利用深度学习合成SAR-光学数据提高作物分类精度
Enhancing crop classification accuracy by synthetic SAR-Optical data generation using deep learning
AI总结：
本研究提出使用条件表格生成对抗网络（CTGAN）合成SAR-光学融合数据，以解决作物分类中少数类训练样本不足的问题，实验证明该方法能生成高质量数据，显著提升分类性能。
摘要：
利用遥感数据进行作物分类在近几十年已成为一个突出的研究领域。研究表明，融合SAR和光学图像可以显著提高分类精度。然而，该领域的一个主要挑战是训练数据的有限可用性，这不利地影响分类器的性能。在农业区域，主要作物通常由一或两种特定类型组成，而其他作物则稀缺。因此，当收集训练样本以创建农产品地图时，来自主要作物的样本丰富，形成多数类。相反，来自其他作物的样本稀缺，代表少数类。解决这一问题需要克服与传统数据生成方法相关的若干挑战和弱点。这些方法已被用于应对训练数据的不平衡性质。然而，它们在有效处理少数类方面仍面临局限性。总体而言，训练数据不足的问题，特别是对于少数类，仍然是传统方法难以克服的障碍。在本研究中，我们探讨了条件表格生成对抗网络（CTGAN）作为一种基于深度学习网络的合成数据生成方法，在利用SAR-光学数据融合进行作物分类时，解决少数类训练数据有限挑战的有效性。我们的研究结果表明，所提出的方法生成的合成数据质量更高，能显著增加少数类的样本数量，从而带来作物分类器更好的性能。
英文摘要：
Crop classification using remote sensing data has emerged as a prominent research area in recent decades. Studies have demonstrated that fusing SAR and optical images can significantly enhance the accuracy of classification. However, a major challenge in this field is the limited availability of training data, which adversely affects the performance of classifiers. In agricultural regions, the dominant crops typically consist of one or two specific types, while other crops are scarce. Consequently, when collecting training samples to create a map of agricultural products, there is an abundance of samples from the dominant crops, forming the majority classes. Conversely, samples from other crops are scarce, representing the minority classes. Addressing this issue requires overcoming several challenges and weaknesses associated with traditional data generation methods. These methods have been employed to tackle the imbalanced nature of the training data. Nevertheless, they still face limitations in effectively handling the minority classes. Overall, the issue of inadequate training data, particularly for minority classes, remains a hurdle that traditional methods struggle to overcome. In this research, We explore the effectiveness of conditional tabular generative adversarial network (CTGAN) as a synthetic data generation method based on a deep learning network, in addressing the challenge of limited training data for minority classes in crop classification using the fusion of SAR-optical data. Our findings demonstrate that the proposed method generates synthetic data with higher quality that can significantly increase the number of samples for minority classes leading to better performance of crop classifiers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
