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An Evaluation of Ecotope Classification using Superresolution Images Derived from Chris/Proba Data

This paper discusses the application of superresolution (SR) image reconstruction on multi-angle Chris/Proba images. The goal is to increase the spatial resolution of Chris/Proba images, with 18 bands from 0.4-1.0 mum in the hope to obtain a better ecotope classification. The SR approach chosen for this study is Total Variation, an iterative method which models the relationship between the desired high resolution image and the low resolution images, with the following components: a subsampling factor, a point spread function, an estimated rotation and shift, and a regularization term. This regularization approach is fast in implementation and flexible in handling noise. Efficient gradient descent methods can be used to find the desired high resolution image. The spatial resolution of the original image is improved from 25 m to 12 m using Total Variation. Subjective assessment through visual interpretation shows substantial improvement in detail. A tree-based ensemble classifier Random Forest is used for the classification of 18 ecotopes. Overall accuracy shows a 10% increase with the SR derived Chris/Proba images, compared with a classification based on the original imagery. Our results demonstrate that SR methods can improve spatial detail of multi-angle images, and subsequently classification accuracy.