Log-Based Transformation Feature Learning for Change Detection in Heterogeneous Images

With the rapid development of remote sensing technology, how to accurately detect changes that have occurred on the land surface has been a critical task, particularly when images come from different satellite sensors. In this letter, we propose an unsupervised change detection method for heterogeneous synthetic aperture radar (SAR) and optical images based on the logarithmic transformation feature learning framework. First, the logarithmic transformation is applied to the SAR image that aims to achieve similar statistical distribution properties as the optical image. Then, high-level feature representations can be learned from the transformed image pair via joint feature extraction, which are used to select reliable samples for training a neural network classifier. When it is trained well, a robust change map can be obtained, thus identifying changed regions accurately. The experimental results on three real heterogeneous data sets demonstrate the effectiveness and superiority of the proposed method compared with other existing state-of-the-art approaches.