An adaptive unimodal subclass decomposition (AUSD) learning system for land use and land cover classification using high-resolution remote sensing

Figures & data

Figure 1. Entire study area and the sub-study area, located in Dalian, China. For full color versions of the figures in this paper, please see the online version.

Figure 2. Topological graph of AUSD (a) Calibration phase and (b) Validation phase.

Figure 3. Flow diagram of AUSD.

Figure 4. Grid partition of subclasses (a) two-dimensional and (b) three-dimensional. For full color versions of the figures in this paper, please see the online version.

Table 1. The number, mean, covariance matrix, and label of simulated Gaussian data in numerical experiment.

	Number	Mean		Covariance matrix		Label
Data1	500	1.9372	−2.0096	4.9036	3.0525	1
				3.0525	3.8927
Data2	300	1.9770	−6.0832	0.6681	0.2257	1
				0.2257	0.6837
Data3	700	9.0432	−5.4291	5.3442	−2.1361	2
				−2.1361	5.3760
Data4	300	−2.0320	1.0542	3.9649	−0.8505	2
				−0.8505	1.9562
Data5	100	9.0235	−5.7989	5.1925	−2.0193	2
				−2.0193	5.0684
Data3	800	8.9558	−5.4175	5.2341	−2.1225	2
Data5				−2.0317	5.4569

Figure 5. Distribution of Gaussian data (a) original data and (b) data with supplementation. For full color versions of the figures in this paper, please see the online version.

Table 2. AUSD learning result (number, mean, covariance matrix, and label) of original data.

	Number	Mean		Covariance matrix		Label
Subclass 1	488	2.0230	−1.7519	5.5012	2.9650	1
(Data 1)				2.9650	3.6453
Subclass 2	312	1.9698	−5.9994	0.9036	0.3039	1
(Data 2)				0.3039	0.5887
Subclass 3	700	9.2055	−5.6940	5.0941	−1.8774	2
(Data 3)				−1.8774	4.8736

Figure 6. Results of data density estimation for the first and second dimensions (a) data distribution histogram and the density estimation curve of original data, (b) data distribution histogram and the density estimation curve of data with supplementation, (c) isoprobability contour plots of original data and data with supplementation. For full color versions of the figures in this paper, please see the online version.

Table 3. AUSD Learning result (number, mean, covariance matrix, and label) of original data and supplementary data.

	Number	Mean		Covariance matrix		Label
Subclass 1	488	2.0230	−1.7519	5.5012	2.9650	1
(Data 1)				2.9650	3.6453
Subclass 2	312	1.9698	−5.9994	0.9036	0.3039	1
(Data 2)				0.3039	0.5887
Subclass 3	799	9.1696	−5.6157	5.0701	−1.8468	2
(Data 3 and 5)				−1.8468	4.8715
Subclass 4	301	−2.0034	0.9740	4.1120	−0.9534	2
(Data 4)				−0.9534	2.1559

Figure 7. AUSD classification images of Groups A and B: (a) sub-image 1, (b) sub-image 2, (c) sub-image 3, (d) sub-image 4, (e) sub-image 5, (f) sub-image 6. Each sub-image is in the size of 300 × 300 pixels. For full color versions of the figures in this paper, please see the online version.

Figure 8. Preliminary images and final classification images of (a) proposed AUSD, (b) SVM, (c) MLC, (d) NB, (e) ELM, (f) FAM, (g) KNN, (h) DT. Each sub-image is in the size of 1000 × 1000 pixels. For full color versions of the figures in this paper, please see the online version.

Table 4. Producer accuracies with Group A calibration set.

	AUSD	SVM	MLC	NB	ELM	FAM	KNN	DT
Water	1.0000	0.9347	0.9946	0.8150	0.7388	0.8599	0.8952	0.8639
Forest	0.9633	0.9306	0.8367	0.8939	0.8041	0.9306	0.9306	0.9143
Grass	0.9143	0.8327	0.9918	0.9878	1.0000	0.9592	0.9347	0.8286
Bare land	0.9197	0.9401	0.8204	0.3946	0.9891	0.8626	0.9020	0.8558
Building	0.9020	0.7857	0.5061	0.5857	0.6673	0.7531	0.8041	0.7102
Road	0.6653	0.5592	0.7673	0.4898	0.5673	0.4367	0.5796	0.4367
OA	0.9187	0.8653	0.8230	0.6519	0.8082	0.8182	0.8586	0.7963
Kappa	0.8972	0.8299	0.7782	0.5729	0.7612	0.7696	0.8222	0.7418

AUSD: Adaptive unimodal subclass decomposition; SVM: support vector machine; MLC: maximum likelihood classifier; NB: naive Bayes; ELM: extreme learning machine; FAM: fuzzy ARTMAP; KNN: k-nearest neighbors; DT: decision tree.

Table 5. Producer accuracies with Group A calibration set and Group B calibration set.

	AUSD	SVM	MLC	NB	ELM	FAM	KNN	DT
Water	1.0000	1.0000	0.9973	0.9973	0.9905	1.0000	1.0000	1.0000
Forest	0.9184	0.9265	0.8245	0.8694	0.8041	0.9224	0.9306	0.9020
Grass	0.9755	0.9020	0.9959	0.9959	1.0000	0.9551	0.9388	0.8041
Bare land	0.9646	0.9810	0.7932	0.5265	0.9918	0.8694	0.9510	0.9551
Building	0.9306	0.9143	0.5041	0.6245	0.6531	0.8735	0.9000	0.8898
Road	0.6041	0.5469	0.7184	0.3755	0.4327	0.4735	0.4653	0.4490
OA	0.9321	0.9224	0.8108	0.7328	0.8627	0.8824	0.9080	0.8909
Kappa	0.9139	0.9016	0.7637	0.6665	0.8247	0.8515	0.8833	0.8609

AUSD: Adaptive unimodal subclass decomposition; SVM: support vector machine; MLC: maximum likelihood classifier; NB: naive Bayes; ELM: extreme learning machine; FAM: fuzzy ARTMAP; KNN: k-nearest neighbors; DT: decision tree.

Table 6. The confusion matrix of AUSD of the entire image experiment.

		Predictions
		Water	Forest	Grass	Bare land	Building	Road	PA
Actual conditions	Water	997	1	0	0	2	0	0.9970
	Forest	0	1386	100	1	12	1	0.9240
	Grass	0	77	921	0	2	0	0.9210
	Bare land	0	0	19	408	59	14	0.8160
	Building	11	14	10	34	2311	120	0.9244
	Road	0	0	1	8	71	420	0.8400
	UA	0.9891	0.9378	0.8763	0.9047	0.9406	0.7568
OA		0.9204
Kappa		0.8976

Table 7. The confusion matrix of SVM of the entire image experiment.

		Predictions
		Water	Forest	Grass	Bare land	Building	Road	PA
Actual conditions	Water	775	0	0	0	225	0	0.7750
	Forest	0	1156	6	0	338	0	0.7707
	Grass	0	3	780	0	217	0	0.7800
	Bare land	0	0	0	387	112	1	0.7740
	Building	0	0	0	2	2498	0	0.9992
	Road	0	0	0	0	106	394	0.7880
	UA	1.0000	0.9974	0.9924	0.9949	0.7145	0.9975
OA		0.8557
Kappa		0.8067

Figure 9. Classification images for the entire study area: (a) proposed AUSD, (b) SVM. Each image is in the size of 8000 × 8000 pixels. For full color versions of the figures in this paper, please see the online version.