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A Deep Multitask Learning Framework Coupling Semantic Segmentation and Fully Convolutional LSTM Networks for Urban Change Detection
IEEE Transactions on Geoscience and Remote Sensing ( IF 8.2 ) Pub Date : 2021-02-10 , DOI: 10.1109/tgrs.2021.3055584
Maria Papadomanolaki , Maria Vakalopoulou , Konstantinos Karantzalos

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 .

中文翻译:

结合语义分割和全卷积 LSTM 网络用于城市变化检测的深度多任务学习框架

在本文中,我们提出了一个深度多任务学习框架,能够使用全卷积长短期记忆 (LSTM) 网络将语义分割和变化检测结合起来。特别是,我们提出了一种类似 UNet 的架构 (L-UNet),该架构在每个编码级别之上使用集成的完全卷积 LSTM 块对空间特征表示的时间关系进行建模。通过这种方式,网络能够捕获所有编码级别的空间特征向量的时间关系,而无需对它们进行下采样或展平,形成端到端的可训练框架。此外,我们通过额外的解码分支进一步丰富了 L-UNet 架构,该分支对不同输入日期中呈现的可用语义类别执行语义分割,形成多任务框架。不同的损失量也被定义并以循环方式组合在一起,以提高整体性能。开发的方法已在三个不同的数据集上进行了评估,即具有挑战性的双时间高分辨率 Office National d'Etudes et de Recherches Aérospatiales (ONERA) 卫星变化检测 (OSCD) Sentinel-2 数据集,超高分辨率 ( VHR) 希腊阿提卡东部地区的多时相数据集,最后是多时相 VHR SpaceNet7 数据集。有希望的定量和定性结果表明,任务之间的协同作用可以提高实现的性能。特别是,所提出的多任务框架有助于显着减少假阳性检测,F1 率至少比其他最先进的方法高出 2.1% 和 4%。在 Attica VHR 和 SpaceNet7 数据集案例中分别为 9%。我们的模型和代码可以在https://github.com/mpaadomanolaki/multi-task-L-UNet .
更新日期:2021-02-10
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