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

XIANG Jun, YAN Enping, JIANG Jiawei, SONG Yabin, WEI Wei, MO Dengkui

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2024, Vol. 48 ›› Issue (1) : 187-195.

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

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

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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.

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low resolution label / fully convolutional network / deep learning / forest changes detection

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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

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