A One-Class Approach to Detect Super-Resolution Satellite Imagery with Spectral Features

Abstract

Satellite imagery has a vital role in many applications and several techniques exist to enhance its quality. An example is given by image Super Resolution (SR), which aims at increasing the pixel resolution to recover lost high-frequency details. Due to their importance, satellite images are also a target for malicious manipulations. In such a context, knowing if an image has been super-resolved (SRV) is crucial for guaranteeing the correct usage of this kind of data. In this paper, we propose a pipeline for detecting satellite images SRV through State-Of-The-Art (SOTA) techniques based on Convolutional Neural Networks. Our solution employs an anomaly detection algorithm, i.e., a one-class Support Vector Machine trained on Fourier spectral features of native high-resolution images. Even though we never process SRV samples during training, the experiments show that we can reject images generated through different SOTA techniques, and our solution is robust against image compression.