Table 2 Quantitative results on the ISPRS Potsdam dataset. Best results are shown in bold, and second-best results are underlined (%). Imp: impervious surface; Low: low vegetation.

From: Projection Kernel regularization for diffusion-based multimodal remote sensing segmentation

Type

Model

Imp

Building

Low

Trees

Car

OA

F1

mIoU

CNN-based

MANet

90.61

97.58

87.34

85.45

95.61

89.82

91.05

83.91

ABCNet

89.88

96.52

85.23

84.10

93.72

88.45

89.51

81.35

PSPNet

87.53

96.04

84.61

78.99

95.14

86.70

88.01

79.07

Transformer-based

FTransUNet

90.13

97.03

84.69

82.16

92.68

88.04

88.80

80.32

ASMFNet

86.46

95.78

83.03

63.63

79.78

82.36

81.15

69.19

CMFNet

91.05

97.51

86.14

79.24

95.04

88.22

88.98

80.62

UNetFormer

91.10

97.36

88.63

85.07

95.58

89.94

91.03

83.87

Diffusion-based

SegDiff

50.48

22.83

20.71

5.51

0.21

28.03

27.39

10.71

RNDiff

90.12

96.80

83.22

83.31

95.30

87.61

88.51

79.90

PKDiff

95.12

97.72

85.76

80.17

95.33

90.54

91.02

84.03

  1. Imp: impervious surface; Low: low vegetation; OA: overall accuracy; mIoU: mean IoU.