Unsupervised Multiclass Change Detection for Multimodal Remote Sensing Data

We propose an unsupervised methodology for multi-class change detection (CD) in multimodal remote sensing data fused using the Kronecker product formalism. The method utilizes the compressed change vector analysis (C2VA) on the fully vectorized change matrices. The multimodal case is demonstrated using dual-frequency full-polarimetric Synthetic Aperture Radar (SAR) data obtained by EMISAR over the Foulum agricultural area. The change types are investigated using ground truth data for the growth of various crops. The work showcases the capability of the Kronecker product-based CD formalism beyond conventional scalar change indices.