Building Extraction from Remote Sensing Image Based on Multi-Module

Xingtao Ming; Dehong Yang

doi:10.3788/LOP231148

Journals >Laser & Optoelectronics Progress >Volume 61 >Issue 4 >Page 0428004 > Article

Laser & Optoelectronics Progress
Vol. 61, Issue 4, 0428004 (2024)

Building Extraction from Remote Sensing Image Based on Multi-Module

Xingtao Ming and Dehong Yang^*

Author Affiliations

Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, Yunnan, China

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DOI: 10.3788/LOP231148 Cite this Article Set citation alerts

Xingtao Ming, Dehong Yang. Building Extraction from Remote Sensing Image Based on Multi-Module[J]. Laser & Optoelectronics Progress, 2024, 61(4): 0428004 Copy Citation Text

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Fig. 1. MM-Unet structure

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Fig. 2. Plain unit and residual unit. (a) Plain unit; (b) residual unit

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Fig. 3. Module improvements. (a) Atrous spatial pyramid pooling module; (b) multi-scale feature enhancement module

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Fig. 4. UAC module

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Fig. 5. Dual attention module

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Fig. 6. Comparison results of Massachusetts Building dataset. (a) Images; (b) labels; (c) FCN; (d) SegNet; (e) Unet; (f) Unet++; (g) MM-Unet

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Fig. 7. Comparison results of WHU Building dataset. (a) Images; (b) labels; (c) FCN; (d) SegNet; (e) Unet; (f) Unet++; (g) MM-Unet

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Fig. 8. Comparison results of ISPRS Vaihingen dataset. (a) Images; (b) labels; (c) FCN; (d) SegNet; (e) Unet; (f) Unet++; (g) MM-Unet

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Dataset	Resolution /m	Train number	Validation number	Test number
Massachusetts Building	1.00	7416	288	90
WHU Building	0.30	4736	1036	2416
ISPRS Vaihingen	0.09	2752	736	306

Table 1. Experimental dataset

Model	R_OA	R_precision	R_recall	s_F1	R_IoU
FCN	92.78	80.53	80.70	80.61	67.52
SegNet	93.00	81.83	81.68	81.75	69.87
Unet	93.76	83.47	81.90	82.68	71.21
Unet++	94.14	85.52	82.47	83.97	72.37
MM-Unet	94.46	86.39	83.12	84.72	73.42

Table 2. Accuracy comparison of Massachusetts Building dataset

Model	R_OA	R_precision	R_recall	s_F1	R_IoU
FCN	98.64	93.65	94.20	93.92	88.54
SegNet	98.62	93.90	94.00	93.95	88.59
Unet	98.68	93.97	94.23	94.10	88.86
Unet++	98.79	94.90	93.91	94.40	89.39
MM-Unet	98.85	95.35	94.25	94.80	90.11

Table 3. Accuracy comparison of WHU Building dataset

Model	R_OA	R_precision	R_recall	s_F1	R_IoU
FCN	95.80	92.56	90.33	91.43	84.57
SegNet	95.46	91.98	90.09	91.02	83.53
Unet	95.57	91.87	90.26	91.05	83.66
Unet++	95.63	91.18	91.64	91.40	84.17
MM-Unet	95.95	92.95	91.68	92.31	85.21

Table 4. Accuracy comparison of ISPRS Vaihingen dataset

Model	R_OA	R_precision	R_recall	s_F1	R_IoU
Unet	93.76	83.47	81.90	82.68	71.21
Unet+DAM	94.08	85.46	82.05	83.72	72.16
Unet+DAM+MFCM	94.23	86.15	82.97	84.53	73.15
MM-Unet	94.46	86.39	83.12	84.72	73.42

Table 5. Ablation experiments of Massachusetts Building dataset

Model	R_OA	R_precision	R_recall	s_F1	R_IoU
Unet	98.68	93.97	94.23	94.10	88.86
Unet+DAM	98.73	94.55	94.09	94.32	89.25
Unet+DAM+MFCM	98.80	95.09	94.05	94.57	89.70
MM-Unet	98.85	95.35	94.25	94.80	90.11

Table 6. Ablation experiments of WHU Building dataset

Model	R_OA	R_precision	R_recall	s_F1	R_IoU
Unet	95.57	91.87	90.26	91.05	83.66
Unet+DAM	95.84	92.06	91.25	91.65	84.59
Unet+DAM+MFCM	95.90	92.74	90.93	91.82	84.88
MM-Unet	95.95	92.95	91.68	92.31	85.21

Table 7. Ablation experiments of ISPRS Vaihingen dataset

Xingtao Ming, Dehong Yang. Building Extraction from Remote Sensing Image Based on Multi-Module[J]. Laser & Optoelectronics Progress, 2024, 61(4): 0428004

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