Self-supervised change detection of heterogeneous images based on difference algorithms

Figures & data

Figure 1. Technology Roadmap.

Figure 2. The process of difference image generation.

Figure 3. PC-SPCNN method fusion.

Figure 4. Sample data.

Figure 5. Heterogeneous image dataset.

Table 1. Data set information.

Data	Time	Spatial resolution	Sensors	Location	Size(pixels)	Event
1	Sept.1995 July.1996	≈30 m 30 m	Landsat-5 Google Earth	Sardinia，Italy	412 × 300	Lake expansion
2	July.2016 Apr.2018	2 m 1 m	GF1 GF2	Lanzhou City，China	412 × 300	construction
3	July.2006 July.2007	0.61 m 1 m	QuickBird2 TerraSAR- X	UK	802 × 500	Flooding

Figure 6. ROC curves for CD in different datasets.

Figure 7. Different DI for experiment 1.

Figure 8. Different DI for experiment 2.

Figure 9. Different DI for experiment 3.

Table 2. Data 1 Change detection accuracy of different difference images.

Experiment	Sample changes	Unchanged sample	FA rate	MD rate	PCC	Kappa
1	($T_1$,$T_2$)	($T_1$+$F_3$,$T_2^{1.5}$)	0.0261	0.0091	0.9648	0.7308
2	($F_3$,$T_2$)	($T_1$+$F_3$,$T_2^{1.5}$)	0.0212	0.0106	0.9682	0.7457
3	($T_1+F_3$,$T_2$)	($T_1$+$F_3$,$T_2^{1.5}$)	0.0085	0.0135	0.9779	0.8020
4	($T_1+F_3$,$T_2$)	($T_1$,$T_2^{1.5}$)	0.0099	0.0127	0.9774	0.8006
5	($T_1+F_3$,$T_2$)	($F_3$,$T_2^{1.5}$)	0.0319	0.0079	0.9602	0.7088
6	($T_1+F_3$,$T_2$)	($T_1+F_3$,$T_2^2$)	0.0083	0.0142	0.9776	0.7972
7	($T_1+F_3$,$T_2$)	($T_1$,$T_2^2$)	0.0099	0.0128	0.9773	0.7992
8	($T_1+F_3$,$T_2$)	($F_3$,$T_2^2$)	0.0220	0.0093	0.9688	0.7541

Table 3. Data 2 Change detection accuracy of different difference images.

Experiment	Sample changes	Unchanged sample	FA rate	MD rate	PCC	Kappa
1	($T_1$,$T_2$)	($T_1$+$F_3$,$T_2^{1.5}$)	0.0768	0.0895	0.8337	0.6411
2	($F_3$,$T_2$)	($T_1$+$F_3$,$T_2^{1.5}$)	0.0499	0.0785	0.8716	0.7204
3	($T_1+F_3$,$T_2$)	($T_1$+$F_3$,$T_2^{1.5}$)	0.0671	0.0458	0.8871	0.7608
4	($T_1+F_3$,$T_2$)	($T_1$,$T_2^{1.5}$)	0.0187	0.1650	0.8163	0.5717
5	($T_1+F_3$,$T_2$)	($F_3$,$T_2^{1.5}$)	0.1598	0.0207	0.8195	0.6408
6	($T_1+F_3$,$T_2$)	($T_1+F_3$,$T_2^2$)	0.1222	0.0607	0.8170	0.6208
7	($T_1+F_3$,$T_2$)	($T_1$,$T_2^2$)	0.0323	0.2025	0.7653	0.4447
8	($T_1+F_3$,$T_2$)	($F_3$,$T_2^2$)	0.1039	0.0747	0.8214	0.6234

Table 4. Data 3 different difference image change detection accuracy.

Experiment	Sample changes	Unchanged sample	FA rate	MD rate	PCC	Kappa
1	($T_1$,$T_2$)	($T_1$+$F_3$,$T_2^{1.5}$)	0.0077	0.0698	0.9225	0.7913
2	($F_3$,$T_2$)	($T_1$+$F_3$,$T_2^{1.5}$)	0.0135	0.0663	0.9202	0.7875
3	($T_1+F_3$,$T_2$)	($T_1$+$F_3$,$T_2^{1.5}$)	0.0125	0.0606	0.9269	0.8063
4	($T_1+F_3$,$T_2$)	($T_1$,$T_2^{1.5}$)	0.0291	0.0542	0.9167	0.7853
5	($T_1+F_3$,$T_2$)	($F_3$,$T_2^{1.5}$)	0.0168	0.0610	0.9222	0.7949
6	($T_1+F_3$,$T_2$)	($T_1+F_3$,$T_2^2$)	0.0061	0.0733	0.9206	0.7848
7	($T_1+F_3$,$T_2$)	($T_1$,$T_2^2$)	0.0057	0.0746	0.9196	0.7818
8	($T_1+F_3$,$T_2$)	($F_3$,$T_2^2$)	0.0060	0.0746	0.9194	0.7811

Figure 10. Error bars for different DIs.

Figure 11. Detection results of changes in different fusion methods.

Table 5. Change detection accuracy of different fusion methods for Data 1.

Method	FA rate	MD rate	PCC	Kappa	F1
HCS	0.1322	0.0067	0.8611	0.3848	0.4419
PCA	0.1006	0.0069	0.8925	0.4570	0.5050
PC-SPCNN	0.0085	0.0135	0.9779	0.8020	0.8137

Table 6. Data 2 different fusion methods change detection accuracy.

Method	FA rate	MD rate	PCC	Kappa	F1
HCS	0.0333	0.1087	0.8580	0.6824	0.7869
PCA	0.0702	0.0543	0.8756	0.7356	0.8357
PC-SPCNN	0.0671	0.0458	0.8871	0.7608	0.8520

Table 7. Data 3 different fusion methods change detection accuracy.

Method	FA rate	MD rate	PCC	Kappa	F1
HCS	0.0153	0.0606	0.9241	0.7997	0.8500
PCA	0.0050	0.0807	0.9144	0.7655	0.8198
PC-SPCNN	0.0125	0.0606	0.9269	0.8063	0.8546

Figure 12. Change detection results of different classifier methods for three heterogeneous image datasets.

Figure 13. Change detection results of different method changes of three groups of heterogeneous images.

Table 8. Change detection accuracy of different classifier methods for Data 1.

Method	FA rate	MD rate	PCC	Kappa	F1
NR-CFI-Bi-FCM-KNN	0.0098	0.0130	0.9771	0.7975	0.8097
NR-CFI-Bi-FCM -RF	0.0085	0.0162	0.9752	0.7728	0.7859
NR-CFI-Bi-FCM -FCM-SBN	0.0525	0.0064	0.9411	0.6229	0.6525
NR-CFI-Bi-FCM -FITENSEMBLE	0.0119	0.0125	0.9757	0.7886	0.8020
NR-CFI-Bi-FCM -ELM	0.0115	0.0129	0.9756	0.7871	0.8000
NR-CFI-Bi-FCM -SVM	0.0085	0.0135	0.9779	0.8020	0.8137

Table 9. Change detection accuracy of different methods for Data 1.

Method	FA rate	MD rate	PCC	Kappa	F1
NR_ELM	0.0347	0.0128	0.9525	0.6480	0.6730
SCASC	0.0074	0.0236	0.9690	0.6952	0.7111
SDA-HCD	0.0072	0.0230	0.9698	0.7035	0.7191
IRG-McS	0.0069	0.0218	0.9714	0.7212	0.7361
SRF	0.0172	0.0106	0.9722	0.7711	0.7860
GMCD	0.0470	0.0187	0.9343	0.5325	0.5668
SRGCAE	0.1029	0.0079	0.8892	0.4435	0.4928
CWNN	0.0338	0.0127	0.9536	0.6541	0.6786
NR-CFI-Bi-FCM -SVM	0.0085	0.0135	0.9779	0.8020	0.8137

Table 10. Change detection accuracy of different classifier methods for Data 2.

Method	FA rate	MD rate	PCC	Kappa	F1
NR-CFI-Bi-FCM -KNN	0.0640	0.0520	0.8840	0.7531	0.8462
NR-CFI-Bi-FCM -RF	0.0617	0.0711	0.8672	0.7140	0.8187
NR-CFI-Bi-FCM -FCM-SBN	0.1851	0.1031	0.7118	0.4092	0.6501
NR-CFI-BI-FCM-FITENSEMBLE	0.0986	0.0526	0.8488	0.6841	0.8081
NR-CFI-Bi-FCM -ELM	0.0569	0.0643	0.8788	0.7392	0.8349
NR-CFI-Bi-FCM -SVM	0.0671	0.0458	0.8871	0.7608	0.8520

Table 11. Change detection accuracy of different methods for Data 2.

Method	FA rate	MD rate	PCC	Kappa	F1
NR_ELM	0.0102	0.2968	0.6930	0.2182	0.3256
SCASC	0.0146	0.3146	0.6708	0.1542	0.2549
SDA-HCD	0.0181	0.2110	0.7709	0.4505	0.5827
IRG-McS	0.0504	0.2926	0.6570	0.1512	0.3134
SRF	0.0159	0.2944	0.6896	0.2137	0.3300
GMCD	0.0206	0.2471	0.7323	0.3441	0.4805
SRGCAE	0.0553	0.2931	0.6516	0.1402	0.3087
CWNN	0.1564	0.1987	0.6448	0.2207	0.4922
NR-CFI-Bi-FCM -SVM	0.0671	0.0458	0.8871	0.7608	0.8520

Table 12. Change detection accuracy of different classifier methods for Data 3.

Method	FA rate	MD rate	PCC	Kappa	F1
NR-CFI-Bi-FCM -KNN	0.0101	0.0636	0.9263	0.8037	0.8520
NR-CFI-Bi-FCM -RF	0.0075	0.0674	0.9251	0.7987	0.8470
NR-CFI-Bi-FCM -FCM-SBN	0.0035	0.0925	0.9040	0.6984	0.7920
NR-CFI-Bi-FCM -FITENSEMBLE	0.0099	0.0638	0.9264	0.8037	0.8519
NR-CFI-Bi-FCM -ELM	0.0041	0.0885	0.9075	0.7438	0.8020
NR-CFI-Bi-FCM -SVM	0.0125	0.0606	0.9269	0.8063	0.8546

Table 13. Change detection accuracy of different methods for Data 3.

Method	FA rate	MD rate	PCC	Kappa	F1
NR_ELM	0.2755	0.0296	0.6949	0.5775	0.5022
SCASC	0.0622	0.0892	0.8485	0.6087	0.7109
SDA-HCD	0.0899	0.0495	0.8606	0.6660	0.7643
IRG-McS	0.1021	0.2067	0.6912	0.1165	0.2933
SRF	0.0189	0.1135	0.8676	0.6287	0.7098
GMCD	0.0700	0.2090	0.7210	0.1716	0.3228
SRGCAE	0.1355	0.1347	0.7299	0.3239	0.5104
CWNN	0.0701	0.0923	0.8376	0.5827	0.6928
NR-CFI-Bi-FCM -SVM	0.0125	0.0606	0.9269	0.8063	0.8546