DCSRL: a change detection method for remote sensing images based on deep coupled sparse representation learning

ABSTRACT

Change detection methods include model-driven methods and data-driven methods. Model-driven methods are rigorous in theory but have difficulties in establishing accurate models for complex problems. Data-driven methods have strong learning abilities, but it is difficult to interpret their mechanism. In order to comprehensively utilize their advantages to highlight changes in multi-temporal remote sensing images. We proposed a change detection method for remote sensing images based on deep coupled sparse representation learning, in which the coupled sparse representation learning is implicitly expressed by convolutional neural networks (CNNs). A coupled sparse representation learning method is first proposed, and then it is unfolded into a CNNs network, the coupled sparse coefficient and dictionary are learned from the training data. Unlike fully data-driven CNNs, auxiliary coupled sparse representation is utilized to guide CNNs to identify the changed areas. Finally, the experiments on two datasets verified the validity of the proposed method.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work described in this letter is supported by the Science and Technology Project of Taizhou (Nos. 20ny13, 2003gy15) and the Zhejiang Provincial Natural Science Foundation of China (Nos. LQ21F020001, LQ21A010001)