Classification of Low-Resolution Satellite Images Using Fractal Augmented Descriptors

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.