An Unsupervised Change Detection Technique Based on a Super-Resolution Convolutional Autoencoder

Deep Learning (DL) methods are widely used for Change Detection (CD) in multi-temporal Remote Sensing (RS) images. The recently reported unsupervised DL CD methods alleviate the problem of the labeled data collection affecting the supervised ones. Many of them exploit the DL models (e.g., Convolutional Autoencoder (CAE)) as a feature extractor and use the retrieved features to detect the changes. However, these features do not efficiently preserve the geometrical details, and they do not optimize the selection of informative features for change detection. We propose an unsupervised DL CD method that exploits the features extracted by a CAE trained with a super-resolution-based loss function. The loss function allows the CAE to be trained to reconstruct the spatial information thus generating features preserving the geometrical details. The proposed method exploits a feature selection based on the Structured Similarity Index (SSIM) to perform a texture analysis and chooses couples of bi-temporal features providing relevant information about changes. We tested the proposed method on a couple of bi-temporal Landsat-8 images representing a burned area near Granada, Spain.