Yang Junliang, Chen Guorong, Huang Jiaming, Ma Denglong, Liu Jingcheng, Zhu Huazheng
Department of Intelligent Technology and Engineering, Chongqing University of Science and Technology, No.20, East University Town Road, Shapingba District, Chongqing, 401331, China.
School of Mechanical Engineering, Xi'an Jiaotong University, No. 28, Xianning West Road, Xi'an, 710049, Shaanxi, China.
Sci Rep. 2024 Oct 25;14(1):25282. doi: 10.1038/s41598-024-76622-4.
Remote sensing (RS) images contain a wealth of information with expansive potential for applications in image segmentation. However, Convolutional Neural Networks (CNN) face challenges in fully harnessing the global contextual information. Leveraging the formidable capabilities of global information modeling with Swin-Transformer, a novel RS images segmentation model with CNN (GLE-Net) was introduced. This integration gives rise to a revamped encoder structure. The subbranch initiates the process by extracting features at varying scales within the RS images using the Multiscale Feature Fusion Module (MFM), acquiring rich semantic information, discerning localized finer features, and adeptly handling occlusions. Subsequently, Feature Compression Module (FCM) is introduced in main branch to downsize the feature map, effectively reducing information loss while preserving finer details, enhancing segmentation accuracy for smaller targets. Finally, we integrate local features and global features through Spatial Information Enhancement Module (SIEM) for comprehensive feature modeling, augmenting the segmentation capabilities of model. We performed experiments on public datasets provided by ISPRS, yielding notably remarkable experimental outcomes. This underscores the substantial potential of our model in the realm of RS image segmentation within the context of scientific research.
遥感（RS）图像包含丰富的信息，在图像分割应用方面具有广阔的潜力。然而，卷积神经网络（CNN）在充分利用全局上下文信息方面面临挑战。利用Swin-Transformer强大的全局信息建模能力，引入了一种新型的基于CNN的RS图像分割模型（GLE-Net）。这种整合产生了一种改进的编码器结构。子分支通过使用多尺度特征融合模块（MFM）在RS图像中提取不同尺度的特征来启动该过程，获取丰富的语义信息，识别局部更精细的特征，并巧妙地处理遮挡。随后，在主分支中引入特征压缩模块（FCM）来缩小特征图的尺寸，有效减少信息损失，同时保留更精细的细节，提高对较小目标的分割精度。最后，我们通过空间信息增强模块（SIEM）整合局部特征和全局特征，进行全面的特征建模，增强模型的分割能力。我们在国际摄影测量与遥感学会（ISPRS）提供的公共数据集上进行了实验，取得了显著的实验结果。这凸显了我们的模型在科学研究背景下的RS图像分割领域的巨大潜力。
