Iterative structure transformation and conditional random field based method for unsupervised multimodal change detection

作者：

Highlights：

• A structure transformation is proposed to transform the heterogeneous images to the same differential domain.

• A CRF model is designed for multimodal change detection by incorporating the change information based unary potential, local spatially-adjacent neighbor information and global spectrally-similar neighbor information based pairwise potentials.

• An iterative framework is used to combine the structure transformation and CRF segmentation to improve the accuracy.

摘要

•A structure transformation is proposed to transform the heterogeneous images to the same differential domain.•A CRF model is designed for multimodal change detection by incorporating the change information based unary potential, local spatially-adjacent neighbor information and global spectrally-similar neighbor information based pairwise potentials.•An iterative framework is used to combine the structure transformation and CRF segmentation to improve the accuracy.

论文关键词：Unsupervised change detection,KNN graph,Image transformation,Multimodal,Conditional random field

论文评审过程：Received 20 November 2021, Revised 5 June 2022, Accepted 9 June 2022, Available online 13 June 2022, Version of Record 16 June 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108845