A Deep Multitask Learning Framework Coupling Semantic Segmentation and Fully Convolutional LSTM Networks for Urban Change Detection

Abstract

In this article, we present a deep multitask learning framework able to couple semantic segmentation and change detection using fully convolutional long short-term memory (LSTM) networks. In particular, we present a UNet-like architecture (L-UNet) that models the temporal relationship of spatial feature representations using integrated fully convolutional LSTM blocks on top of every encoding level. In this way, the network is able to capture the temporal relationship of spatial feature vectors in all encoding levels without the need to downsample or flatten them, forming an end-to-end trainable framework. Moreover, we further enrich the L-UNet architecture with an additional decoding branch that performs semantic segmentation on the available semantic categories that are presented in the different input dates, forming a multitask framework. Different loss quantities are also defined and combined together in a circular way to boost the overall performance. The developed methodology has been evaluated on three different data sets, i.e., the challenging bitemporal high-resolution Office National d'Etudes et de Recherches Aérospatiales (ONERA) Satellite Change Detection (OSCD) Sentinel-2 data set, the very high-resolution (VHR) multitemporal data set of the East Prefecture of Attica, Greece, and finally, the multitemporal VHR SpaceNet7 data set. Promising quantitative and qualitative results demonstrated that the synergy among the tasks can boost up the achieved performances. In particular, the proposed multitask framework contributed to a significant decrease in false-positive detections, with the F1 rate outperforming other state-of-the-art methods by at least 2.1% and 4.9% in the Attica VHR and SpaceNet7 data set cases, respectively. Our models and code can be found at https://github.com/mpapadomanolaki/multi-task-L-UNet.