Abstract
Based on HJ-1A HSI data and Landsat-8 OLI data, RS image fusion experiments were carried out using three fusion methods: principal component (PC) transform, Gram Schimdt (GS) transform and nearest neighbor diffusion (NND) algorithm. Four evaluation indexes, namely mean, standard deviation, information entropy and average gradient, were selected to evaluate the fusion results from the aspects of image brightness, clarity and information content. Wetland vegetation was classified by spectral angle mapping (SAM) to find a suitable fusion method for wetland vegetation information extraction. The results show that PC fusion image contains the largest amount of information, GS fusion image has certain advantages in brightness and clarity maintenance, and NND fusion method can retain the spectral characteristics of the image to the maximum extent; Among the three fusion methods, PC transform is the most suitable for wetland information extraction. It can retain more spectral information while improving spatial resolution, with classification accuracy of 89.24% and Kappa coefficient of 0.86.
Highlights
With the development of remote sensing technology, a large number of high-resolution satellites have appeared, such as IRS-1C/1D, HJ-1A/B, IKONOS, Quickbird, Orbview, GF-1/2
The results show that the fusion of 543 bands of landsat-5 and the third principal component of SPOT-5 are most suitable for the extraction of wetland information
The PCA transform, Gram Schimdt (GS) transform and nearest neighbor diffusion (NND) algorithm results of HJ-1A HIS image are shown in the figure (Fig. 3.)
Summary
With the development of remote sensing technology, a large number of high-resolution satellites have appeared, such as IRS-1C/1D, HJ-1A/B, IKONOS, Quickbird, Orbview, GF-1/2. These satellites with different band ranges, spatial resolution and spectral resolution have led to an increasing number of remote sensing data of different types in the same region (Wang, Li, & Li, 2001). How to integrate these remote sensing data and obtain the most applicable remote sensing image has become a difficulty in remote sensing technology. The results show that the fusion of 543 bands of landsat-5 and the third principal component of SPOT-5 are most suitable for the extraction of wetland information. Wei, Li, Tan, & Xun (2011) used the fusion image of NDVI (normalized difference vegetation index) extracted -36°CHRIS high spectrum image and 0°CHRIS image, adopted the method of spectral Angle mapping (SAM) to extract wetland vegetation type information, the experimental results show that the accuracy of vegetation classification in hyperspectral images is improved. Zhu (2012) used the fusion image of hyperspectral image of HJ-1A and CCD image, and extracted the typical vegetation distribution in Zhalong region by using spectral Angle mapping, and obtained higher classification results
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