  Unsupervised transfer learning-based change detection methods exploit the
feature extraction capability of pre-trained networks to distinguish changed
pixels from the 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
change detection and the other with confidence estimation. While the change
detection network can be used in conjunction with popular transfer
learning-based change detection 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. We
tested the proposed method on three different Earth observation sensors:
optical, Synthetic Aperture Radar, and hyperspectral sensors.
