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Locally Linear Embedding based Example Learning for Pan-sharpening
Qingjie Liu; Lining Liu; Yunhong Wang; Zhaoxiang Zhang
Conference NameInternational Conference on Pattern Recognition
Source PublicationICPR 2012
Conference Date11-15 November 2012
Conference PlaceTsukuba, Japan
AbstractIn this paper, a novel example based method is proposed to solve the remote sensing pan-sharpening problem, utilizing an implicit non-parametric learning framework. The high resolution (HR) and down-sampled panchromatic (PAN) images are used to train the high/low resolution patch pair dictionaries. Based on the perspective of locally linear embedding (LLE), every patch in each multi-spectral (MS) image band is modeled by its K nearest neighbors in patch set generated from low resolution (LR) PAN image, and this model can be generalized to the HR condition. The intended HR MS patch is reconstructed from the corresponding neighbors in HR PAN patches. Finally, the HR MS images are recovered by stitching these patches together. Two datasets of images acquired by Quick-Bird satellite are used to test the performance of the proposed method. Experimental results show that the proposed method performs well in preserving spectral information as well as spatial details.
KeywordSpatial Resolution Image Reconstruction Principal Component Analysis Training Remote Sensing Vectors
Document Type会议论文
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Qingjie Liu,Lining Liu,Yunhong Wang,et al. Locally Linear Embedding based Example Learning for Pan-sharpening[C],2012.
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