Heterogeneous image change detection using Deep Canonical Correlation Analysis

Abstract

Cross-sensor change detection is nowadays of paramount importance for earth observation applications. Most current change detection techniques are based on homogeneous input images. Due to the detailed and complementary spatial and spectral information, heterogeneous images change detection has become an active research topic. Change detection models need effective feature representations to estimate changes of interest. Although great progress has been made, existing approaches mainly focus on shallow models, which only extracting handcrafted low-level features. To this end, this paper proposes a novel heterogeneous change detection method using deep canonical correlation analysis (DCCA). Specifically, the two heterogeneous images are transformed via a deep neural network, and they are projected in the common latent space in the output layer. Experiments on the commonly used homogenous and heterogeneous image datasets demonstrate the superiority of the proposed method compared with the traditional approaches.