Multiscale Spatial-Channel Transformer Architecture Search for Remote Sensing Image Change Detection
Mengxuan Zhang, Long Liu, Zhikun Lei, Kun Ma, Jie Feng, Zhao Liu, Licheng Jiao
Abstract
Deep learning-based approaches play important roles and achieve impressive performances in remote sensing image change detection. Most of the networks are designed by the researchers with rich experiences. It is difficult to design the fixed networks with universal and good performance on various datasets. Regarding this issue, this study presents a transformer architecture search for remote sensing image change detection. The transformer architectures can be designed automatically by a two-stage transformer architecture search. The first stage can search for the effective combinations of attention mechanisms, while the second stage can identify the suitable combinations of multiscale modules. A gradient-based optimization method is employed for enabling the stable and efficient transformer architecture search. The experiments on various change detection datasets can verify the effectiveness of this work.