Comparison of Layer-stacking and Dempster-Shafer Theory-based Methods Using Sentinel-1 and Sentinel-2 Data Fusion in Urban Land Cover Mapping

Figures & data

Figure 1. Study area.

Figure 2. Land cover classes in the study area.

Figure 3. Process flowchart.

Table 1. Summary of the input datasets.

Dataset	Data sources	Variables	Note
D1	S-1 only	VH, VV	Group 1
D2	S-2 only	2, 3, 4, 5, 6, 7, 8, 8A, 11, 12	Group 1
D3	S-1 with GLCM textures	VH, VV, and textures of mean, correlation, variance, homogeneity, contrast, dissimilarity, entropy, and angular second moment of VH and VV	Group 2
D4	S-2 with indices	2, 3, 4, 5, 6, 7, 8, 8A, 11, 12, NDVI, NDWI	Group 2
D5	D1 and D2	All variables of D1 and D2	Group 1, pixel-level fusion
D6	D3 and D4	All variables of D3 and D4	Group 2, pixel-level fusion
D7	Random forest results of D1 and D2	Probability of each land cover class, and OA, or UA, or PA of each result	Group 1, decision-level fusion
D8	Random forest results of D3 and D4	Probability of each land cover class, and OA, or UA, or PA of each result	Group 2, decision-level fusion

GLCM = gray-level co-occurrence matrix; VV = vertical transmit-vertical receive; VH = vertical transmit-horizontal receive; NDVI = Normalized Difference Vegetation Index; NDWI = Normalized Difference Water Index; OA = overall accuracy; UA = user’s accuracy; PA = producer’s accuracy.

Table 2. Comparison of the overall accuracy and Kappa coefficient of the classification result of all datasets.

Dataset	Overall Accuracy (%)	Kappa coefficient
Group 1: datasets without textures and indices
D7 using OA	90.60	0.88
D7 using PA	90.23	0.88
D7 using UA	90.23	0.88
D2	89.47	0.87
D5	84.59	0.81
D1	42.86	0.29
Group 2: datasets with textures and indices
D8 using UA	92.67	0.91
D8 using PA	91.92	0.90
D8 using OA	91.35	0.89
D4	90.60	0.88
D6	84.02	0.80
D3	52.07	0.41

OA = overall accuracy; UA = user’s accuracy; PA = producer’s accuracy.

Figure 4. Land cover maps from the datasets without textures and indices: (a) dataset D1; (b) dataset D2; (c) dataset D5; (d) dataset D7 using PA.

Figure 5. Land cover maps from the datasets with textures and indices: (a) dataset D3; (b) dataset D4; (c) dataset D6; (d) dataset D8 using PA.

Table 3. The producer’s accuracy and user’s accuracy of the classification result of the datasets without textures and indices.

Dataset	Accuracy index	Class
		BL_H	BL_L	BU_H	BU_L	VE	WA
D1	PA (%)	10.71	46.51	41.57	43.70	64.35	23.53
	UA (%)	60.00	31.50	37.37	51.30	49.01	40.00
D2	PA (%)	91.07	93.02	87.64	83.70	91.30	96.08
	UA (%)	89.47	73.39	92.86	91.13	97.22	98.00
D5	PA (%)	92.86	61.63	92.13	86.67	86.96	90.20
	UA (%)	94.55	69.74	95.35	71.34	97.09	95.83
D7 using OA	PA (%)	91.07	94.19	86.52	88.15	92.17	94.12
	UA (%)	89.47	78.64	95.06	89.47	97.25	97.96
D7 using UA	PA (%)	91.07	88.37	86.52	89.63	92.17	96.08
	UA (%)	89.47	83.52	92.77	86.43	96.36	96.08
D7 using PA	PA (%)	91.07	95.35	86.52	85.93	92.17	94.12
	UA (%)	89.47	77.36	95.06	89.92	96.36	97.96

BL_H = Bare land with high albedo; BL_L = Bare land with low albedo; BU_H = Built-up with high albedo; BU_L = Built-up with low albedo; VE = Vegetation; WA = Open water surface; OA = overall accuracy; UA = user’s accuracy; PA = producer’s accuracy.

Table 4. The producer’s accuracy and user’s accuracy of the classification result of the datasets with textures and indices.

Dataset	Accuracy index	Class
		BL_H	BL_L	BU_H	BU_L	VE	WA
D3	PA (%)	7.14	47.67	78.65	45.93	60.00	60.78
	UA (%)	50.00	44.57	46.67	50.82	58.47	73.81
D4	PA (%)	96.43	91.86	92.13	83.70	95.65	86.27
	UA (%)	94.74	73.83	94.25	91.87	96.49	100.00
D6	PA (%)	83.93	63.95	91.01	87.41	87.83	88.24
	UA (%)	95.92	74.32	88.04	69.82	98.06	100.00
D8 using OA	PA (%)	91.07	90.70	95.51	87.41	95.65	86.27
	UA (%)	98.08	75.00	97.70	90.77	95.65	100.00
D8 using UA	PA (%)	94.64	87.21	95.51	91.11	94.78	94.12
	UA (%)	98.15	83.33	98.84	89.13	95.61	96.00
D8 using PA	PA (%)	92.86	91.86	95.51	86.67	95.65	90.20
	UA (%)	98.11	75.96	97.70	92.13	95.65	100.00

Figure 6. Comparison of the classification results from the datasets with textures and indices in three example regions.

Data availability statement

The satellite images can be downloaded from the Copernicus Scientific Data Hub (https://scihub.copernicus.eu/). The administrative boundary can be downloaded from the Database of Global Administrative Areas project website (https://gadm.org/). The ground truth data and R code are available from the corresponding author, [Dang Hung Bui], upon reasonable request.