Modelling relational contexts in GEOBIA framework for improving urban land-cover mapping

Figures & data

Figure 1. SBR approximation of objects (Du, Shu, and Feng 2016a).

Figure 2. Fitting degree of SBR.

Figure 3. Fitting degree of SBR, where the black line is the skeleton of the polygon.

Figure 4. Delaunay triangulation of buildings, where the red polygons enclose the between/among regions and the yellow polygons refers to the types of triangles (modified from Du et al. 2016b).

Figure 5. Spatial regions of azimuth relations.

Figure 6. Spatial regions of alongness relations.

Figure 7. The among regions, while the red triagnles refer to the first one to be added into the among region.

Figure 8. The surrounding regions, where the yellow polygons refer to the surrounding regions, while the red polygon refers to the among region.

Figure 9. Combination of alongness and azimuth relations.

Figure 10. Test data. (a) Quickbird image in Beijing city and (b) existing vector buildings and roads.

Table 1. The number of training and test samples for each class.

	Buildings	Trees	Shadows	Roads	Water	Other surfaces
Training samples	27	31	20	17	5	20
Test samples	468	598	343	233	24	467

Figure 11. The training samples in the initial classification.

Figure 12. Results of extracted relational contexts. (a) Collinear building patterns, (b) the betweeness relations among all buildings, (c) betweeness regions between buildings within building patterns, and (d) betweeness regions among collinear building patterns.

Figure 13. Results of initial classification. (a) Segmented objects, and (b) classified objects.

Table 2. The features used in the initial classification.

Types	Names	Meanings
Spectrum	[Mean]	The average spectrum of pixels
	[Brightness]	The weighted average spectrum of pixels on each band
	[Max. diff.]	The ratio of the maximal difference of average spectrum of each band to the brightness
	[Std. dev]	The standard deviation of pixels’ grayscale of an image object
	[NDVI]	Normalized difference vegetation index
	[Skewness]	The skewness of grayscale histogram of pixels
	[Ratio]	The ratio of average spectrum to brightness
Texture	[Entropy]	The entropy derived from GLCM
	[Dissimilarity]	The heterogeneity derived from GLCM
	[Homogeneity]	The homogeneity derived from GLCM
	[Contrast]	The contrast derived from GLCM
Geometry	[Area]	The number of pixels within image objects
	[Border length]	The number of pixels on the inner and outer borders
	[Length]	The length of the envelop rectangle
	[Width]	The width of the envelop rectangle
	[Length/Width]	The length-width ratio of the envelop rectangle
	[Roundness]	The difference between the radiuses of the maximal inscribed and minimum circumscribed ellipses
	[Elliptic fit]	The goodness of an image object fitting into an ellipse
	[Border index]	The ratio between the border lengths of the image object and the smallest enclosing rectangle
	[Compactness]	The ratio of the product of length and width to the area an image object
	[Main direction]	The direction of the eigenvector belonging to the larger of the two eigenvalues
	[Rectangular fit]	The goodness of an image object fitting into a rectangle
	[Shape index]	The ratio of perimeter of a building to four times the square root of its area

Table 3. Confusion matrix of the initial classification results.

Predicted Actual	Buildings	Water	Trees	Roads	Surfaces	Shadows	F-Score
Buildings	349	0	3	58	54	4	65.5%
Water	0	17	1	0	0	6	65.4%
Trees	9	1	477	40	55	16	86.0%
Roads	55	0	7	148	14	9	51.4%
Surfaces	182	0	5	73	206	1	51.6%
Shadows	3	10	18	24	2	286	86.0%

Figure 14. Examples of misclassified classes. (a) Buildings classified as roads, (b) buildings as surfaces, (c) water as shadows, (d) trees as surfaces, (e) trees as roads, (f) roads as buildings, (g) roads as surfaces, (h) surfaces as buildings, (i) surfaces as roads, (j) shadows as trees, (k) shadows as roads, and (l) shadows as water.

Figure 15. Retrieved image objects using relational contexts. (a) Overlay of the initial classification and the relational contexts, and (b) the objects located in the relational contexts.

Figure 16. Azimuth and alongness relations for distinguishing water and shadows.

Figure 17. The refined results of post-processing.

Table 4. Confusion matrix of refined classification.

Predicted Actual	Buildings	Water	Trees	Roads	Surfaces	Shadows	F-Score
Buildings	406	0	3	23	32	4	82.9%
Water	0	23	1	0	0	0	95.8%
Trees	1	1	533	7	48	8	92.9%
Roads	46	0	4	167	7	9	72.9%
Surfaces	56	0	2	20	388	1	82.1%
Shadows	2	0	7	8	3	323	93.9%

Figure 18. A map of differences between (a) the initial results and (b) the refined results.

Figure 19. The test on GF-2 data. (a) The experimental image, (b) betweeness regions between buildings within building patterns, (c) the reclassified objects in the post-classification, and (d) the same objects in (c) in the initial classification.

Table 5. Confusion matrix of initial classification results using GF-2 data.

Predicted Actual	Buildings	Water	Trees	Roads	Surfaces	Shadows	F-Score
Buildings	152	0	2	33	5	6	61.7%
Water	1	0	2	0	0	11	0
Trees	32	1	130	6	8	0	81.5%
Roads	4	0	1	98	1	0	62.4%
Surfaces	71	1	4	70	74	0	48.1%
Shadows	35	0	3	3	0	113	79.6%

Table 6. Confusion matrix of refined classification results by relational contexts.

Predicted Actual	Buildings	Water	Trees	Roads	Surfaces	Shadows	F-Score
Buildings	171	0	2	11	8	6	89.8%
Water	1	11	1	0	0	1	88.0%
Trees	1	0	174	1	1	0	98.0%
Roads	4	0	1	98	1	0	78.7%
Surfaces	4	0	0	35	181	0	88.1%
Shadows	2	0	0	0	0	152	97.1%

Table 7. Confusion matrix of refined classification results by the CRF model.

Predicted Actual	Buildings	Water	Trees	Roads	Surfaces	Shadows	F-Score
Buildings	168	0	2	16	7	5	68.2%
Water	2	6	2	0	0	4	57.1%
Trees	22	0	130	6	18	1	81.3%
Roads	5	0	2	95	2	0	71.2%
Surfaces	63	1	6	42	105	3	59.3%
Shadows	35	0	1	4	2	112	80.3%

Figure 20. The classification results. (a) The initial results, (b) the results refined with relational contexts, and (c) the CRF-refined classification results.

Du, S., M. Shu, and -C.-C. Feng. 2016a. “Representation and Discovery of Building Patterns: A Three-Level Relational Approach.” International Journal of Geographic Information Science 30 (6): 1161–1186. doi:10.1080/13658816.2015.1108421.

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Du, S., L. Luo, K. Cao, and M. Shu. 2016b. “Extracting Building Patterns with Multilevel Graph Partition and Building Grouping.” ISPRS Journal of Photogrammetry and Remote Sensing 122: 81–96. doi:10.1016/j.isprsjprs.2016.10.001.

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