Human civilization has an increasingly powerful influence on the earth system, and earth observations are an invaluable tool for assessing and mitigating the negative impacts. To this end, observing precisely defined changes on Earth's surface is essential, and we propose an effective way to achieve this goal. Notably, our change detection (CD)/ segmentation method proposes a novel way to incorporate the millions of off-the-shelf, unlabeled, remote sensing images available through different earth observation programs into the training process through denoising diffusion probabilistic models. We first leverage the information from these off-the-shelf, uncurated, and unlabeled remote sensing images by using a pre-trained denoising diffusion probabilistic model and then employ the multi-scale feature representations from the diffusion model decoder to train a lightweight CD classifier to detect precise changes. The experiments performed on four publically available CD datasets show that the proposed approach achieves remarkably better results than the state-of-the-art methods in F1, IoU, and overall accuracy. Code and pre-trained models are available at: https://github.com/wgcban/ddpm-cd
翻译:人类文明对地球系统的影响越来越强大,地球观测是评估和减轻负面影响的宝贵工具。为此,观测精确定义的地球表面变化至关重要,我们提出了实现这一目标的有效方法。值得注意的是,我们的变化探测(CD)/分解方法提出了一种新颖的方法,通过解密扩散概率模型,将通过不同地球观测方案提供的数百万现成、未贴标签的遥感图像纳入培训过程。我们首先利用这些现成、未涂色和未贴标签的遥感图像中的信息,为此,我们使用事先经过训练的解密扩散概率模型,然后使用扩散模型的多尺度特征显示,以训练轻量的CD分类,以探测精确的变化。在四个公开提供的CD数据集上进行的实验表明,拟议方法取得了比F1、IoU和总体准确性的最新技术方法要好得多的结果。代码和预先训练的模型见:https://github.com/wgban/ddmb/dddd。