Urban Land Use and Land Cover Classification Using Remotely Sensed SAR Data through Deep Belief Networks

Table 2

Comparison of different classification methods.

	SVM		NN		SEM		DBN
Water	0.8521	0.9169	0.7847	0.9560	0.9668	0.9733	0.8697	0.9052
Golf	0.8588	0.5364	0.8922	0.6048	0.9245	0.8346	0.8118	0.7727
Pasture	0.5776	0.8949	0.6095	0.9198	0.8502	0.8499	0.8139	0.8987
Cons.	0.7639	0.6879	0.6383	0.6657	0.7239	0.7750	0.7265	0.7899
LD	0.6847	0.8509	0.5771	0.8175	0.3160	0.7697	0.6703	0.8884
Crop1	0.9020	0.7548	0.7991	0.8971	0.9617	0.6497	0.8800	0.8804
Crop2	0.7965	0.8882	0.8615	0.7671	0.8306	0.8649	0.8986	0.8469
Forest	0.8703	0.9098	0.8908	0.9408	0.9542	0.7076	0.9095	0.9489
HD	0.7203	0.5830	0.7195	0.4570	0.6264	0.4898	0.7824	0.5867
Ind.	0.7593	0.7556	0.6817	0.7394	0.4135	0.5811	0.7936	0.7632
OA	0.7679		0.7437		0.7243		0.8174
Kappa	0.7398		0.7119		0.6906		0.7945