Confidence Estimation in Unsupervised Deep Change Vector Analysis

Unsupervised transfer learning-based change detection (CD) methods exploit the feature extraction capability of pretrained networks to distinguish changed pixels from unchanged ones. However, their performance may vary significantly depending on several geographical and model-related aspects. In many applications, it is of utmost importance to provide trustworthy or confident results, even if over a subset of pixels. The core challenge in this problem is to identify changed pixels and confident pixels in an unsupervised manner. To address this, we propose a two-network model—one tasked with mere CD and the other with confidence estimation. While the CD network can be used in conjunction with popular transfer learning-based CD methods such as deep change vector analysis, the confidence estimation network operates similarly to a randomized smoothing model. By ingesting ensembles of inputs perturbed by noise, it creates a distribution over the output and assigns confidence to each pixel’s outcome. The novelty of this work lies in methodologically identifying confident pixels during unsupervised deep transfer learning-based CD, a feature typically absent in these methods, which generally do not offer an indicator of confidence or uncertainty. We tested the proposed method on two different Earth observation sensors: optical and synthetic aperture radar (SAR). The proposed method achieved an increase in the $F1$ score by approximately eight points for the optical dataset and five points for the SAR dataset compared to no confidence estimation.