Nowadays, advanced sensor technology allow us to measure different aspects of the objects on the Earth’s surface. In particular, hyperspectral image (e.g. captured by the PROBA satellite) provides spectral information to differentiate objects made of the different materials, but typically with lower spatial resolution. QuickBird Satellite Sensor, on the other hand, provides very high spatial resolution panchromatic image, with very limited spectral information. Fusion of different sensor modalities has proven very effective in numerous remote sensing applications. In this paper, we present a method to efficiently enhance the spatial quality of the PROBA hyperspectral image by fusing a high spatial resolution QuickBird image. Our method first decorrelates the PROBA hyperspectral image by PCA (Principal Component Analysis). Then we only transfer the spatial structures from QuickBird Pan image to the first few principal components by Guided Filter. We finally get the enhanced PROBA hyperspectral images by inversing the PCA. Experimental results on PROBA hyperspectral and QuickBird images demonstrate the potential of the proposed method. Compared to some popular pansharpening methods, our approach performs better on both spectral and spatial preservations. Both the method’s details and the results of a comprehensive test will be presented at Proba-V Symposium 2016.
|Publication status||Published - 2016|
|Event||Proba-V Symposium 2016 - Ghent, Belgium|
Duration: 26 Jan 2016 → 28 Jan 2016
|Conference||Proba-V Symposium 2016|
|Period||26/01/16 → 28/01/16|
- hyperspectral image
- principal component analysis