A variational pan-sharpening algorithm to enhance the spectral and spatial details

IF 1.8 Q3 REMOTE SENSING International Journal of Image and Data Fusion Pub Date : 2020-11-02 DOI:10.1080/19479832.2020.1838629

Rajesh Gogineni, A. Chaturvedi, Daya Sagar B S

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引用次数: 6

Abstract

ABSTRACT Pan-sharpening is a remote sensing image fusion technique that generates a high-resolution multispectral (HRMS) image on combining a low resolution multispectral (MS) image and a panchromatic (PAN) image. In this paper, a new optimisation model is proposed for pan-sharpening. The proposed model consists of three terms: (i) a data synthesis fidelity term formulated on inferring the relationship between source MS image and fused image to preserve the spectral information, (ii) a total generalised variation-based prior term to inject the significant spatial details from PAN image to pan-sharpened image, and (iii) a spectral distortion reduction term that exploits the correlation between multispectral image bands. To solve the resultant convex optimisation problem, an efficient and convergence guaranteed operator splitting framework based on the alternating direction method of multipliers (ADMM) algorithm is formulated. Finally, the proposed model is experimentally validated using full-resolution and reduced-resolution data. The pan-sharpened outcomes exhibit the potential of the proposed method in enhancing the spatial and spectral quality.

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一种变分泛锐化算法，增强光谱和空间细节

泛锐化是一种将低分辨率多光谱(MS)图像与全色(PAN)图像结合生成高分辨率多光谱(HRMS)图像的遥感图像融合技术。本文提出了一种新的泛锐化优化模型。该模型由三个术语组成:(i)通过推断源MS图像与融合图像之间的关系来建立数据合成保真度术语，以保留光谱信息;(ii)基于总广义变化的先验术语，将PAN图像中的重要空间细节注入泛锐化图像;(iii)利用多光谱图像波段之间的相关性来减小光谱失真术语。为了解决由此产生的凸优化问题，提出了一种基于乘法器交替方向法(ADMM)算法的高效且保证收敛的算子分裂框架。最后，利用全分辨率和降分辨率数据对该模型进行了实验验证。泛锐化的结果显示了该方法在提高空间和光谱质量方面的潜力。

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来源期刊

International Journal of Image and Data Fusion REMOTE SENSING-

CiteScore

5.00

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0.00%

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期刊介绍： International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).