Abstract
Unsupervised domain adaptation (UDA) is a challenging open problem in land cover mapping. Previous studies show encouraging progress in addressing cross-domain distribution shifts on remote sensing benchmarks for land cover mapping. The existing works are mainly built on large neural network architectures, which makes them resource-hungry systems, limiting their practical impact for many real-world applications in resource-constrained environments. Thus, we proposed a simple yet effective framework to search for lightweight neural networks automatically for land cover mapping tasks under domain shifts. This is achieved by integrating Markov random field neural architecture search (MRF-NAS) into a self-training UDA framework to search for efficient and effective networks under a limited computation budget. This is the first attempt to combine NAS with self-training UDA as a single framework for land cover mapping. We also investigate two different pseudo-labelling approaches (confidence-based and energy-based) in self-training scheme. Experimental results on two recent datasets (OpenEarthMap & FLAIR #1) for remote sensing UDA demonstrate a satisfactory performance. With only less than 2M parameters and 30.16 GFLOPs, the best-discovered lightweight network reaches state-of-the-art performance on the regional target domain of OpenEarthMap (59.38% mIoU) and the considered target domain of FLAIR #1 (51.19% mIoU). The code is at this https URL}{this https URL.
Abstract (translated)
未经监督的领域适应（UDA）是土地覆盖制图领域的一个具有挑战性的开放问题。以前的研究表明，在遥感基准测试中解决跨领域分布变化对于土地覆盖制图取得了鼓舞人心的进展。现有的工作主要是基于大型神经网络架构构建的，这使得它们成为资源密集型系统，对于许多资源受限环境中的实际应用限制了其实际影响。因此，我们提出了一个简单而有效的框架，用于在领域变化下搜索轻量级神经网络，以解决土地覆盖制图任务。这是第一个将 NAS 与自训练 UDA 相结合作为一个单一框架的土地覆盖制图问题。我们还研究了两种不同的伪标签方法（基于信度的和基于能量的方法）在自训练方案中的效果。对于遥感 UDA 的两个最近的数据集（OpenEarthMap & FLAIR #1），实验结果表明，轻量级网络在仅不到 2M 个参数和 30.16 GFLOPs 的条件下，在 OpenEarthMap 的区域目标域（59.38% mIoU）和 FLAIR #1 考虑的目标域（51.19% mIoU）的性能达到最先进水平。代码位于此链接：<https://this URL>
URL
https://arxiv.org/abs/2404.14704
