A comparison of superresolution reconstruction methods for multi-angle CHRIS/Proba images

Compact High Resolution Imaging Spectrometer onboard the Project for On-board Autonomy, or CHRIS/Proba, represents a new generation of satellite images that provide different acquisitions of the same scene at five different angles. Given the hyperspectral-oriented waveband configuration of the CHRIS images, the scope of its application would be much wider if the present 17m nadir resolution could be refined. This paper presents the results of three superresolution methods applied to multiangular CHRIS/Proba data. The CHRIS images were preprocessed and then calibrated into reflectance using the method described in [1][2]. Automatic registration using an intensity variation approach described in [3] was implemented for motion estimation. Three methods, namely non-uniform interpolation and de-convolution [4], iterative back-projection [5], and total variation [6] are examined. Quantitative measures including peak signal to noise ratio [7], structural similarity [8], and edge stability [9], are used for the evaluation of the image quality. To further examine the benefit of multi-frame superresolution methods, a single-frame superresolution method of bicubic resampling was also applied. Our results show that a high resolution image derived from superresolution methods enhance spatial resolution and provides substantially more image details. The spectral profiles of selected land covers before and after the application of superresolution show negligible differences, hinting the use of superresolution algorithm would not degrade the capability of the data set for classification. Among the three methods, total variation gives the best performance in all quantitative measures. Visual inspections find good results with total variation and iterative back-projection approaches. The use of superresolution algorithms, however, is complex as there are many parameters. In this paper, most of the parameter settings were tuned manually or decided empirically.

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