Image segmentation is a challenging and essential task in remote sensing. Deep neural networks (DNNs) have successfully segmented images from different domains, but the models usually require time-consuming and expensive pixel-level data annotation. In this letter, we exploit a recent technique to learn features (an encoder) from a few markers placed by the user in relevant image regions, build an encoder–decoder model from a small set of regions delineated by click-based segmentation, and use that model to annotate the remaining pixels. Such user-guided data expansion modeling can be repeated as the encoder–decoder network improves, and by selecting well-annotated regions, the user considerably expands the pixel set to train DNNs with little supervision. We show the role of feature learning from image markers (FLIM) and that our data expansion model can significantly improve the generalization performance of a state-of-the-art DNN when segmenting buildings in aerial images of distinct cities.

中文翻译：

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用户引导的数据扩展建模在几乎没有监督的情况下训练深度神经网络


图像分割是遥感中一项具有挑战性且重要的任务。深度神经网络（DNN）已成功分割来自不同领域的图像，但该模型通常需要耗时且昂贵的像素级数据注释。在这封信中，我们利用最新的技术从用户在相关图像区域中放置的一些标记中学习特征（编码器），从基于点击的分割描绘的一小组区域构建编码器-解码器模型，并使用该模型来注释剩余的像素。随着编码器-解码器网络的改进，可以重复这种用户引导的数据扩展建模，并且通过选择注释良好的区域，用户可以在几乎没有监督的情况下显着扩展像素集来训练 DNN。我们展示了从图像标记 (FLIM) 进行特征学习的作用，并且我们的数据扩展模型在分割不同城市的航空图像中的建筑物时可以显着提高最先进的 DNN 的泛化性能。