpening Approach for Image Fusion

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