Change detection in multi-pass imagery may be subject to unknowable and/or unstructured variations. This challenges supervised approaches, since annotations and examples may not exist to explain what is observed. This paper proposes two components that jointly characterize this class of change. The first component is a self-supervised clustering network that is data-driven and can be applied at single or multiple-pass datacubes at once. The second component builds on several recently introduced measures of information entropy in images that are applied to the clusters detected in the first stage. This framework is versatile and can be used in an end-to-end pipeline. It consumes raw data and computes qualitative and quantitative metrics on the data without using priors.
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Change Detection Based on Ordinal Entropy of Self-Supervised Segmentation
Semantic Scholar · Computer Science · 2023
Abstract
Change detection in multi-pass imagery may be subject to unknowable and/or unstructured variations. This challenges supervised approaches, since annotations and examples may not exist to explain what is observed. This paper proposes two components that jointly characterize this class of change. The first component is a self-supervised clustering network that is data-driven and can be applied at single or multiple-pass datacubes at once. The second component builds on several recently introduced measures of information entropy in images that are applied to the clusters detected in the first stage. This framework is versatile and can be used in an end-to-end pipeline. It consumes raw data and computes qualitative and quantitative metrics on the data without using priors.