An unsupervised heterogeneous change detection method based on image translation network and post-processing algorithm

Abstract

ABSTRACT The change detection (CD) of heterogeneous remote sensing images is an important but challenging task. The difficulty is to obtain the change information by directly comparing the different statistical characteristics of the images acquired by different sensors. This paper proposes an unsupervised method for heterogeneous image CD based on an image domain transfer network. First, an attention mechanism is added to the Cycle-generative adversarial networks (Cycle-GANs) to obtain a more consistent feature expression by transferring bi-temporal heterogeneous images to the common domain. The Euclidean distance of the corresponding pixels is calculated in the common domain to form a difference map, and a threshold algorithm is applied to get a rough change map. Finally, the proposed adaptive Discrete Cosine Transform (DCT) algorithm reduces the noise introduced by false detection, and the final change map is obtained. The proposed method is verified on three real heterogeneous CD datasets and compared with the current state-of-the-art methods. The results show that the proposed method is accurate and robust for performing heterogeneous CD tasks.