基于改进CycleMLP的高分遥感图像采石场识别

doi:10.13474/j.cnki.11-2246.2023.0045

测绘通报 ›› 2023, Vol. 0 ›› Issue (2): 84-90.doi: 10.13474/j.cnki.11-2246.2023.0045

基于改进CycleMLP的高分遥感图像采石场识别

赵宇滨¹, 倪欢¹, 牛晓楠²

1. 南京信息工程大学遥感与测绘工程学院, 江苏南京 210044;
2. 中国地质调查局南京地质调查中心, 江苏南京 210016

收稿日期:2022-10-14 发布日期:2023-03-01
通讯作者: 倪欢。E-mail:nih@nuist.edu.cn
作者简介:赵宇滨(1998-),男,硕士生,研究方向为深度学习在遥感图像处理中的应用。E-mail:919296237@qq.com
基金资助:
国家自然科学基金(41801384);江苏省自然科学基金(BK20180795);南京信息工程大学人才启动项目(2018r030)

Quarry recognition based on improved CycleMLP from high-resolution remote sensing imagery

ZHAO Yubin¹, NI Huan¹, NIU Xiaonan²

1. School of Remote Sensing & Geomatics Engineering Nanjing, University of Information Science & Technology, Nanjing 210044, China;
2. Nanjing Center of China Geological Survey, Nanjing 210016, China

Received:2022-10-14 Published:2023-03-01

摘要/Abstract

摘要： 石矿区生态修复是改善区域生态系统功能的重要环节,识别采石场、确定采矿区边界是完成修复任务的前提。目前,基于深度学习的语义分割技术,能够精准识别高分遥感图像中的感兴趣地物,为采石场识别提供了有效途径。本文基于CycleMLP框架,利用金字塔结构,将多级特征输入到一个轻量级MLP解码器中,聚合来自不同层次的特征信息,同时获取局部和全局特征。在前馈网络中嵌入卷积层,避免位置编码插值导致的精度下降现象。引入福建省南安市石矿区语义分割数据集,以训练网络和验证算法精度。结果表明,改进后的CycleMLP能够从高分遥感图像中有效识别石矿区,与其他基于自注意力机制的方法相比,精度更高,且可以准确界定石矿区边界,能够为修复石矿区生态系统提供可靠支撑材料。

关键词: 采石场, 高分遥感图像, MLP, 解码器, 生态修复

Abstract: The ecological restoration of the quarry area is an important link to improve the function of the regional ecosystem. Identifying the quarry and determining the boundary of the mining area are the prerequisites for completing the restoration task. Currently, the semantic segmentation technology based on deep learning can accurately recognize the objects of interest in high-resolution remote sensing images, providing an effective way for quarry identification. Based on the CycleMLP framework, this paper uses the pyramid structure to input multi-level features into a lightweight MLP decoder, aggregates feature information from different levels, and obtains local and global features at the same time. The convolutional layers are embedded into the feed-forward network, which avoids the accuracy decline caused by position-encoded interpolation. Experiments employ the semantic segmentation dataset of the quarry areas in Nan'an city, Fujian province to train the network and verify the accuracy. The results show that the improved CycleMLP can effectively identify quarry areas from high-resolution remote sensing images. Compared with other methods based on self-attention mechanism, the improved CycleMLP achieves higher accuracy, accurately extracts the boundary of the quarry area, and can provide reliable supporting materials for the restoration of the ecosystem in the quarry area.

Key words: quarry, high-resolution remote sensing images, MLP, decoder, ecological restoration

中图分类号:

P237

赵宇滨, 倪欢, 牛晓楠. 基于改进CycleMLP的高分遥感图像采石场识别[J]. 测绘通报, 2023, 0(2): 84-90.

ZHAO Yubin, NI Huan, NIU Xiaonan. Quarry recognition based on improved CycleMLP from high-resolution remote sensing imagery[J]. Bulletin of Surveying and Mapping, 2023, 0(2): 84-90.

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基于改进CycleMLP的高分遥感图像采石场识别

Quarry recognition based on improved CycleMLP from high-resolution remote sensing imagery

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