Deep Learning (DL) has become a popular method for Remote Sensing (RS) Change Detection (CD) due to its superior performance compared to traditional methods. However, generating extensive labeled datasets for DL models is time-consuming and labor-intensive. Additionally, the imbalance between changed and unchanged areas in object CD datasets, such as buildings, poses a critical issue affecting DL model efficacy. To address this issue, this paper proposes a change detection enhancement method using artificial image generation and attention mechanism. Firstly, the content of the imbalanced CD dataset is enhanced using a data augmentation strategy that synthesizes effective building CD samples using artificial RS image generation and building label creation. The created building labels, which serve as new change maps, are fed into a generator model based on a conditional Generative Adversarial Network (c-GAN) to generate high-resolution RS images featuring building changes. The generated images with their corresponding change maps are then added to the CD dataset to create the balance between changed and unchanged samples. Secondly, a channel attention mechanism is added to the proposed Adversarial Change Detection Network (Adv-CDNet) to boost its performance when training on the imbalanced dataset. The study evaluates the Adv-CDNet using WHU-CD and LEVIR-CD datasets, with WHU-CD exhibiting a higher degree of sample imbalance compared to LEVIR-CD. Training the Adv-CDNet on the augmented dataset results in a significant 16.5% F1-Score improvement for the highly imbalanced WHU-CD. Moreover, comparative analysis showcases the superior performance of the Adv-CDNet when complemented with the attention module, achieving a 6.85% F1-Score enhancement.

中文翻译：

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使用人工图像生成和注意力机制增强不平衡数据集中基于对抗性学习的变化检测

深度学习（DL）由于其与传统方法相比具有优越的性能，已成为遥感（RS）变化检测（CD）的流行方法。然而，为深度学习模型生成大量标记数据集既耗时又费力。此外，对象 CD 数据集中已更改区域和未更改区域（例如建筑物）之间的不平衡是影响深度学习模型功效的关键问题。为了解决这个问题，本文提出了一种利用人工图像生成和注意力机制的变化检测增强方法。首先，使用数据增强策略增强不平衡 CD 数据集的内容，该策略使用人工 RS 图像生成和建筑标签创建来合成有效的建筑 CD 样本。创建的建筑标签作为新的变化地图，被输入到基于条件生成对抗网络（c-GAN）的生成器模型中，以生成具有建筑变化的高分辨率 RS 图像。然后将生成的图像及其相应的变化图添加到 CD 数据集，以在变化和未变化的样本之间创建平衡。其次，在所提出的对抗性变化检测网络（Adv-CDNet）中添加了通道注意机制，以提高其在不平衡数据集上训练时的性能。该研究使用 WHU-CD 和 LEVIR-CD 数据集评估 Adv-CDNet，与 LEVIR-CD 相比，WHU-CD 表现出更高程度的样本不平衡。在增强数据集上训练 Adv-CDNet 可使高度不平衡的 WHU-CD 的 F1 分数显着提高 16.5%。此外，比较分析展示了 Adv-CDNet 在与注意力模块互补时的优越性能，实现了 6.85% 的 F1-Score 增强。