基于全卷积神经网络和低分辨率标签的森林变化检测研究

向俊; 严恩萍; 姜镓伟; 宋亚斌; 韦维; 莫登奎

doi:10.12302/j.issn.1000-2006.202204069

向俊, 严恩萍, 姜镓伟, 宋亚斌, 韦维, 莫登奎

南京林业大学学报（自然科学版） ›› 2024, Vol. 48 ›› Issue (1) : 187-195.

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南京林业大学学报（自然科学版） ›› 2024, Vol. 48 ›› Issue (1) : 187-195. DOI: 10.12302/j.issn.1000-2006.202204069

研究论文

基于全卷积神经网络和低分辨率标签的森林变化检测研究

向俊 ¹^,² ,
严恩萍 ² ,
姜镓伟 ²^,³ ,
宋亚斌 ⁴ ,
韦维 ¹^,^* ,
莫登奎 ²^,^*

作者信息 +

Research on forest change detection based on fully convolutional network and low resolution label

XIANG Jun ¹^,² ,
YAN Enping ² ,
JIANG Jiawei ²^,³ ,
SONG Yabin ⁴ ,
WEI Wei ¹^,^* ,
MO Dengkui ²^,^*

Author information +

文章历史 +

摘要

【目的】针对目前森林变化检测中高精度标签样本缺失或不足的问题,提出一种基于全卷积神经网络和低分辨率标签的森林变化检测方法,旨在实现林地区域内森林变化的简易快速提取。【方法】首先对获取的数据进行去云、筛选、标签融合等预处理,利用全卷积神经网络模型分别提取2020年和2021年研究区森林高分遥感影像,并评价模型精度;利用分类后比较法获取森林变化区域,得到变化结果并与目视解译结果进行对比,基于像素面积计算森林变化检测的精确率等评价指标。【结果】所用全卷积神经网络(FCN)模型在2020年森林提取结果的精确率和召回率的调和均值(F₁分数)为97.09%,2021年森林提取结果的F₁分数为95.96%,与分割网络模型(U-Net、FPN、LinkNet)相比更优。比较两期森林提取结果得到变化区域,森林增加与森林减少的合计变化精确率为73.30%,召回率为77.37%,F₁分数为75.28%。【结论】该方法实现了基于低分辨率标签对高分遥感影像森林变化区域进行快速、准确的获取。采用少量的低分辨率标签完成森林变化检测任务,同时可为大面积林地变更调查提供参考。

Abstract

【Objective】A forest change detection method based on fully convolutional networks and low resolution labels is proposed to address the problem of missing or insufficient high-precision label samples in current forest change detection, with the goal of achieving simple and rapid extraction of forest changes in forest areas. 【Method】First, the gathered data was de-clouded, screened and labeled, and then the fully convolutional network model was used to extract the forests in high-scoring remote sensing photos in the study area in 2020 and 2021, respectively, and the model accuracy was evaluated. The forest change area was calculated using the post-classification comparison method, and the findings were compared with visual interpretation results. The pixel area was used to calculate evaluation indicators, such as forest change detection accuracy. 【Result】Experiments reveal that the F₁ score of the model employed in this research is 97.09% in 2020 forest extraction results and 95.96% in 2021 forest extraction results, which was the best among segmentation network models (U-Net, FPN, LinkNet). The total change precision rate of forest increase and forest decline was 73.30%, the recall rate was 77.37%, and the F₁ score was 75.28% when comparing the forest extraction data from the two periods to obtain the changed area. 【Conclusion】Based on low resolution labeling, this method allows for the speedy and precise capture of forest change regions from high-resolution remote sensing pictures. To accomplish forest change detection, a small number of low-resolution labels are used, which can also serve as a reference for large-scale forestland change inquiries.

导出引用

向俊, 严恩萍, 姜镓伟, 等. 基于全卷积神经网络和低分辨率标签的森林变化检测研究[J]. 南京林业大学学报（自然科学版）. 2024, 48(1): 187-195 https://doi.org/10.12302/j.issn.1000-2006.202204069

XIANG Jun, YAN Enping, JIANG Jiawei, et al. Research on forest change detection based on fully convolutional network and low resolution label[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2024, 48(1): 187-195 https://doi.org/10.12302/j.issn.1000-2006.202204069

中图分类号： TP391.4;S757

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