UNSUPERVISED MULTICLASS CHANGE DETECTION FOR MULTIMODAL REMOTE SENSING DATA

被引：0
|
作者：
Chirakkal, Sanid [1 ]
Bovolo, Francesca [2 ]
Misra, Arundhati [1 ]
Bruzzone, Lorenzo [3 ]
Bhattacharya, Avik [4 ]
机构：
[1] Space Applicat Ctr ISRO, Ahmadabad 380015, Gujarat, India
[2] Fdn Bruno Kessler, I-38123 Trento, Italy
[3] Univ Trento, I-38123 Trento, Italy
[4] Indian Inst Technol, Mumbai 400076, Maharashtra, India
来源：
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022) | 2022年
关键词：
Multi-modal data; Change Vector Analysis; C(2)VA; Dual-frequency PolSAR;
D O I：
10.1109/IGARSS46834.2022.9883211
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
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 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.
引用
收藏
页码：3223 / 3226
页数：4
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