锐化
黑森矩阵
人工智能
图像分辨率
计算机视觉
全色胶片
计算机科学
特征检测(计算机视觉)
特征(语言学)
多光谱图像
模式识别(心理学)
图像(数学)
数学
图像处理
哲学
语言学
应用数学
作者
Pengfei Liu,Liang Xiao,Jun Zhang,Bushra Naz
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2016-04-01
卷期号:54 (4): 2235-2253
被引量:36
标识
DOI:10.1109/tgrs.2015.2497966
摘要
In this paper, we propose a new spatial-Hessian-feature-guided variational model for pan-sharpening, which aims at obtaining a pan-sharpened multispectral (MS) image with both high spatial and spectral resolutions from a low-resolution MS image and a high-resolution panchromatic (PAN) image. First, we assume that the low-resolution MS image corresponds to the blurred and downsampled version of the high-resolution pan-sharpened MS image. Since the pan-sharpened MS image and the PAN image are two images of the same scene, the pan-sharpened MS image shares similar geometric correspondence with the PAN image. To this end, the geometric correspondence between the PAN image and the pan-sharpened MS image is learnt as spatial position consistency by interest point detection. Second, a new vectorial Hessian Frobenius norm term based on the image spatial Hessian feature is presented to constrain the special correspondence between the PAN image and the pan-sharpened MS image, as well as the intracorrelations among different bands of the pan-sharpened MS image. Based on these assumptions, a novel variational model is proposed for pan-sharpening. Accordingly, an efficient algorithm for the proposed model is designed under the operator splitting framework. Finally, the results on both simulated data and real data demonstrate the effectiveness of the proposed method in producing pan-sharpened results with high spectral quality and high spatial quality.
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