无需真实标签，自监督训练实现遥感图像立体匹配
SAda-Net: A Self-Supervised Adaptive Stereo Estimation CNN For Remote Sensing Image Data
- 用左右一致性检测生成初始伪标签，动态更新优化
- 每轮训练后自适应修正伪标签，提升精度与鲁棒性
- 适合无标注数据的遥感图像立体估计任务
近年来，深度学习推动了立体估计的快速发展。然而，传统监督学习依赖大量精确的真值数据，而这些数据在遥感领域往往难以获取且成本高昂。针对这一问题，我们提出一种自监督的自适应立体估计卷积神经网络（SAda-Net）。初始阶段生成的视差图存在噪声和误差，利用左右一致性检查得到稀疏但更准确的视差图作为初始伪真值。该伪真值在每次训练迭代中被更新并自适应调整。通过统计不一致点的总和来监控网络收敛情况。代码已公开：https://github.com/thedodo/SAda-Net
原文摘要 · Abstract (English)
Stereo estimation has made many advancements in recent years with the introduction of deep-learning. However the traditional supervised approach to deep-learning requires the creation of accurate and plentiful ground-truth data, which is expensive to create and not available in many situations. This is especially true for remote sensing applications, where there is an excess of available data without proper ground truth. To tackle this problem, we propose a self-supervised CNN with self-improving adaptive abilities. In the first iteration, the created disparity map is inaccurate and noisy. Leveraging the left-right consistency check, we get a sparse but more accurate disparity map which is used as an initial pseudo ground-truth. This pseudo ground-truth is then adapted and updated after every epoch in the training step of the network. We use the sum of inconsistent points in order to track the network convergence. The code for our method is publicly available at: https://github.com/thedodo/SAda-Net}{https://github.com/thedodo/SAda-Net
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