http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Harpreet Singh,Prabhakar Alok Verma,Sameer Saran 대한공간정보학회 2019 Spatial Information Research Vol.27 No.3
Broadly satellite images are available in two categories (1) high spectral resolution but less spatial resolution (2) high spatial resolution but less spectral resolution. But in certain applications, images with high spatial as well as high spectral resolution are required. To meet such kind of requirement, Image fusion is widely accepted and increasingly being used. In this study satellite image fusion is done using geostatistical methods (cokriging, regression kriging) and non-geostatistical methods (intensity hue saturation, principal component analysis). The study is focused on performing qualitative assessment of selected image fusion techniques. In this study, the primary variable is RGB bands of Landsat 8 Operational Land Imager (OLI) and the panchromatic band is chosen as the second variable. The output of these selected methods is compared to access spectral and spatial quality. Spectral quality is accessed by finding the correlation between the primary variable and the output, however spatial quality is accessed via texture analysis method named entropy. Overall assessment of loss of correlation, luminance distortion, and contrast distortion is done using Image quality index. Correlation index of regression kriging and PCA are comparable whereas entropy and image quality index of fused output is highest in case of regression kriging. Hence regression kriging can be concluded as the best fusion technique out of the compared techniques.