Satellite imagery consists of highly complex spatial features that make it difficult for traditional image processing techniques to use them for classification tasks. In this paper, we propose a novel method to use these hidden fractal information that naturally exist in these satellite images. We have designed a fractal-based descriptor which generates a scale invariant fractal image for easier fractal-based pattern extraction and uses it as an added feature vector that is combined with the original image and fed into a VGG-16 deep learning architecture which successfully classifies even low-resolution satellite images with an f1-score of 0.78.

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