Change Detection in SAR Images Using Deep Learning Methods

SAR data allow regular monitoring of target areas since their acquisition is not affected by weather or light condition problems. This characteristic makes them optimal for civil protection tasks, such as earthquake damage assessment, where quick responses in any weather and light conditions are needed. Change detection (CD) methods address this application by identifying changes over an analyzed area using bi-temporal or multi-temporal SAR images. These methods detect the changes using features that provide useful information about the changes. In the last years, CD methods exploiting deep learning (DL) models have become popular because of their capability to learn features from the input data. Many DL CD methods exploit supervised models to identify changes using labeled bi-temporal or multi-temporal data to train the model. However, the gathering of labeled multi-temporal data is challenging. Transfer learning methods avoid the supervised training and the labeled data gathering by exploiting pre-trained models as feature extractors for CD tasks. They achieve good results when the target images are similar to the images used to pre-train the model. We focus this chapter on the unsupervised DL CD methods that exploit unsupervised DL models to extract feature maps. Unsupervised DL models automatically learn features from unlabeled samples during the training. The feature maps retrieved from these models are used to detect changed areas in multi-temporal images and handle the speckle noise.