Anomaly Feature Learning for Unsupervised Change Detection in Heterogeneous Images: A Deep Sparse Residual Model

In this article, we propose a novel and simple automatic model based on multimodal anomaly feature learning in a residual space, aiming at solving the binary classification problem of temporal change detection (CD) between pairs of heterogeneous remote sensing images. The model starts by learning from image pairs the normal existing patterns in the before and after images to come up with a suitable representation of the normal (nonchange) class. To achieve this, we employ a stacked sparse autoencoder trained on a large number of temporal image features (training data) in an unsupervised manner. To classify pixels of new unseen image-pairs, the built anomaly detection model reconstructs the input from its representation in the latent space. First, the probe (new) image (i.e., the bitemporal heterogeneous image pair as the input request) is encoded in this compact normal space from a stacked hidden representation. The reconstruction error is computed using the L2 norm in what we call the residual normal space. In which, the nonchange patterns are characterized by small reconstruction errors as a normal class while the change patterns are quantified by high reconstruction errors categorizing the abnormal class. The dichotomic (changed/unchanged) classification map is generated in the residual space by clustering the reconstructed errors using a Gaussian mixture model. Experimental results on different real heterogeneous images, reflecting a mixture of imaging and land surface CD conditions, confirm the robustness of the proposed anomaly detection model.