DSNet: dual-stream network for fine-grained ship classification in optical remote sensing images

作者：Shijun Yang, Xiang Zhang, Wanqing Zhao, Qiyao Hu, Hangzai Luo, Cheng Liu, Sheng Zhong, Jinye Peng · 发表于：Remote Sensing Letters · 年份：2024 · DOI：10.1080/2150704x.2024.2377211 · 被引用次数：4 · 研究领域：Remote-Sensing Image Classification、Advanced Neural Network Applications、Automated Road and Building Extraction

The fine-grained ship classification (FGSC) task in remote sensing aims to distinguish subordinate ship categories. Due to the high similarity among different subcategories and the significant variations in pose, scale, rotation, and color within the same subcategory in the fine-grained ship datasets, the FGSC task is extremely challenging. To alleviate these issues, a novel approach, named Dual-Stream Network (DSNet), is proposed in this paper for FGSC. DSNet consists of two sub-branches: the Local Residual Branch (LRB) and the Global Representation Branch (GRB). Specifically, the LRB effectively extracts the most discriminative local fine-grained features of the target using Full-scale Skip Connections (FSC) and Stripe Pooling Add (SPA) modules. The GRB captures global contextual information through Swin-Transformer (Swin-T), establishing remote dependencies of the target and extracting global coarse-grained features. Furthermore, a Lightweight Fusion (LF) block is designed in the feature fusion module to combine the feature information from the two branches. This ensures that the discriminative global and local information complement each other without overwhelming one another, thereby effectively improving the performance of the model. Extensive experiments and analyses demonstrate that our proposed method achieves state-of-the-art performance on two mainstream FGSC datasets in remote sensing.