Unsupervised Multiclass Change Detection on 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 (C 2 VA) on the fully vectorized change matrices. The multimodal case is demonstrated using dual-frequency full-polarimetric Syn-thetic Aperture Radar (SAR) data obtained by EMISAR over the Foulum agricultural area. The change types are inves-tigated 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.