Unsupervised change detection using hierarchical convolutional autoencoder

Change detection (CD) benefits of the capability of deep-learning (DL) methods of exploiting complex temporal behaviors in a large amount of data. Unsupervised CD DL methods are preferred since they do not require labeled data. Unsupervised CD methods use autoencoders (AE) or convolutional AE (CAE) for CD. However, features provided by the CAE hidden layers tend to degrade the geometrical information during the encoding. To mitigate this effect, we propose an unsupervised CD exploiting a multilayer CAE trained by a hierarchical loss function. This loss function guarantees a better trade-off between noise reduction and preservation of geometrical details at each hidden layer of the CAE. On the contrary to standard CAE, the proposed novel loss function considers input/output specular pairs of multiple hidden layers. These layers are analyzed by considering encoder/decoder pairs that work at corresponding geometrical resolution and show similar spatialcontext information. Single-layer loss functions are defined by comparing the specular corresponding encoder and decoder pairs then aggregated to design a multilayer loss function. The proposed hierarchical loss function allows for a layer-by-layer control of the training and improvement of the reconstruction quality of the hidden layers that better preserves the geometrical details while reducing noise. The CD is performed by processing bi-temporal remote sensing images with the CAE. A detail-preserving multi-scale CD process exploits the most informative features for bi-temporal images to compute the change map. Preliminary experimental results conducted on a couple of Landsat-8 multitemporal images acquired before and after a fire near Granada, Spain of July 8th, 2015, provided promising results.