基于深度学习的寒旱区多时序影像土地利用及变化监测——以新疆莫索湾垦区为例

doi:10.12118/j.issn.1000–6060.2021.06.20

Abstract

Abstract:

In this study, we conducted researchon the extraction of ground elements and the dynamic monitoring of land cover change in cold and arid areas with fragile ecological environments. The Mosuowan reclamation area in Xinjiang, China was selected as the target area, and the Landsat series satellite images from 2015 to 2019 were cut into subimages with sizes of 416×416 px. Specifically, the training set, validation set and testset are 306 subimages, 204 subimages and 120 subimages respectively. Three traditional methods were then evaluated: MLC,SVM, and RF. Five semantic segmentation models were also evaluated: DeepLabv3+(Xception), DeepLabv3+ (MobileNet), SegNet (ResNet50), U-Net (MobileNet), and PSPNet (MobileNet). In the evaluation experiments, DeepLabv3+ (Xception) was found to achieve the optimal segmentation effect and multiscale feature fusion using fewer parameters. The overall accuracy, Kappa coefficient, precision, recall, F1-score, and MIoU were 96.06%, 0.96, 87.69%, 83.78%, 0.86, and 0.77, respectively. The MIoU of the DeepLabv3+(Xception) model was significantly better than those of the other four models, improving from 0.03 to 0.39. On the basis of the land use classification results of the long-term timeseries remote sensing data from 1998 to 2020, we analyze spatial structure change in land use and the associated driving factors. Over the past 23 years, the total areas of desert, agricultural land, and construction land have been reduced by 15.00%, 12.68%, 2.53%, respectively. At the same time, the amount of water area has remained relatively stable. The overall transformation direction of land use is from desert to agricultural land and then from agricultural land to construction land. It can be seen from the results that the desertification control was effective, and urbanization rapidly developed. Consequently, this study can provide a reference for the application of deep learning in the field of medium-resolution remote sensing images, which can be used to realize the dynamic monitoring of land use and change.

Key words: remote sensing image, long time-series, deep learning, DeepLabv3+, feature classification, dynamic monitoring

YUAN Panli,WANG Chuanjian,ZHAO Qingzhan,WANG Xuewen,REN Yuanyuan,YANG Qiyuan. Dynamic monitoring of land-use/land-cover change in cold and arid region based on deep learning: A case study of Mosuowan reclamation area in Xinjiang[J].Arid Land Geography, 2021, 44(6): 1717-1728.

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传感器类型	发射时间(年-月-日)	空间分辨率/m	时间分辨率/d	年份	云量范围/%
Landsat-5/TM	1984-03-01	30	16	1998,2006—2011	0.00~3.00
Landsat-7/ETM+	1999-04-15	30	16	1999—2005	0.00~6.45
Landsat-8/OLI	2013-02-11	30	16	2013—2020	0.00~2.39

地物类型	包含类型	颜色编码 (Red,Green,Blue)
建筑用地	城镇、村庄、工业建筑用地等	(1,1,1)
农用地	耕地(旱地、水田、光果园等)、林地(灌木林地、有林地等)、草地(天然牧草地、其他草地)等	(2,2,2)
水体	滩涂、坑塘、水库、湿地等	(3,3,3)
荒漠	沙漠、未利用土地等	(4,4,4)
背景	研究区范围以外的区域	(0,0,0)

模型	评价指标
模型	OA/%	Kappa	precision/%	recall/%	F1值	MIoU
MLC	62.73	0.42	52.78	71.17	0.51	0.38
SVM	68.32	0.48	52.12	73.18	0.54	0.41
RF	76.35	0.59	75.59	85.69	0.75	0.64
DeepLabv3+ (Xception)	96.06	0.96	87.69	83.78	0.86	0.77
DeepLabv3+ (MobileNet)	94.77	0.94	74.59	75.82	0.75	0.62
SegNet (ResNet50)	95.47	0.95	81.94	81.04	0.81	0.71
U-Net (MobileNet)	95.83	0.95	85.23	83.13	0.83	0.74
PSPNet (MobileNet)	95.52	0.90	85.34	81.08	0.83	0.74

地物类型	指标	语义分割模型
地物类型	指标	DeepLabv3+ (Xception)	DeepLabv3+ (MobileNet)	SegNet (ResNet50)	U-Net (MobileNet)	PSPNet (MobileNet)
农用地	precision/%	91.25	89.02	89.46	91.68	89.27
	recall/%	91.16	88.82	88.66	89.31	86.85
	F1值	0.91	0.88	0.89	0.90	0.87
建筑用地	precision/%	81.12	66.73	78.65	80.79	75.83
	recall/%	70.36	47.95	63.46	69.07	71.68
	F1值	0.74	0.53	0.69	0.74	0.73
水体	precision/%	99.24	95.58	97.80	97.76	96.31
	recall/%	90.19	96.37	95.37	95.21	96.77
	F1值	0.94	0.96	0.97	0.96	0.97
荒漠	precision/%	81.72	80.17	77.16	77.02	79.55
	recall/%	77.64	67.50	74.66	78.30	70.27
	F1值	0.78	0.70	0.75	0.76	0.73

日期（年-月）	评价指标
日期（年-月）	OA/%	Kappa
1998-08	96.79	0.95
2020-08	97.49	0.96
实验数据	96.06	0.96

Dynamic monitoring of land-use/land-cover change in cold and arid region based on deep learning: A case study of Mosuowan reclamation area in Xinjiang

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2020年地物类型	1998年地物类型
2020年地物类型	农用地	建筑用地	水体	荒漠
农用地	778.40	11.32	1.02	222.39
建筑用地	37.05	11.61	0.19	11.05
水体	0.86	0.01	34.00	0.40
荒漠	12.81	0.34	3.09	326.52

地物类型	面积变化/km²			单一土地利用动态度/%
地物类型	1998—2008年	2008—2020年	1998—2020年	1998—2008年	2008—2020年	1998—2020年
农用地	+162.43	+22.77	+185.20	+1.96	+0.23	+1.01
建筑用地	+4.65	+32.12	+36.77	+2.00	+11.49	+7.17
水体	-5.49	+2.49	-3.00	-1.43	+0.76	-0.36
荒漠	-160.86	-56.83	-217.69	-2.87	-1.42	-1.76

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