Change detection by comparing two bitemporal images is one of the most fundamental challenges for dynamic monitoring of the Earth surface. In this article, we propose a metric learning-based generative adversarial network (GAN) (MeGAN) to automatically explore seasonal invariant features for pseudochange suppressing and real change detection. To achieve this purpose, a seasonal invariant term is introduced to maximally suppress pseudochanges, whereas the MeGAN explores the transition patterns between adjacent images in a self-learning fashion. Different from the previous works on bitemporal imagery change detection, the proposed MeGAN have the following contributions: 1) it automatically explores change patterns from the complex bitemporal background without human intervention and 2) it aims to maximally exclude pseudochanges from the seasonal transition term and map out real changes efficiently. To our best knowledge, this is the first time we incorporate the seasonal transition term and GAN for change detection between bitemporal images. At last, to demonstrate the robustness of the proposed method, we included two data sets which are the Google Earth data and the Landsat data, for bitemporal change detection and evaluation. The experimental results indicated that the proposed method is able to perform change detection with precision can be as high as 81% and 88% for the Google Earth and Landsat data set, respectively.

中文翻译：

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结合度量学习和对抗网络进行季节性不变变化检测

通过比较两个双时相图像来检测变化是地球表面动态监测的最基本挑战之一。在本文中，我们提出了一种基于度量学习的生成对抗网络 (GAN) (MeGAN)，以自动探索季节性不变特征，用于伪变化抑制和真实变化检测。为了达到这个目的，引入了一个季节性不变项来最大限度地抑制伪变化，而 MeGAN 以自学习的方式探索相邻图像之间的过渡模式。与之前关于双时态图像变化检测的工作不同，提出的 MeGAN 有以下贡献：1）它在没有人工干预的情况下自动探索复杂双时态背景的变化模式，2）它旨在最大限度地从季节性过渡项中排除伪变化并有效地绘制出真实变化。据我们所知，这是我们第一次结合季节性转换项和 GAN 来检测双时相图像之间的变化。最后，为了证明所提出方法的鲁棒性，我们包括两个数据集，即 Google Earth 数据和 Landsat 数据，用于双时态变化检测和评估。实验结果表明，所提出的方法能够对Google Earth和Landsat数据集进行变化检测，精度分别高达81%和88%。这是我们第一次结合季节性转换项和 GAN 来检测双时态图像之间的变化。最后，为了证明所提出方法的鲁棒性，我们包括两个数据集，即 Google Earth 数据和 Landsat 数据，用于双时态变化检测和评估。实验结果表明，所提出的方法能够对Google Earth和Landsat数据集进行变化检测，精度分别高达81%和88%。这是我们第一次结合季节性转换项和 GAN 来检测双时态图像之间的变化。最后，为了证明所提出方法的鲁棒性，我们包括两个数据集，即 Google Earth 数据和 Landsat 数据，用于双时态变化检测和评估。实验结果表明，所提出的方法能够对Google Earth和Landsat数据集进行变化检测，精度分别高达81%和88%。