模式识别(心理学)
遥感
代表(政治)
融合
图像(数学)
K-SVD公司
作者
Wang Jun,Jinye Peng,Xiaoyue Jiang,Xiaoyi Feng,Zhou Jianhong
出处
期刊:Journal of remote sensing
[China Science Publishing & Media Ltd.]
日期:2017-06-01
卷期号:38 (12): 3564-3585
被引量:8
标识
DOI:10.1080/01431161.2017.1302106
摘要
Remote-sensing image fusion aims to obtain a multispectral MS image with a high spatial resolution, which integrates spatial information from the panchromatic Pan image and with spectral information from the MS image. Sparse representation SR has been recently used in remote-sensing image fusion method, and can obtain superior results to many traditional methods. However, the main obstacle is that the dictionary is generated from high resolution MS images HRMS, which are difficult to acquire. In this article, a new SR-based remote-sensing image fusion method with sub-dictionaries is proposed. The image fusion problem is transformed into a restoration problem under the observation model with the sparsity constraint, so the fused HRMS image can then be reconstructed by a trained dictionary. The proposed dictionary for image fusion is composed of several sub-dictionaries, each of which is constructed from a source Pan image and its corresponding MS images. Therefore, the dictionary can be constructed without other HRMS images. The fusion results from QuickBird and IKONOS remote-sensing images demonstrate that the proposed method gives higher spatial resolution and less spectral distortion compared with other widely used and the state-of-the-art remote-sensing image fusion methods.
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