A Probabilistic Model Based on Bipartite Convolutional Neural Network for Unsupervised Change Detection

This article presents a probabilistic model based on a bipartite convolutional architecture for unsupervised change detection. We aim to develop a robust change detection method that can adapt to different types of data and scenarios for multitemporal coregistered remote sensing images of the same spatial resolution. On the premise of coregistration, unsupervised change detection usually suffers from the distinct appearances (different intensities or data structures) of the same object in multitemporal images, such as images obtained in different climatic conditions (season, illumination, and so on), and by different and even heterogeneous sensors. Since change detection in heterogeneous images can also adapt to other scenarios, many methods have been proposed recently focusing on such data, but most of them are limited by the need for labeled data or by specific assumptions. With the excellent and flexible feature learning capability of neural networks, we model the change detection into a Gibbs probabilistic model based on a bipartite neural network. The model is driven by an energy function defined as the squared feature distance, which is the core of change detection. Via optimizing the model, the difference degree of each pixel is automatically obtained for further identification. The probabilistic model learns to capture the distribution in an unsupervised way. Therefore, the proposed method can adapt to various scenarios without being trained by labeled data. Experiments on different types of data and scenarios demonstrate the superiority of the proposed method.