A New Instance Segmentation Model for High-Resolution Remote Sensing Images Based on Edge Processing

Xiaoying Zhang, Jie Shen, Huaijin Hu and Houqun Yang ()
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Xiaoying Zhang: School of Computer Science and Technology, Hainan University, Haikou 570228, China
Jie Shen: School of Computer Science and Technology, Hainan University, Haikou 570228, China
Huaijin Hu: School of Computer Science and Technology, Hainan University, Haikou 570228, China
Houqun Yang: School of Computer Science and Technology, Hainan University, Haikou 570228, China

Mathematics, 2024, vol. 12, issue 18, 1-17

Abstract: With the goal of addressing the challenges of small, densely packed targets in remote sensing images, we propose a high-resolution instance segmentation model named QuadTransPointRend Net (QTPR-Net). This model significantly enhances instance segmentation performance in remote sensing images. The model consists of two main modules: preliminary edge feature extraction (PEFE) and edge point feature refinement (EPFR). We also created a specific approach and strategy named TransQTA for edge uncertainty point selection and feature processing in high-resolution remote sensing images. Multi-scale feature fusion and transformer technologies are used in QTPR-Net to refine rough masks and fine-grained features for selected edge uncertainty points while balancing model size and accuracy. Based on experiments performed on three public datasets: NWPU VHR-10, SSDD, and iSAID, we demonstrate the superiority of QTPR-Net over existing approaches.

Keywords: instance segmentation; high-resolution remote sensing images; feature pyramid network; transformer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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