Zeng Junying, Gu Yajin, Qin Chuanbo, Jia Xudong, Deng Senyao, Xu Jiahua, Tian Huiming
School of Electronics and Information Engineering, Wuyi University, Guangdong, 529020, China.
College of Intelligent Systems Science and Engineering, Guangzhou Huali College, Guangdong, 511325, China.
Sci Rep. 2024 Oct 9;14(1):23552. doi: 10.1038/s41598-024-74781-y.
There would be the differences in spectra, scale and resolution between the Remote Sensing datasets of the source and target domains, which would lead to the degradation of the cross-domain segmentation performance of the model. Image transfer faced two problems in the process of domain-adaptive learning: overly focusing on style features while ignoring semantic information, leading to biased transformation results, and easily overlooking the true transfer characteristics of remote sensing images, resulting in unstable model training. To address these issues, we proposes a novel dual-space generative adversarial domain adaptation segmentation framework, DS-DWTGAN, to minimize the differences between the source domain and the target domain. DS-DWTGAN aims to mitigate the distinctions between the source and target domains, thereby rectifying the imbalances in style and semantic representation.The framework introduces a network branch leveraging wavelet transform to capture comprehensive frequency domain and semantic information. It aims to preserve semantic details within the frequency domain space, mitigating image conversion deviations. Furthermore, our proposed method integrates output adaptation and data enhancement training strategies to reinforce the acquisition of domain-invariant features. This approach effectively diminishes noise interference during the migration process, bolsters model stability, and elevates the model's adaptability to remote sensing images within different domains. Experimental validation was conducted on the publicly available Potsdam and Vaihingen datasets. The findings reveal that in the PotsdamIRRG to Vaihingen task, the proposed method attains outstanding performance with mIoU and mF1 values reaching 56.04% and 67.28%, respectively. Notably, these metrics surpass the corresponding values achieved by state-of-the-art (SOTA) methods, registering an increase of 2.81% and 2.08%. In comparison to alternative approaches, our proposed framework exhibits superior efficacy in the domain of unsupervised semantic segmentation for UAV remote sensing images.
源域和目标域的遥感数据集在光谱、尺度和分辨率上会存在差异，这将导致模型的跨域分割性能下降。图像迁移在域自适应学习过程中面临两个问题：过度关注风格特征而忽略语义信息，导致变换结果有偏差，并且容易忽略遥感图像的真实迁移特征，从而导致模型训练不稳定。为了解决这些问题，我们提出了一种新颖的双空间生成对抗域自适应分割框架DS-DWTGAN，以最小化源域和目标域之间的差异。DS-DWTGAN旨在减轻源域和目标域之间的差异，从而纠正风格和语义表示中的不平衡。该框架引入了一个利用小波变换的网络分支，以捕获全面的频域和语义信息。其目的是在频域空间中保留语义细节，减轻图像转换偏差。此外，我们提出的方法集成了输出自适应和数据增强训练策略，以加强对域不变特征的获取。这种方法有效地减少了迁移过程中的噪声干扰，增强了模型稳定性，并提高了模型对不同域内遥感图像的适应性。在公开可用的波茨坦和弗赖辛数据集上进行了实验验证。结果表明，在从波茨坦红外遥感影像到弗赖辛的任务中，该方法取得了优异的性能，mIoU和mF1值分别达到56.04%和67.28%。值得注意的是，这些指标超过了现有最先进（SOTA）方法的相应值，分别提高了2.81%和2.08%。与其他方法相比，我们提出的框架在无人机遥感图像的无监督语义分割领域表现出卓越的效果。
