用可逆网络增强卫星图像，提升交互式变化检测效果
Image augmentation with invertible networks in interactive satellite image change detection
- 通过可逆网络将图像映射到隐空间，实现线性增广
- 在隐空间中进行数据增强后回映到原空间，提升模型性能
- 适用于需要少量标注的高精度卫星图像变化检测任务
本文提出一种基于主动学习的交互式卫星图像变化检测新算法。框架采用迭代过程，利用问答模型向人工标注者（即oracle）询问一小部分图像（称为display）的标签，并根据反馈动态更新变化检测模型。核心贡献在于设计了一种新型可逆网络，可将图像从高度非线性的输入空间映射到隐空间，在该空间中数据增强操作变为线性且更易处理。增强后的数据再被映射回输入空间，用于后续主动学习迭代中的模型重训练，从而提升检测效果。实验表明，所提方法优于现有相关工作。
原文摘要 · Abstract (English)
This paper devises a novel interactive satellite image change detection algorithm based on active learning. Our framework employs an iterative process that leverages a question-and-answer model. This model queries the oracle (user) about the labels of a small subset of images (dubbed as display), and based on the oracle's responses, change detection model is dynamically updated. The main contribution of our framework resides in a novel invertible network that allows augmenting displays, by mapping them from highly nonlinear input spaces to latent ones, where augmentation transformations become linear and more tractable. The resulting augmented data are afterwards mapped back to the input space, and used to retrain more effective change detection criteria in the subsequent iterations of active learning. Experimental results demonstrate superior performance of our proposed method compared to the related work.
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