Urban land-use classification by combining high-resolution optical and long-wave infrared images

Figures & data

Figure 1. The overall classification framework.

Table 1. Number of training and testing samples.

Class	Training numbers	Testing numbers	Testing numbers
		(down-sampled map)	(original map)
Road	2236	45,061	1,134,943
Trees	552	6928	219,695
Red roof	920	9178	231,192
Gray roof	1067	12,382	312,039
Concrete roof	1942	4829	122,046
Vegetation	3691	15,619	412,647
Bare soil	894	3985	115,697
Total	11,302	97,982	2,548,259

Table 2. Class-specific accuracies of spectral classification for optical and LWIR image.

Accuracy	Optical	LWIR
OA	74.2	54.2
AA	78.0	34.3
KAPPA	66.5	37.1
Road	74.2	90.9
Trees	83.1	12.0
Red roof	81.3	44.3
Gray roof	82.6	21.5
Concrete roof	87.3	17.5
Vegetation	52.1	13.5
Bare soil	85.5	40.7

Note: average accuracy (AA), overall accuracy (OA).

Table 3. Class-specific accuracies of spectral-spatial classifications for both optical and LWIR image.

Accuracy	GLCM		EAP		EMP		DMP		3D DWT
	Opt	LWIR	Opt	LWIR	Opt	LWIR	Opt	LWIR	Opt	LWIR
OA	80.2	58.7	79.3	61.3	75.9	61.8	78.2	57.8	83.4	62.9
AA	81.6	41.9	80.1	42.4	78.2	43.9	79.1	42.5	80.7	46.9
KAPPA	74.1	43.8	72.9	47.2	68.6	47.8	71.4	43.8	77.5	49.2
Road	80.7	89.6	81.0	96.5	80.1	95.7	82.4	85.3	94.4	95.4
Trees	86.4	29.0	87.6	40.0	92.7	38.4	81.6	41.4	81.9	45.2
Red roof	86.5	43.0	82.3	76.1	85.6	49.3	85.2	27.8	85.4	59.9
Gray roof	93.6	41.6	90.4	17.6	81.7	45.8	88.7	49.6	84.3	26.9
Concrete roof	79.3	28.8	69.5	21.7	87.1	14.9	81.9	18.3	86.7	24.5
Vegetation	61.2	19.0	60.7	15.9	41.8	11.8	49.2	24.3	49.4	21.0
Bare soil	83.4	42.1	89.0	28.9	78.6	51.4	84.5	50.5	83.1	55.3

Figure 2. Percentage of improvement for the AA obtained by the spectral-spatial classification methods, compared to the raw spectral-based method.

Table 4. Class-specific accuracies for the joint classification optical and LWIR image (with spatial features calculated on the first PC of optical image).

Accuracy	Spectral	GLCM	EAP	EMP	DMP	3D DWT
OA	79.0	82.6	79.8	78.5	78.6	83.3
AA	79.9	81.1	80.5	79.7	79.3	81.1
KAPPA	72.4	76.9	73.5	71.8	71.9	77.5
Road	84.4	84.9	81.6	83.7	84.2	94.5
Trees	85.2	93.5	88.3	92.6	84.6	84.3
Red roof	94.0	87.7	85.2	90.0	93.0	89.9
Gray roof	90.5	90.8	91.4	91.3	92.0	91.2
Concrete roof	86.8	76.6	69.4	83.1	81.2	85.1
Vegetation	40.7	67.7	59.4	39.0	39.1	40.2
Bare soil	78.0	66.8	88.5	78.1	81.4	82.3

Table 5. Class-specific accuracies for the joint classification optical and LWIR Image (with spatial features calculated on the first PC of LWIR image).

Accuracy	Spectral	GLCM	EAP	EMP	DMP	3D DWT
OA	79.0	76.2	76.8	76.0	77.6	78.4
AA	79.9	76.0	74.8	75.5	78.6	77.9
KAPPA	72.4	68.8	69.0	68.1	70.7	71.3
Road	84.4	82.2	88.0	86.0	82.8	87.9
Trees	85.2	76.2	81.4	84.1	82.6	81.4
Red roof	94.0	92.7	92.3	94.2	93.4	92.4
Gray roof	90.5	89.5	80.5	82.9	89.1	84.8
Concrete roof	86.8	86.0	82.9	85.1	87.1	84.2
Vegetation	40.7	37.4	30.9	26.2	39.9	34.4
Bare soil	78.0	68.4	67.4	69.9	75.6	80.3

Table 6. Class-specific accuracies for the multiple features classification.

	Stacking	Voting	Probability	Uncertainty
OA	77.2	82.5	83.4	83.6
AA	77.8	83.7	83.9	83.9
KAPPA	70.2	76.9	78.0	78.3
Road	75.4	87.2	88.2	88.4
Trees	93.9	90.2	91.4	91.7
Red roof	74.4	92.1	92.6	92.2
Gray roof	83.1	94.9	95.9	95.7
Concrete roof	79.7	85.3	82.1	81.8
Vegetation	74.9	47.9	50.6	51.5
Bare soil	63.0	88.4	86.3	85.6

Table 7. Class-specific accuracies for the object-based classification.

Accuracy	Stacking	Voting	Probability	Uncertainty	Spectral-spatial GLCM	Joint GLCM	Spectral-spatial 3D DWT	Joint 3D DWT
OA	83.0	86.6	87.0	87.1	87.3	86.9	84.0	84.2
AA	78.3	84.9	84.5	84.6	84.9	84.4	81.2	81.9
KAPPA	77.4	82.1	82.6	82.7	83.0	82.5	78.6	78.9
Road	94.8	96.1	96.2	96.2	96.3	97.0	95.5	95.6
Trees	89.5	89.8	90.0	90.2	90.4	92.3	82.8	85.6
Red roof	91.2	93.7	94.6	94.6	86.7	93.4	85.5	90.1
Gray roof	88.3	96.2	96.9	97.5	95.5	97.5	85.5	93.9
Concrete roof	67.3	85.7	81.8	81.8	83.5	79.6	87.1	85.4
Vegetation	46.8	48.3	51.5	51.5	56.5	47.9	50.7	41.7
Bare soil	70.1	84.4	80.1	80.2	85.3	82.9	81.2	80.9

Figure 3. Classification maps for the object-based classification: (a) Multiple features classification map using majority voting; (b) Multiple features classification map using posterior probability; (c) Multiple features classification map using uncertainty; (d) Spectral-spatial classification map using GLCM textures; (e) Joint classification map using GLCM textures.