A Pan-Sharpening Method Based on Evolutionary Optimization and IHS Transformation

Chen, Yingxia; Zhang, Guixu

doi:10.1155/2017/8269078

Cited by 9 publications

(7 citation statements)

References 14 publications

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“…After that, IAIHS [6] considers the gradient information of PAN and MS images, builds a new weighting matrix, and obtains better spatial information fusion capability than the AIHS method. The EIHS [7] method considers the relationship between fused and given images by objective function, and obtains the best control parameters for rebuilding the highresolution MS image according to the optimization algorithm. MIHS [8] transforms the PS problem into a multiobjective optimization problem by showing an objective function.…”

Section: Related Workmentioning

confidence: 99%

“…The IHS method runs with fast efficiency and low computational complexity. There are many improved methods based on IHS, such as the generalized IHS (GIHS) [3], matting model [4], adaptive IHS (AIHS) [5], and improved adaptive IHS (IAIHS) [6], evolutionary optimization IHS (EIHS) [7], and multiobjective IHS (MIHS) [8] methods, in addition to the band-dependent spatial detail (BDSD) [9,10] method, the adaptive fusion method based on component replacement [11], clustering method based on mixed pixels [12], and the combination of IHS and PCA [13]. The CS method has the advantage of fast computational efficiency, but problems such as spectral distortion usually arise because of the difference in PAN images and the inclusion of spatially detailed parts.…”

Section: Introductionmentioning

confidence: 99%

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A Two-Stage Pansharpening Method for the Fusion of Remote-Sensing Images

et al. 2022

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The pansharpening (PS) of remote-sensing images aims to fuse a high-resolution panchromatic image with several low-resolution multispectral images for obtaining a high-resolution multispectral image. In this work, a two-stage PS model is proposed by integrating the ideas of component replacement and the variational method. The global sparse gradient of the panchromatic image is extracted by variational method, and the weight function is constructed by combining the gradient of multispectral image in which the global sparse gradient can provide more robust gradient information. Furthermore, we refine the results in order to reduce spatial and spectral distortions. Experimental results show that our method had high generalization ability for QuickBird, Gaofen-1, and WorldView-4 satellite data. Experimental results evaluated by seven metrics demonstrate that the proposed two-stage method enhanced spatial details subjective visual effects better than other state-of-the-art methods do. At the same time, in the process of quantitative evaluation, the method in this paper had high improvement compared with that other methods, and some of them can reach a maximal improvement of 60%.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Two-Stage Pansharpening Method for the Fusion of Remote-Sensing Images

et al. 2022

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“…The panchromatic band has wide spectral coverage in the visible and near-infrared wavelength regions. Pansharpening is aimed at producing a synthesized multispectral image with an enhanced spatial resolution equivalent to that of a panchromatic band [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Image Fusion Techniques Using Satellite Pour l’Observation de la Terre (SPOT) 6 Satellite Imagery

2020

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Preservation of spectral and spatial information is an important requirement for most quantitative remote sensing applications. In this study, we use image quality metrics to evaluate the performance of several image fusion techniques to assess the spectral and spatial quality of pansharpened images. We evaluated twelve pansharpening algorithms in this study; the Local Mean and Variance Matching (IMVM) algorithm was the best in terms of spectral consistency and synthesis followed by the ratio component substitution (RCS) algorithm. Whereas the IMVM and RCS image fusion techniques showed better results compared to other pansharpening methods, it is pertinent to highlight that our study also showed the credibility of other pansharpening algorithms in terms of spatial and spectral consistency as shown by the high correlation coefficients achieved in all methods. We noted that the algorithms that ranked higher in terms of spectral consistency and synthesis were outperformed by other competing algorithms in terms of spatial consistency. The study, therefore, concludes that the selection of image fusion techniques is driven by the requirements of remote sensing application and a careful trade-off is necessary to account for the impact of scene radiometry, image sharpness, spatial and spectral consistency, and computational overhead.

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“…Jian et al 26 utilized bilateral filter (BFGF) to decompose source image and utilized guided filter to refine the HMS image. Chen and Zhang 27 used an evolutionary algorithm to optimize fused result based on IHS transformation.…”

Section: Introductionmentioning

confidence: 99%

Pansharpening multispectral remote‐sensing images with guided filter for monitoring impact of human behavior on environment

Yang

et al. 2018

Concurrency and Computation

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Summary Human behavior would lead to a significant impact on the environment. By monitoring the environment, we can indirectly monitor human behavior. Remote sensing (RS) technology provides a large number of multispectral (MS) images. When combining the Internet of things (IoT) technology, those images can be used for human behavioral monitoring. However, due to the limitation of the optical sensors embedded in satellites, the spatial resolution of MS image is relatively low, which poses a huge problem for further understanding these images. Pansharpening, also known as multisensor image fusion, aims to sharp an MS image to a high‐resolution multisensor image (HMS) by integrating a corresponding high‐resolution panchromatic (PAN) image. By doing so, the redundancy among big data can be effectively reduced. Traditional Intensity‐Hue‐Saturation (IHS)–based methods often suffer from spectral distortion. To address this problem, a novel pansharpening method is proposed in this paper. Different from those traditional IHS methods, the proposed method first decomposes MS and PAN into high‐frequency‐component (HFC) and low‐frequency‐component (LFC), respectively. Then, the guided filter (GF) is utilized to enhance the spectral information on the detail map. Furthermore, the detail map is refined according to the adaptive coefficients for each band of MS. By performing experiments, we demonstrate the proposed method can obtain satisfying results in both visual quality and object assessment among existing methods.

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A Pan-Sharpening Method Based on Evolutionary Optimization and IHS Transformation

Cited by 9 publications

References 14 publications

A Two-Stage Pansharpening Method for the Fusion of Remote-Sensing Images

A Two-Stage Pansharpening Method for the Fusion of Remote-Sensing Images

Comparison of Image Fusion Techniques Using Satellite Pour l’Observation de la Terre (SPOT) 6 Satellite Imagery

Pansharpening multispectral remote‐sensing images with guided filter for monitoring impact of human behavior on environment

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