CDNet++: Improved Change Detection with Deep Neural Network Feature Correlation

Abstract

In this paper, we present a deep convolutional neural network (CNN) architecture for segmenting semantic changes between two images. The main objective is to segment changes at the semantic level than detecting background changes, which are irrelevant to the application. The difficulties include seasonal changes, lighting differences, artifacts due to alignment and occlusion. The existing approaches fail to address all the problems together; thus, none of them achieve state-of-the-art performance in three publicly available change detection datasets: VL-CMU-CD [1], TSUNAMI [2] and GSV [2]. Our proposed approach is a simple yet effective method that can handle even adverse challenges. In our approach, we leverage the correlation between high-level abstract CNN features to segment the changes. Compared with several traditional and other deep learning-based change detection methods, our proposed method achieves state-of-the-art performance in all three datasets.