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Edge-perception enhanced segmentation method for high-resolution remote sensing image

  • role： First author第一作者
  • Affiliation：

    Center of Hyperspectral Imaging in Remote Sensing of Dalian Maritime University, Dalian 116026, China

  • Email：yuchunyan1997@126.com
  • Introduction：于纯妍，研究方向为高光谱遥感智能解译方法。E-mail：yuchunyan1997@126.com
YU Chunyan1,  
  • Affiliation：

    Center of Hyperspectral Imaging in Remote Sensing of Dalian Maritime University, Dalian 116026, China

LI Donglin1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Center of Hyperspectral Imaging in Remote Sensing of Dalian Maritime University, Dalian 116026, China

  • Email：smping@163.com
  • Introduction：宋梅萍，研究方向为高光谱遥感智能解译方法。E-mail：smping@163.com
SONG Meiping1*,  
  • Affiliation：

    Center of Hyperspectral Imaging in Remote Sensing of Dalian Maritime University, Dalian 116026, China

YU Haoyang1,  
  • Affiliation：

    Center of Hyperspectral Imaging in Remote Sensing of Dalian Maritime University, Dalian 116026, China

    Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA

Chein-I Chang12

реферат

The semantic segmentation method for high-resolution remote sensing images that is based on Deep Convolutional Neural Network (DCNN) has achieved remarkable progress, but problems still exist in the extraction and expression of the edge features of segmented objects. As a result, the edge segmentation effect of occluded and small target objects is unsatisfactory, which affects the overall accuracy of the semantic segmentation method. To solve these problems, this study proposes an edge-aware enhanced semantic segmentation method for high-resolution remote sensing images.First, we utilize the Transformer-DCNN collaborative feature extraction mechanism to extract the global self-attention features and spatial context information of remote sensing images. In this way, the proposed model makes full use of the advantages of Transformer and DCNN to extract global context information and spatial local context information, respectively. The proposed model extracts a highly accurate ground object semantic feature expression and designs a simple but effective feature extraction fusion module to fuse the features extracted by DCNN and Transformer. Second, we construct an edge-aware enhancement module composed of an edge-enhanced decoder and an uncertain point-enhanced decoder. This module enhances the edge information processing ability of the remote sensing image semantic segmentation model from two perspectives, namely, uncertain points and entity edges. Last, the semantic segmentation decoder effectively employs the feature codes containing edge information to improve the accuracy and completeness of segmented object edge prediction, which guarantees that the semantic segmentation effect of remote sensing images is improved overall.Comparative experiments are conducted on two public datasets, namely, Potsdam and Vaihingen. In comparison with the classical Unet++ network, the proposed method demonstrates improvements of 3.44% and 4.01% in mean intersection over union for the two datasets. The average F1 score and overall accuracy exhibit varying degrees of improvement. Furthermore, compared with the Transformer-based TransUNet model, the proposed method achieves better results.Enhancing the feature extraction of edge information in remote sensing objects leads to remarkable improvements in the edge and overall semantic segmentation accuracies of high-resolution remote sensing images. The proposed edge perception enhancement module improves the model’s ability to process edge information from two perspectives, namely, uncertain points and entity edges, thus effectively enhancing the edge segmentation accuracy for complex terrain objects. The results of commonly used evaluation indicators demonstrate the effectiveness and robustness of the developed model.

ключеви́че слова́

remote sensing image;semantic segmentation;Edge perception;feature extraction;Encoder;Decoder

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