A Semantic Segmentation Method for Remote Sensing Images Based on an Improved TransDeepLab Model

Jinxin Wang, Manman Wang (), Kaiwei Cong and Zilong Qin
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Jinxin Wang: School of Geoscience & Technology, Zhengzhou University, Zhengzhou 450001, China
Manman Wang: School of Geoscience & Technology, Zhengzhou University, Zhengzhou 450001, China
Kaiwei Cong: School of Computer and Information Technology, Northeast Petroleum University, Daqing 163000, China
Zilong Qin: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

Land, 2024, vol. 14, issue 1, 1-17

Abstract: Due to the various types of land cover and large spectral differences in remote sensing images, high-quality semantic segmentation of these images still faces challenges such as fuzzy object boundary extraction and difficulty in identifying small targets. To address these challenges, this study proposes a new improved model based on the TransDeepLab segmentation method. The model introduces a GAM attention mechanism in the coding stage, and incorporates a multi-level linear up-sampling strategy in the decoding stage. These enhancements allow the model to fully utilize multi-level semantic information and small target details in high-resolution remote sensing images, thereby effectively improving the segmentation accuracy of target objects. Using the open-source LoveDA large remote sensing image datasets for the validation experiment, the results show that compared to the original model, the improved model’s MIOU increased by 2.68%, aACC by 3.41%, and mACC by 4.65%. Compared to other mainstream models, the model also achieved superior segmentation performance.

Keywords: deep learning; high-resolution remote sensing image; semantic segmentation; feature extraction (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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