Analysis of Machine Learning Techniques for Sentinel-2A Satellite Images

Table 9

Group 2 area and percentages of LULC for machine learning in Sentinel-2A satellite (2016).

No.	Name	MLC		Maximum Entropy		ANN		MDC		SAM		Parallelepiped
		Area, km^2	%	Area, km^2	%	Area, km^2	%	Area, km^2	%	Area, km^2	%	Area, km^2	%
1	High land	41708400	2.27	33302400	4.53	193600	0.01	22922800	1.57	25655800	1.38	15333800	0.82
2	Land area	750954500	40.86	1010829000	137.62	1487337700	79.13	397663200	27.25	727984700	39.26	299130200	16.05
3	Agricultural area	270312300	14.71	2674700	0.36	14122000	0.75	366576900	25.12	138350600	7.46	406113800	21.80
4	Built-up area	407204500	22.15	727986600	99.12	376240400	20.02	488692800	33.49	283015300	15.26	418275700	22.45
5	Mountains	363201000	19.76	3825100	0.52	1795100	0.10	441238100	30.24	409992100	22.11	730496000	39.21
6	Bare land	46308000	2.52	101070800	13.76	0	0.00	162594900	11.14	294690100	15.89	9256600	0.50
Total		1837980300	100.00	734486400	100.00	1879688800	100.00	1459102700	100.00	1854032800	100.00	1863272300	100.00