A General Framework for Change Detection Using Multimodal Remote Sensing Data

A general framework for change detection (CD) is proposed to analyze multi-modal remotely sensed data utilizing the Kronecker product between two representations (vectors or matrices). The method sensor independent and provides comparable results techniques that exist specific sensors. fusion technique a pixel-level approach incorporates inputs from different modalities, rendering enriched representation. Thus, hybridization procedure helps assimilate multi-sensor information in meaningful manner. novel index () defined case. This then used quantify bi-temporal data. article explores usability, consistency, robustness of multimodal framework, including index, with proper validation on cases: (1) dual-frequency (C- L-band) full-polarimetric Danish EMISAR data, (2) dual-polarimetric SAR multispectral Sentinel constellation satellites. Detailed analysis using extensive ground truth are presented establish framework.