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

doi:10.12302/j.issn.1000-2006.202204069

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2024, Vol. 48 ›› Issue (1): 187-195.doi: 10.12302/j.issn.1000-2006.202204069

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

XIANG Jun¹^,²(), YAN Enping², JIANG Jiawei²^,³, SONG Yabin⁴, WEI Wei¹^,^*(), MO Dengkui²^,^*()

1. Guangxi Zhuang Autonomous Region Forestry Research Institute, Nanning 530002, China
2. Hunan Provincial Key Laboratory of Forestry Remote Sensing Big Data and Ecological Security, College of Forestry, Central South University of Forestry and Technology, Changsha 410004, China
3. School of Civil Engineering, Sun Yat-Sen University, Zhuhai 519082, China
4. Central South Inventory and Planning Institute of National Forestry and Grassland Administration, Changsha 410014, China

Received:2022-04-29 Revised:2022-06-19 Online:2024-01-30 Published:2024-01-24
Contact: WEI Wei,MO Dengkui E-mail:15084934667@163.com;22010230@qq.com;Dengkuimo@csuft.edu.cn

Abstract

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.

Key words: low resolution label, fully convolutional network, deep learning, forest changes detection

CLC Number:

TP391.4
S757

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

Figures/Tables 9

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森林检测模型 forest detection model	年份 year	样本总面积/ km² total sample area	森林面积/ km² forest area	S_TP/km²	S_FP/km²	S_FN/km²	精确率/% precision	召回率/% recall	F₁分数/% F₁ score
FPN	2020	400	347.07	338.25	21.77	8.82	93.95	97.46	95.67
	2021	400	344.78	335.57	24.02	9.21	93.32	97.33	95.28
LinkNet	2020	400	347.07	336.87	22.65	10.20	93.70	97.06	95.35
	2021	400	344.78	328.27	14.90	16.51	95.66	95.21	95.43
U-Net	2020	400	347.07	339.36	18.55	7.71	94.82	97.78	96.28
	2021	400	344.78	314.65	11.36	30.13	96.52	91.26	93.81
FCN	2020	400	347.07	339.96	13.26	7.11	96.25	97.95	97.09
	2021	400	344.78	343.57	27.73	1.21	92.53	99.65	95.96

森林变化 forest change	真实变化面积/ hm² true change area	预测变化面积/ hm² predicted change area	S_TP/ hm²	S_FP/ hm²	S_FN/ hm²	精确率/% precision	召回率/% recall	F₁分数/% F₁ score
森林减少 forest reduction	369.46	386.56	286.60	99.96	82.85	74.14	77.57	75.82
森林增加 forest increase	84.04	92.12	64.26	27.86	19.78	69.76	76.46	72.96
合计变化 total change	453.50	478.68	350.86	127.82	102.63	73.30	77.37	75.28

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

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