Urban land use land cover classification based on GF-6 satellite imagery and multi-feature optimization

Figures & data

Figure 1. Location of study area, with true colour composite map illustrating full extent of the study area.

Table 1. Spectral bands of the GF-6 satellite.

Band name	Bandwidth (μm)	Spatial resolution (m)
Band 1 Blue	0.45 ∼ 0.52	16
Band 2 Green	0.52 ∼ 0.59	16
Band 3 Red	0.63 ∼ 0.69	16
Band 4 NIR	0.77 ∼ 0.89	16
Band 5 Red edge1	0.69 ∼ 0.73	16
Band 6 Red edge2	0.73 ∼ 0.77	16
Band 7 Purple	0.40 ∼ 0.45	16
Band 8 Yellow	0.59 ∼ 0.63	16

Figure 2. The overall framework of the urban land use/land cover classifications using GF-6 imagery.

Table 2. Features extracted from GF-6 images.

Feature serial number	Feature variable	Feature description	Abbreviation of features
1 ∼ 8	Blue Band、 Green Band、Red Band、 Near infrared band、 Rededge_1 Band、 Rededge_2 Band、 Yellow Band、Purple Band	Spectral bands of GF-6 WFV image	Blue, Green, Red, Nir, Rededge1, Rededge2, Yellow, Purple
9	$\mathit{BAI}=(\mathit{blue}-\mathit{NIR})/(\mathit{blue}+\mathit{NIR})$ (Shao et al. 2016)	Built-up index	BAI
10 ∼ 15	$\mathit{NDVI}=(\mathit{NIR}-\mathit{red})/(\mathit{NIR}+\mathit{red})$ (Carlson and Ripley 1997)	Vegetation index	NDVI1 ∼NDVI6
16	$\mathit{SAVI}=(1+L)(\mathit{NIR}-\mathit{red})/(\mathit{NIR}+\mathit{red}+L)$ (Huete 1988)		SAVI
17	$\mathit{EVI}=2.5\frac{(\mathit{NIR}-\mathit{red})}{\mathit{NIR}+6red-7.5b\mathit{lue}+1}$ (Ahamed et al. 2011)		EVI
18	$\mathit{RVI}=\mathit{NIR}/\mathit{red}$ (Duveiller et al. 2011)		RVI
19	$\mathit{NDWI}=(\mathit{green}-\mathit{NIR})/(\mathit{green}+\mathit{NIR})$ (Gao 1996)	Water index	NDWI
20	$\mathit{CIWI}=\mathit{NDVI}+\mathit{nir}$ (Zhang et al. 2018)		CIWI
21	$\mathit{SRWI}=\mathit{Green}/\mathit{NIR}$ (Zarco-Tejada et al. 2003)		SRWI
22 ∼ 33	$\mathit{NDVIre}=(\mathit{NIR}-re{d}_{\mathit{ged}})/(\mathit{NIR}+re{d}_{\mathit{ged}})$ (Zhang et al. 2019)	Red-edge index	NDVIre1_1 ∼NDVIre1_6, NDVIre2_1 ∼NDVIre2_6,
34	$N{D}_{re}=(re{d}_{\mathit{ged}1}-re{d}_{\mathit{ged}2})/(re{d}_{\mathit{ged}1}+re{d}_{\mathit{ged}2})$ (Ehammer et al. 2010)		NDre
36 ∼ 50	Mean、Contrast、Variance、 Entropy 、Homogeneity、 Second Moment 、Correlation、Dissimilarity	Grey level co-occurrence matrix index calculated by the first two principal components	PC1MEAN, PC1V, PC1E, PC1CON, PC1HOM, PC1SEC, PC1COR, PC1DIS, PC2MEAN, PC2V, PC2E, PC2CON, PC2HOM, PC2SEC, PC2COR, PC2DIS

Table 3. Experimental schemes for seven feature sets.

Classification scheme	Feature set	Feature serial number
Scheme 1	Spectral feature (all bands)	1 ∼ 8
Scheme 2	Spectral feature (band1 – band 4)	1 ∼ 4
Scheme 3	Spectrum + built-up + water + Vegetation index	1 ∼ 8, 9 ∼ 21
Scheme 4	Spectrum + texture	1 ∼ 8, 35 ∼ 50
Scheme 5	Spectrum + Red edge index	1 ∼ 8, 22 ∼ 24
Scheme 6	Spectrum + index + texture	1 ∼ 50
Scheme 7	optimal feature	2, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 17, 18, 22, 24, 26, 27, 29, 36

Figure 3. The classification process for Random Forests and Extreme Gradient Boosting.

Figure 4. Relevance ranking based on mRMR algorithm. The horizontal axis represents the feature name. The vertical axis represents the relevance score of each feature.

Figure 5. Max-relevance and min-redundancy ranking based on mRMR algorithm. The horizontal axis represents the feature name. The vertical axis represents the mRMR score of each feature.

Figure 6. Relationship between number of features, OA and Kappa based on max-relevance and min-redundancy ranking for the two tree-based classifier.

Figure 7. Relationship between number of features, OA and Kappa based on relevance ranking for the two tree-based classifier.

Figure 8. Classification results of seven feature schemes; the black rectangle is area 1, the black ellipse is area 2.

Figure 9. Comparisons of overall accuracy and Kappa derived from RF in different feature schemes.

Figure 10. Detailed classification results of seven schemes in area 1.

Figure 11. The producer’s accuracy of different categories in 7 schemes.

Figure 12. The User’s accuracy of different categories in 7 schemes.

Table 4. Confusion matrix of the classification results using the optimal features.

Class	Ground truth
	F	B	G	U	C	W	Total	UA(%)
F	239	1	7	0	0	0	247	96.76
B	0	238	7	4	1	0	250	95.2
G	27	2	186	0	21	0	236	78.81
U	0	12	10	219	2	0	243	90.12
C	4	2	19	0	221	0	246	89.84
W	0	4	3	0	1	242	250	96.8
Total	270	259	232	223	246	242
PA (%)	88.52	91.89	80.17	98.21	89.84	100
OA= 91.37% Kappa = 0.90

Note: F – Forestland, B – Built-up land, G – Grassland, U – Unused land, C – Cropland, W – Water.

Figure 13. A comparison of LULC classifications among (a) our proposed method, (b) the ESA WorldCover, and (c) GlobeLand30.

Table

Scheme	type	PA	UA
Scheme 1	Forestland	88.37%	92.3%
	Built-up land	77.45%	94.8%
	Grassland	61.53%	57.63%
	Unused land	94.42%	90.53%
	Cropland	66.67%	59.35%
	water	100%	94%
Scheme 2	Forestland	87.98%	91.9%
	Built-up land	77.81%	94%
	Grassland	59.72%	54.66%
	Unused land	93.51%	88.89%
	Cropland	64.63%	60.16%
	water	100%	94%
Scheme 3	Forestland	88.05%	95.54%
	Built-up land	91.19%	95.2%
	Grassland	78.18%	72.88%
	Unused land	97.77%	90.12%
	Cropland	85.21%	89.02%
	water	100%	96.8%
Scheme 4	Forestland	88.08%	92.71%
	Built-up land	71.52%	88.4%
	Grassland	59.48%	58.47%
	Unused land	91.95%	89.3%
	Cropland	59.9%	47.97%
	water	99.57%	94.8%
Scheme 5	Forestland	87.91%	97.17%
	Built-up land	88.3%	93.6%
	Grassland	78.54%	77.54%
	Unused land	96.88%	89.3%
	Cropland	90.76%	87.8%
	water	100%	95.6%
Scheme 6	Forestland	88.85%	96.76%
	Built-up land	92.25%	952%
	Grassland	78.17%	75.85%
	Unused land	97.32%	89.71%
	Cropland	87.2%	88.62%
	water	100%	96.8%
Scheme 7	Forestland	88.52%	96.76%
	Built-up land	91.89%	95.2%
	Grassland	80.17%	78.81%
	Unused land	98.2%	90.12%
	Cropland	89.84%	89.84%
	water	100%	96.8%

Table

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Data availability statement

The data that support the findings of this study are available from the corresponding author, WZ, upon reasonable request.