MRU-Net: A remote sensing image segmentation network for enhanced edge contour Detection

• Jing Han,
• Weiyu Wang,
• Yuqi Lin,
• Xueqiang LYU,

Vol. 17, No. 12, December 31, 2023
10.3837/tiis.2023.12.008, Download Paper (Free):

• Convolutional Neural Network
• image processing
• Hybrid multiscale identification module
• Micro residual structure
• Remote sensing image segmentation

Abstract

Remote sensing image segmentation plays an important role in realizing intelligent city construction. The current mainstream segmentation networks effectively improve the segmentation effect of remote sensing images by deeply mining the rich texture and semantic features of images. But there are still some problems such as rough results of small target region segmentation and poor edge contour segmentation. To overcome these three challenges, we propose an improved semantic segmentation model, referred to as MRU-Net, which adopts the U-Net architecture as its backbone. Firstly, the convolutional layer is replaced by BasicBlock structure in U-Net network to extract features, then the activation function is replaced to reduce the computational load of model in the network. Secondly, a hybrid multi-scale recognition module is added in the encoder to improve the accuracy of image segmentation of small targets and edge parts. Finally, test on Massachusetts Buildings Dataset and WHU Dataset the experimental results show that compared with the original network the ACC、mIoU and F1 value are improved, and the imposed network shows good robustness and portability in different datasets.

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