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An Object-Aware Network Embedding Deep Superpixel for Semantic Segmentation of Remote Sensing Images

文献类型： 外文期刊

作者： Ye, Ziran 1 ; Lin, Yue 2 ; Dong, Baiyu 3 ; Tan, Xiangfeng 1 ; Dai, Mengdi 1 ; Kong, Dedong 1 ;

作者机构： 1.Zhejiang Acad Agr Sci, Inst Digital Agr, Hangzhou 310021, Peoples R China

2.Hangzhou City Univ, Sch Spatial Planning & Design, Hangzhou 310015, Peoples R China

3.Zhejiang Univ, Coll Environm & Resource Sci, Hangzhou 310058, Peoples R China

关键词： VHR image; convolutional neural network; deep superpixel; semantic segmentation

期刊名称：REMOTE SENSING （ 影响因子：4.1； 五年影响因子：4.8 ）

ISSN：

年卷期： 2024 年 16 卷 20 期

页码：

收录情况： SCI

摘要： Semantic segmentation forms the foundation for understanding very high resolution (VHR) remote sensing images, with extensive demand and practical application value. The convolutional neural networks (CNNs), known for their prowess in hierarchical feature representation, have dominated the field of semantic image segmentation. Recently, hierarchical vision transformers such as Swin have also shown excellent performance for semantic segmentation tasks. However, the hierarchical structure enlarges the receptive field to accumulate features and inevitably leads to the blurring of object boundaries. We introduce a novel object-aware network, Embedding deep SuperPixel, for VHR image semantic segmentation called ESPNet, which integrates advanced ConvNeXt and the learnable superpixel algorithm. Specifically, the developed task-oriented superpixel generation module can refine the results of the semantic segmentation branch by preserving object boundaries. This study reveals the capability of utilizing deep convolutional neural networks to accomplish both superpixel generation and semantic segmentation of VHR images within an integrated end-to-end framework. The proposed method achieved mIoU scores of 84.32, 90.13, and 55.73 on the Vaihingen, Potsdam, and LoveDA datasets, respectively. These results indicate that our model surpasses the current advanced methods, thus demonstrating the effectiveness of the proposed scheme.

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