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首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation
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Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation

机译：跨域遥感图像语义分割多弱监督约束下的深度语义分割网络

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摘要

Due to its wide applications, remote sensing (RS) image semantic segmentation has attracted increasing research interest in recent years. Benefiting from its hierarchical abstract ability, the deep semantic segmentation network (DSSN) has achieved tremendous success on RS image semantic segmentation and has gradually become the mainstream technology. However, the superior performance of DSSN highly depends on two conditions: (I) massive quantities of labeled training data exist; (II) the testing data seriously resemble the training data. In actual RS applications, it is difficult to fully meet these conditions due to the RS sensor variation and the distinct landscape variation in different geographic locations. To make DSSN fit the actual RS scenario, this paper exploits the cross-domain RS image semantic segmentation task, which means that DSSN is trained on one labeled dataset (i.e., the source domain) but is tested on another varied dataset (i.e., the target domain). In this setting, the performance of DSSN is inevitably very limited due to the data shift between the source and target domains. To reduce the disadvantageous influence of data shift, this paper proposes a novel objective function with multiple weakly-supervised constraints to learn DSSN for cross-domain RS image semantic segmentation. Through carefully examining the characteristics of cross-domain RS image semantic segmentation, multiple weakly-supervised constraints include the weakly-supervised transfer invariant constraint (WTIC), weakly-supervised pseudo-label constraint (WPLC) and weakly-supervised rotation consistency constraint (WRCC). Specifically, DualGAN is recommended to conduct unsupervised style transfer between the source and target domains to carry out WTIC. To make full use of the merits of multiple constraints, this paper presents a dynamic optimization strategy that dynamically adjusts the constraint weights of the objective function during the training process. With full consideration of the characteristics of the cross-domain RS image semantic segmentation task, this paper gives two cross-domain RS image semantic segmentation settings: (I) variation in geographic location and (II) variation in both geographic location and imaging mode. Extensive experiments demonstrate that our proposed method remarkably outperforms the state-of-the-art methods under both of these settings. The collected datasets and evaluation benchmarks have been made publicly available online (htt ps://github.com/te-shi/MUCSS).
机译：由于其广泛的应用，遥感（RS）图像语义分割已经吸引了越来越多在​​最近几年的研究兴趣。从它的层次抽象能力的推动，深语义分割网络（DSSN）已实现对遥感图像语义分割了巨大的成功，并已逐渐成为主流技术。然而，DSSN的卓越性能在很大程度上取决于两个条件：（I）标记的训练数据的数量大量存在; （II）的检测数据严重类似于训练数据。在实际的RS的应用中，难以完全满足这些条件，由于RS传感器变化和在不同的地理位置的不同景观变化。为了使DSSN适合实际的RS的情况下，本文利用了跨域RS图像语义分割任务，这意味着DSSN是在一个标记的数据集（即，源结构域）训练但在另一变化的数据集进行测试（即，目标域）。在此设置中，DSSN的性能不可避免地非常有限的，由于源和目标域之间的数据移位。为了减少数据移位的不利影响，提出了具有多个弱监督约束的新颖目标函数学习DSSN跨域RS图像语义分割。通过仔细检查跨域RS图像语义分割的特性，多个弱监督约束包括弱监督传递不变的约束（WTIC），弱监督伪标签约束（WPLC）和弱监督旋转一致性约束（水资源承载力）。具体而言，DualGAN建议进行源和目标域之间无监督式的转印进行WTIC。为了充分利用多个约束的优点，本文提出了一种动态优化策略，在训练过程中动态调整目标函数的约束权。充分考虑的跨域RS图像语义分割任务的特点，本文给出了两个跨域RS图像语义分割设置：在地理位置（I）的变化和在两个地理位置和成像模式（II）的变化。大量的实验证明，我们提出的方法明显优于下这两种设置国家的最先进的方法。所收集的数据集和评估基准已经公开发布在网上（HTT PS：//github.com/te-shi/MUCSS）。

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