Building assessment is highly prioritized during rescue operations and damage relief after hurricane disasters. Although machine learning has made remarkable improvement in building damage classification, it remains challenging because classifiers must be trained using a massive amount of labeled data. Furthermore, data labeling is labor intensive, costly, and unavailable after a disaster. To address this issue, we propose an unsupervised domain adaptation method with aligned discriminative and representative features (ADRF), which leverage a substantial amount of labeled data of relevant disaster scenes for new classification tasks. The remote sensing imageries of different disasters are collected using different sensors, viewpoints, times, even at various places. Compared with the public datasets used in the domain adaptation community, the remote sensing imageries are more complicated which exhibit characteristics of lower discrimination between categories and higher diversity within categories. As a result, pursuing domain invariance is a huge challenge. To achieve this goal, we build a framework with ADRF to improve the discriminative and representative capability of the extracted features to facilitate the classification task. The ADRF framework consists of three pipelines: a classifier for the labeled data of the source domain and one autoencoder each for the source and target domains. The latent variables of autoencoders are forced to observe unit Gaussian distributions by minimizing the maximum mean discrepancy (MMD), whereas the marginal distributions of both domains are aligned via the MMD. As a case study, two challenging transfer tasks using the hurricane Sandy, Maria, and Irma datasets are investigated. Experimental results demonstrate that ADRF achieves overall accuracy of 71.6% and 84.1% in the transfer tasks from dataset Sandy to dataset Maria and dataset Irma, respectively.

中文翻译：

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协调区分性和代表性特征：用于建筑损坏评估的无监督域适应方法。


在飓风灾难后的救援行动和损害救济过程中，建筑评估是高度优先的。尽管机器学习在建筑损坏分类方面取得了显着的进步，但它仍然具有挑战性，因为分类器必须使用大量标记数据进行训练。此外，数据标记是劳动密集型的、成本高昂的，并且在灾难发生后无法使用。为了解决这个问题，我们提出了一种具有对齐的判别性和代表性特征（ADRF）的无监督域适应方法，该方法利用相关灾难场景的大量标记数据来执行新的分类任务。不同灾害的遥感图像是使用不同的传感器、视点、时间、甚至在不同的地点收集的。与领域适应社区使用的公共数据集相比，遥感图像更加复杂，表现出类别间区分度较低、类别内多样性较高的特点。因此，追求领域不变性是一个巨大的挑战。为了实现这一目标，我们利用 ADRF 构建了一个框架，以提高提取特征的判别性和代表性能力，以促进分类任务。 ADRF 框架由三个管道组成：一个用于源域标记数据的分类器，以及一个用于源域和目标域的自动编码器。自动编码器的潜在变量被迫通过最小化最大平均差异（MMD）来观察单位高斯分布，而两个域的边缘分布通过 MMD 对齐。作为案例研究，研究了使用飓风桑迪、玛丽亚和艾尔玛数据集的两个具有挑战性的传输任务。 实验结果表明，ADRF 在从数据集 Sandy 到数据集 Maria 和数据集 Irma 的传输任务中分别实现了 71.6% 和 84.1% 的总体准确率。