Canonical Correlation Analysis for Misaligned Satellite Image Change Detection
This addresses the issue of misaligned data in multi-view analysis for applications such as satellite image change detection, but it is incremental as it builds on existing CCA methods.
The paper tackles the problem of alignment errors in canonical correlation analysis (CCA) by proposing an alignment-agnostic variant that optimizes a constrained maximization problem with data correlation and context regularization, resulting in a method that is highly effective and resilient to mispairing errors in multi-view tasks like satellite image change detection.
Canonical correlation analysis (CCA) is a statistical learning method that seeks to build view-independent latent representations from multi-view data. This method has been successfully applied to several pattern analysis tasks such as image-to-text mapping and view-invariant object/action recognition. However, this success is highly dependent on the quality of data pairing (i.e., alignments) and mispairing adversely affects the generalization ability of the learned CCA representations. In this paper, we address the issue of alignment errors using a new variant of canonical correlation analysis referred to as alignment-agnostic (AA) CCA. Starting from erroneously paired data taken from different views, this CCA finds transformation matrices by optimizing a constrained maximization problem that mixes a data correlation term with context regularization; the particular design of these two terms mitigates the effect of alignment errors when learning the CCA transformations. Experiments conducted on multi-view tasks, including multi-temporal satellite image change detection, show that our AA CCA method is highly effective and resilient to mispairing errors.