论文标题
S2-CGAN：在多光谱图像中进行二进制变更检测的自我监督的对抗表示学习
S2-cGAN: Self-Supervised Adversarial Representation Learning for Binary Change Detection in Multispectral Images
论文作者
论文摘要
深度神经网络最近在遥感（RS）中表现出了有希望的二元变化检测（CD）问题的表现，需要大量标记的多个阶段训练样本。由于收集此类数据是耗时且昂贵的，因此大多数现有方法都依赖于公开可用的计算机视觉（CV）数据集的预训练网络。但是，由于CV和RS的图像特性差异，此方法限制了现有CD方法的性能。为了解决这个问题，我们提出了一个自制的条件生成对抗网络（S2-CGAN）。提出的S^2-CAN经过训练，仅生成未改变样品的分布。为此，提出的方法包括两个主要步骤：1）生成输入图像的重建版本作为一个不变的图像2）通过对抗性游戏学习不变样品的分布。与现有的基于GAN的方法（仅在对抗训练中使用判别器来监督发电机）不同，S2-CGAN直接利用判别器的可能性来解决二进制CD任务。实验结果表明，与最先进的CD方法相比，提出的S2-CGAN的有效性。
Deep Neural Networks have recently demonstrated promising performance in binary change detection (CD) problems in remote sensing (RS), requiring a large amount of labeled multitemporal training samples. Since collecting such data is time-consuming and costly, most of the existing methods rely on pre-trained networks on publicly available computer vision (CV) datasets. However, because of the differences in image characteristics in CV and RS, this approach limits the performance of the existing CD methods. To address this problem, we propose a self-supervised conditional Generative Adversarial Network (S2-cGAN). The proposed S^2-cGAN is trained to generate only the distribution of unchanged samples. To this end, the proposed method consists of two main steps: 1) Generating a reconstructed version of the input image as an unchanged image 2) Learning the distribution of unchanged samples through an adversarial game. Unlike the existing GAN based methods (which only use the discriminator during the adversarial training to supervise the generator), the S2-cGAN directly exploits the discriminator likelihood to solve the binary CD task. Experimental results show the effectiveness of the proposed S2-cGAN when compared to the state of the art CD methods.
