Verfahren und Systeme zur Domänenübergreifenden Änderungsdetektion einer Region Aufgrund eines Ereignisses

Details

Veröffentlichungs-Nr.

EP4814895

Anmeldetag

3. März 2026

Veröffentlichung

30. September 2026

Rechtsraum

EP

Abstract

The disclosure relates generally to methods and systems for cross-domain based change detection of region due to an event. Conventional techniques on cross-domain change detection (CDCD) rely on transformation-based approaches involving two tasks: image translation from one modal to another modal (SAR to optical) and then performing the change detection (CD) in one of the translated modals (for example, in SAR or optical). The methods and system of the present disclosure propose a deep-learning (DL) architecture called ReFUjetNet leverages generic embeddings from the extensively pre-trained domain adaptive foundation model alongside locally trained embeddings. The ReFUjetNet efficiently fuses these embeddings and employs a Kolmogorov-Arnold Network (KAN)-based convolutional neural network (CNN) classifier to generate a binary change matrix, which is then joined with another binary change matrix derived from the Gabor jet-based dissimilarity checker, resulting in the final binary change map.

Anmelder

Firma

Tata Consultancy Services Limited

Land

🇮🇳 Indien