Varying weighted spatial quality assessment for high resolution satellite image pan-sharpening

IF 1.8 Q3 REMOTE SENSING

International Journal of Image and Data Fusion Pub Date : 2021-05-03 DOI:10.1080/19479832.2021.1921059

S. Mehravar, Farzaneh Dadras Javan, F. Samadzadegan, A. Toosi, Armin Moghimi, Reza Khatami, A. Stein

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引用次数: 2

Abstract

ABSTRACT This paper focuses on spatial quality assessment of pan-sharpened imagery that contains valuable information of input images. Its aim is to show that fusion functions respond differently to different types of landscapes. It compares a quality assessment of an object-level procedure with that of a conventional pixel-level-based procedure which assigns uniform quality scores to all image pixels of pan-sharpened images. To do so, after performing a series of pan-sharpening evaluations, a weighted procedure for spatial quality assessments of pan-sharpening products, allocating spatially varying weight factors to the image pixels proportional to their level of spatial information content is proposed. All experiments are performed using five high-resolution image datasets using fusion products produced by three common pan-sharpening algorithms. The datasets are acquired from WorldView-2, QuickBird, and IKONOS. Experimental results show that the spatial distortion of fused images for the class vegetation cover exceeds that of man-made structures, reaching more than 4% in some cases. Our procedure can preclude illogical fidelity estimations occurring when pan-sharpened images contain different land covers. Since particular image structures are of high importance in remote sensing applications, our procedure provides a purpose-oriented estimation of the spatial quality for pan-sharpened images in comparison with conventional procedures.

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高分辨率卫星图像泛锐化的变加权空间质量评价

摘要:本文主要研究了包含输入图像有价值信息的泛锐化图像的空间质量评估。其目的是表明融合功能对不同类型的景观有不同的反应。它比较了对象级程序的质量评估与传统的基于像素级的程序的质量评估，该程序为泛锐化图像的所有图像像素分配统一的质量分数。为此，在进行了一系列泛锐化评价之后，提出了一种泛锐化产品空间质量评价的加权程序，将空间变化的权重因子分配给与其空间信息含量水平成比例的图像像素。所有实验使用5个高分辨率图像数据集进行，使用三种常见泛锐化算法产生的融合产物。数据集来自WorldView-2、QuickBird和IKONOS。实验结果表明，该类植被覆盖融合图像的空间畸变超过人工结构的空间畸变，在某些情况下达到4%以上。我们的程序可以排除当泛锐化图像包含不同的土地覆盖时发生的不合逻辑的保真度估计。由于特定的图像结构在遥感应用中非常重要，因此与传统方法相比，我们的程序提供了面向目的的泛锐化图像空间质量估计。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Image and Data Fusion REMOTE SENSING-

CiteScore

5.00

自引率

0.00%

发文量

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.).