Joint Frequency-Spatial Domain Network for Remote Sensing Optical Image Change Detection

Abstract

Change detection for remote sensing images involves detecting regional surface changes of interest between two images taken of the same geographical area but at different times. In image processing, the spatial domain uses grayscale values to describe an image. The frequency is directly related to the spatial change rate, so the frequency domain can be intuitively associated with patterns of intensity variations in the image. These two domains provide different perspectives for image interpretation. Most existing deep-learning-based methods formulate change detection as a pixel-wise binary classification problem and utilize various strategies to extract information in the spatial domain. However, they rarely pay attention to the rich information in the frequency domain. To address this problem, we propose an end-to-end joint frequency-spatial domain network (JFSDNet) to implement remote sensing optical image change detection. Specifically, we introduce frequency information into the change detection to supplement the loss of image details caused by down-sampling. In addition, we employ a frequency selection module to adaptively discriminate and choose frequency clues by reducing the complexity of the frequency features. The JFSDNet is applied to two publicly available datasets: the CDD dataset and the LEVIR-CD dataset. Compared with other methods, both visual interpretation and quantitative assessment confirmed that our proposed method achieved a favorable performance.