Dual-Task Framework For Change Detection In Remote Sensing Image Time Series

Change detection (CD) is one of the essential tasks in Remote Sensing applications. Deep Learning (DL) methods for CD are typically categorized as either bitemporal or multitemporal, and methods focus on one kind of task. In this paper, we propose a dual-branch supervised CD method which uses time series of Remote Sensing optical data to simultaneously perform two tasks, identifying binary abrupt changes and multitemporal seasonal ones. The method relies on a 3D fully convolutional architecture and uses dilated convolutions as well as spatial and channel attention mechanisms to examine the spatial and temporal dimensions of the data. The method is tested on a time series of multispectral images acquired by Landsat-8. Results show that the proposed framework proves effective in detecting the two kinds of changes.