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RISS 인기검색어
- 검색키워드
- 저자 : Vijay Solanky (검색결과 2 건)
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Pixel-level image fusion techniques in remote sensing: a review
Vijay Solanky,S. K. Katiyar 대한공간정보학회 2016 Spatial Information Research Vol.24 No.4
In the recent years remote sensing image fusion of satellite images has become a popular tool for analyzing different features presented on satellite images. We have analyzed only pixel-level image fusion techniques in this paper, which integrates a low-resolution multispectral (MS) image and high-resolution panchromatic (PAN) image to produce a more informative image than any of the single image. Generally, a PAN image is having a better spatial resolution, while the MS image is having a better spectral resolution than PAN image, due to this trade-off between MS and PAN image resolutions, it could be difficult to preserve spectral and spatial resolution in a single image without a fusion technique. In this paper, we have reviewed most popular and recent image fusion techniques and implemented them on Cartosat-1, RESOURCESAT-2, LANDSAT-8 data set. Results obtained from each method by visual analysis and quantitative measures indicated that UNB fusion algorithm outperforms other techniques used in this paper.
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Vijay Solanky,Kandrika Sreenivas,Sunil Kumar Katiyar 대한공간정보학회 2019 Spatial Information Research Vol.27 No.1
Image fusion is being used since last two to three decades in remote sensing for improving visual appearance of coarse resolution imagery using fine spatial resolution data. The resultant outputs are being used successfully in various applications such as image classification, feature extraction, digital change detection, and many more including multi-temporal and multi-scale change detection. Acceptability of a fusion method for a particular application depends upon various factors; one of them is quality of fused image. In this research paper the four different image fusion techniques namely Ehlers, IHS fusion, Brovey and FuzeGo have been evaluated using IRS-P6 (Cartosat-1) and RESOURCESAT-2 (LISS-IV) images of Bhopal city, India. Quality of fusion results is assessed by performing visual analysis between fused image and multispectral (MS) image along with statistical analysis. Visual comparison is done based on better visibility of different land cover features such as roads, buildings, water body and sharpness of edges present in image. For statistical evaluation of fusion process, six statistical parameters i.e. standard deviation (SD), correlation coefficient (CC), entropy/ noise, RMSE and ERGAS have been used. In addition to these traditional statistical measures, Z-test is used for combined assessment of fusion techniques. Visual comparisons of fusion results obtained for test site have shown that FuzeGo algorithm has given comparatively better results than other algorithms. Statistical parameter CC, SD are found highest for IHS method, RMSE, and ERGAS are found highest for Brovey method and least noise is added by FuzeGo algorithm. Overall visual analysis and Z-test indicates that FuzeGo has given better results which are followed by Ehlers as compare to other methods.
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