Human visual system consistent quality assessment for remote sensing image fusion

被引:32
|
作者
Liu, Jun [1 ]
Huang, Junyi [3 ]
Liu, Shuguang [2 ]
Li, Huali [4 ]
Zhou, Qiming [1 ,3 ]
Liu, Junchen [5 ]
机构
[1] Chinese Acad Sci, Shenzhen Inst Adv Technol, Guangzhou 518055, Guangdong, Peoples R China
[2] Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
[3] Hong Kong Baptist Univ, Dept Geog, Hong Kong, Hong Kong, Peoples R China
[4] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China
[5] Tianjin Inst Surveying & Mapping, Tianjin 300381, Peoples R China
关键词
Image fusion; Quality assessment; Human visual system; Gaussian scale space; Spatial quality index; Spectral quality index; MULTIRESOLUTION; ALGORITHMS;
D O I
10.1016/j.isprsjprs.2014.12.018
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Quality assessment for image fusion is essential for remote sensing application. Generally used indices require a high spatial resolution multispectral (MS) image for reference, which is not always readily available. Meanwhile, the fusion quality assessments using these indices may not be consistent with the Human Visual System (HVS). As an attempt to overcome this requirement and inconsistency, this paper proposes an HVS-consistent image fusion quality assessment index at the highest resolution without a reference MS image using Gaussian Scale Space (GSS) technology that could simulate the HVS. The spatial details and spectral information of original and fused images are first separated in GSS, and the qualities are evaluated using the proposed spatial and spectral quality index respectively. The overall quality is determined without a reference MS image by a combination of the proposed two indices. Experimental results on various remote sensing images indicate that the proposed index is more consistent with HVS evaluation compared with other widely used indices that may or may not require reference images. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:79 / 90
页数:12
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