A Deep Feature Retrieved Network for Bitemporal Remote Sensing Image Change Detection
Shizhen Chang, Michael Kopp, and Pedram Ghamisi
The task of bitemporal change detection aims to identify the surface changes of specific scenes at two different points in time. In recent years, we have increasingly witnessed the success of deep learning in a variety of applications in remote sensing, including change detection and monitoring. In this paper, a novel deep feature retrieval neural network architecture for change detection is proposed that uses a trainable associative memory component to exploit potential similarities and connections of the deep features between image pairs. A key ingredient in our novel architecture is the use of a continuous modern Hopfield network component. The proposed method beats the current state-of-the-art on the well-known LEVIR-CD data set.
CEUR Workshop Proceedings, 3207, 2022-07-25.