Affiliation:
1. Hubei Subsurface Multi-Scale Imaging Key Laboratory, School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
2. School of Computer Science, Hubei University of Technology, Wuhan 430074, China
3. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Abstract
Spatial and spectral information are essential sources of information in remote sensing applications, and the fusion of panchromatic and multispectral images effectively combines the advantages of both. Due to the existence of two main classes of fusion methods—component substitution (CS) and multi-resolution analysis (MRA), which have different advantages—mixed approaches are possible. This paper proposes a fusion algorithm that combines the advantages of generalized intensity–hue–saturation (GIHS) and non-subsampled shearlet transform (NSST) with principal component analysis (PCA) technology to extract more spatial information. Therefore, compared with the traditional algorithms, the algorithm in this paper uses PCA transformation to obtain spatial structure components from PAN and MS, which can effectively inject spatial information while maintaining spectral information with high fidelity. First, PCA is applied to each band of low-resolution multispectral (MS) images and panchromatic (PAN) images to obtain the first principal component and to calculate the intensity of MS. Then, the PAN image is fused with the first principal component using NSST, and the fused image is used to replace the original intensity component. Finally, a fused image is obtained using the GIHS algorithm. Using the urban, plants and water, farmland, and desert images from GeoEye-1, WorldView-4, GaoFen-7 (GF-7), and Gaofen Multi-Mode (GFDM) as experimental data, this fusion method was tested using the evaluation mode with references and the evaluation mode without references and was compared with five other classic fusion algorithms. The results showed that the algorithms in this paper had better fusion performances in both spectral preservation and spatial information incorporation.
Funder
National Natural Science Foundation of China
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference29 articles.
1. Panchromatic and Multi-spectral Fusion Method Combined with Adaptive Gaussian Filter and SFIM Model;Wang;Acta Geod. Cartogr. Sin.,2018
2. Pansharpening With Matting Model;Kang;IEEE Trans. Geosci. Remote Sens.,2014
3. Hyperspectral and multispectral image fusion via variational tensor subspace decomposition;Xing;IEEE Geosci. Remote Sens. Lett.,2021
4. Dual-collaborative fusion model for multispectral and panchromatic image fusion;Xing;IEEE Trans. Geosci. Remote Sens.,2020
5. Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis;Aiazzi;IEEE Trans. Geosci. Remote Sens.,2002
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