Change detection in image pairs has traditionally been a binary process, reporting either “Change” or “No Change.” In this paper, we present LambdaNet, a novel deep architecture for performing pixel-level directional change detection based on a four class classification scheme. LambdaNet successfully incorporates the notion of “directional change” and identifies differences between two images as “Additive Change” when a new object appears, “Subtractive Change” when an object is removed, “Exchange” when different objects are present in the same location, and “No Change.” To obtain pixel annotated change maps for training, we generated directional change class labels for the Change Detection 2014 dataset. Our tests illustrate that LambdaNet would be suitable for situations where the type of change is unstructured, such as change detection scenarios in satellite imagery.
Change detection from ground vehicles has various applications, such as the detection of roadside Improvised Explosive Devices (IEDs). Although IEDs are hidden, they are often accompanied by visible markers, which can be any kind of object. Because of this, any suspicious change in the environment compared to an earlier moment in time, should be detected. Little work has been published to solve this ill-posed problem using deep learning. This paper shows the feasibility of applying convolutional neural networks (CNNs) to HD video, to accurately predict the presence and location of such markers in real time. The network is trained for the detection of pixel-level changes in HD video, compared to an earlier reference recording. We investigate Siamese CNNs in combination with an encoder-decoder architecture and introduce a modified double-margin contrastive loss function, to achieve pixel-level change detection results. Our dataset consists of seven pairs of challenging real-world recordings with geo-tagged test objects. The proposed network architecture is capable of comparing two images of 1920×1440 pixels in 150 ms on a GTX1080Ti GPU. The proposed network significantly outperforms state-of-the-art networks and algorithms on our dataset in terms of F-1 score, on average by 0.28.