Remote Sensing Vegetation Classification Method Based on Vegetation Index and Convolution Neural Network

Mingzhu Xu; Hao Xu; Peng Kong; Yanlan Wu

doi:10.3788/LOP202259.2428005

Laser & Optoelectronics Progress, Volume. 59, Issue 24, 2428005(2022)

Remote Sensing Vegetation Classification Method Based on Vegetation Index and Convolution Neural Network

Mingzhu Xu¹, Hao Xu², Peng Kong², and Yanlan Wu^1,3,4、*

Author Affiliations

¹School of Resources and Environmental Engineering, Anhui University, Hefei 230601, Anhui, China

²Institute of Spacecraft System Engineering, Beijing 100094, China

³Information Materials and Intelligent Sensing Laboratory of Anhui Province, Hefei 230601, Anhui, China

⁴Anhui Engineering Research Center for Geographical Information Intelligent Technology, Hefei 230601, Anhui, China

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Abstract Get PDF(in Chinese)

Figures & Tables(16)

Fig. 1. Flow chart of network structure

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Fig. 2. Dense blocks

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Fig. 3. Atrous spatial pyramid pooling (ASPP) block

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Fig. 4. Multiresolution feature fusion modes. (a) Mode 1); (b) mode 2); (c) mode 3)

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Fig. 5. Example of vegetation samples from remote sensing images

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Fig. 6. City classification results before and after HRDN joined NDVI

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Fig. 7. Rural classification results before and after HRDN joined NDVI

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Fig. 8. Result map of vegetation classification in urban areas by HRDN, Deeplab-V3+, BiseNet, and DCCN

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Fig. 9. Result map of vegetation classification in rural areas by HRDN, Deeplab-V3+, BiseNet, and DCCN

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Fig. 10. Result map of vegetation classification and extraction by proposed method

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Table 1. Image parameters

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Table 1. Image parameters

Use	Sample	Model testing	Universal validation 1	Universal validation 2
Sensor	GF2-PMS2	GF2-PMS2	GF6-PMS	GF2-PMS2
Region	Hefei	Hefei	Hefei	Beijing
Spatial resolution /m	1	1	2	1
Image acquisition date	2015-08-03	2016-08-27 2015-08-03	2018-10-04	2016-08-27
Midline coordinates	E117.3，N31.7	E116.8，N40.4 E117.3，N31.7	E117.4，N32.1	E116.8，N40.4

Table 2. Comparison of classification accuracy of HRDN before and after adding NDVI. (a), (b) are classification accuracies of fused NDVI model and unfused NDVI model, respectively

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Table 2. Comparison of classification accuracy of HRDN before and after adding NDVI. (a), (b) are classification accuracies of fused NDVI model and unfused NDVI model, respectively

Region	Image	Cultivated land		Grassland		Forest		Aquatic vegetation		Mean F1 score	Mean IOU	OA
Region	Image	F1	IOU	F1	IOU	F1	IOU	F1	IOU	Mean F1 score	Mean IOU	OA
City	Image 1	14.53	9.01	18.22	10.02	63.33	46.33	/	/	32.03	21.79	87.02
	Image 2	15.67	8.50	13.59	7.29	67.49	50.93	67.28	50.70	41.01	29.36	89.79
	Image 3	/	/	12.44	6.63	90.27	82.27	/	/	51.36	44.45	94.88
	Image 4	/	/	14.94	8.07	83.56	71.77	/	/	49.25	39.92	93.90
	All images	15.10	8.76	14.80	8.00	76.16	62.83	67.28	50.70	43.41	33.88	91.40
Rural	Image 5	89.05	80.26	87.80	78.26	79.20	65.56	78.07	64.02	83.53	72.03	78.49
	Image 6	86.19	75.74	81.89	69.33	77.21	62.87	77.32	63.03	80.65	67.74	77.90
	Image 7	86.71	76.54	84.44	73.07	80.99	68.05	80.68	67.61	83.21	71.32	82.60
	Image 8	72.08	56.35	70.97	55.00	61.11	44.00	60.68	43.11	66.21	49.62	61.16
	All images	83.51	72.22	81.28	68.92	74.63	60.12	74.19	59.44	78.40	65.18	75.04
（a）
Region	Image	Cultivated land		Grassland		Forest		Aquatic vegetation		Mean F1 score	Mean IOU	OA
Region	Image	F1	IOU	F1	IOU	F1	IOU	F1	IOU	Mean F1 score	Mean IOU	OA
City	Image 1	53.23	36.27	57.49	40.34	82.45	70.14	/	/	64..39	48.92	93.48
	Image 2	56.02	38.91	62.31	45.25	84.5	96.56	84.48	73.12	71.83	63.46	94.79
	Image 3	/	/	77.61	63.42	98.34	96.73	/	/	87.98	80.08	99.25
	Image 4	/	/	75.32	60.41	97.87	95.83	/	/	86.60	78.12	99.38
	All images	54.63	37.59	68.18	52.36	90.79	89.82	84.48	73.12	82.14	67.65	96.73
Rural	Image 5	90.60	82.82	88.58	79.50	85.49	74.66	84.82	73.64	87.37	77.66	87.20
	Image 6	94.33	89.27	92.50	86.05	91.71	84.69	91.67	84.62	92.55	86.16	93.08
	Image 7	94.34	89.29	93.14	87.16	92.52	86.08	92.43	85.93	93.11	87.11	94.41
	Image 8	88.29	79.04	86.44	76.12	89.03	80.23	88.59	79.52	88.09	78.73	90.70
	All images	91.89	85.11	90.17	82.21	89.69	81.42	89.38	80.93	90.28	82.41	91.35
（b）

Table 3. Confusion matrices of images in urban areas. (a)-(d) are confusion matrices of four images in urban area respectively

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Table 3. Confusion matrices of images in urban areas. (a)-(d) are confusion matrices of four images in urban area respectively

Image 1	Cultivated land	Grassland	Forest	Aquatic vegetation	UA /%
Cultivated land	47.35	2.22	1.21	/	60.95
Grassland	8.52	56.64	4.45	/	60.33
Forest	33.24	31.31	85.02	/	90.84
Aquatic vegetation	0	0	0.01	/	0
PA /%	47.35	56.64	85.02	/
Kappa：84.50%
（a）
Image 2	Cultivated land	Grassland	Forest	Aquatic vegetation	UA /%
Cultivated land	58.62	6.73	0.14	0	53.63
Grassland	6.47	60.63	2.87	27.66	66.91
Forest	31.86	28.72	93.15	1.1	84.63
Aquatic vegetation	0	0.02	0.02	67.26	58.37
PA /%	58.62	60.63	93.15	67.26
Kappa：85.85%
（b）
Image 3	Cultivated land	Grassland	Forest	Aquatic vegetation	UA /%
Cultivated land	/	0	0	/	0
Grassland	/	81.38	0.42	/	79.97
Forest	/	18.43	99.56	/	98.68
Aquatic vegetation	/	0.12	0.20	/	0
PA /%	/	81.38	99.35	/
Kappa：98.37%
（c）
Image 4	Cultivated land	Grassland	Forest	Aquatic vegetation	UA /%
Cultivated land	/	0.37	0.14	/	0
Grassland	/	85.08	1.09	/	70.40
Forest	/	14.16	97.89	/	99.47
Aquatic vegetation	/	0.05	0.01	/	0
PA /%	/	85.08	97.89	/
Kappa：98.29%
（d）

Table 4. Confusion matrices of images in rural areas. (a)-(d) are confusion matrices of four images in rural areas respectively

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Table 4. Confusion matrices of images in rural areas. (a)-(d) are confusion matrices of four images in rural areas respectively

Image 5		Cultivated land		Grassland		Forest		Aquatic vegetation		UA /%
Cultivated land		87.51		2.00		8.14		27.34		93.92
Grassland		2.03		25.05		2.18		0.71		11.80
Forest		7.85		60.66		80.87		22.70		77.96
Aquatic vegetation		0.27		4.65		0.54		42.39		66.73
PA /%		87.51		25.05		80.87		42.39
Kappa：80.61%
（a）
Image 6		Cultivated land		Grassland		Forest		Aquatic vegetation		UA /%
Cultivated land		94.71		16.74		5.90		1.09		93.96
Grassland		0.67		37.79		0.53		0.23		53.04
Forest		4.47		36.79		92.50		7.27		88.87
Aquatic vegetation		0.06		0.24		0.42		89.87		92.32
PA /%		94.71		37.79		92.5		89.87
Kappa：90.05%
（b）
Image 7	Cultivated land		Grassland		Forest		Aquatic vegetation		UA /%
Cultivated land	93.51		7.65		5.32		0.96		95.19
Grassland	0.20		70.67		1.55		0.08		52.67
Forest	5.41		18.45		91.74		9.61		91.76
Aquatic vegetation	0.24		0.09		0.16		86.54		85.99
PA /%	93.51		70.67		91.74		86.54
Kappa：91.78%
（c）
Image 8	Cultivated land		Grassland		Forest		Aquatic vegetation		UA /%
Cultivated land	88.8		40.70		6.01		13.83		87.79
Grassland	0.14		26.37		0.24		0.26		70.91
Forest	10.19		29.37		93.22		15.85		89.07
Aquatic vegetation	0.68		0.71		0.26		69.53		83.87
PA /%	88.8		26.37		93.22		69.53
Kappa：86.23%
（d）

Table 5. Classification results of three methods. (a), (b), (c) are classification and extraction results of Deeplab-V3+, BiseNet, and DCCN models, respectively

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Table 5. Classification results of three methods. (a), (b), (c) are classification and extraction results of Deeplab-V3+, BiseNet, and DCCN models, respectively

Region	Image	Cultivated land		Grassland		Forest		Aquatic vegetation		Mean F1 score	Mean IOU	OA
Region	Image	F1	IOU	F1	IOU	F1	IOU	F1	IOU	Mean F1 score	Mean IOU	OA
City	Image 1	39.60	24.69	36.60	22.40	79.31	65.72	/	/	51.84	37.60	92.48
	Image 2	55.19	38.11	38.50	23.84	80.10	66.80	80.06	66.74	63.46	48.87	93.32
	Image 3	/	/	30.68	18.12	92.60	86.21	/	/	61.64	52.17	95.77
	Image 4	/	/	35.37	21.48	91.17	83.77	/	/	63.27	52.63	96.23
	All images	47.40	31.40	35.29	21.46	85.99	75.80	80.06	66.74	60.05	46.21	94.45
Rural	Image 5	90.00	81.81	89.19	80.49	85.50	74.67	84.72	73.49	87.35	78.99	85.96
	Image 6	91.47	84.29	90.07	81.93	88.35	79.13	88.40	79.21	89.57	81.14	88.57
	Image 7	87.98	78.54	86.66	76.46	85.10	74.07	85.00	73.92	86.19	75.75	87.53
	Image 8	77.59	63.39	76.95	62.46	79.12	65.46	78.57	64.70	70.06	64.00	78.92
	All images	86.76	77.01	85.71	75.34	84.52	73.33	84.17	72.83	83.29	74.97	85.25
（a）
Region	Image	Cultivated land		Grassland		Forest		Aquatic vegetation		Mean F1 score	Mean IOU	OA
Region	Image	F1	IOU	F1	IOU	F1	IOU	F1	IOU	Mean F1 score	Mean IOU	OA
City	Image 1	0	0	35.99	21.94	67.16	50.56	/	/	51.57	36.25	88.42
	Image 2	0	0	35.52	21.59	68.77	52.41	68.72	52.35	57.67	42.12	90.36
	Image 3	/	/	67.95	51.45	92.84	86.64	/	/	80.40	69.05	95.64
	Image 4	/	/	51.00	34.23	89.86	81.59	/	/	70.43	57.91	95.91
	All images	0	0	47.62	32.30	79.66	67.80	68.72	52.35	65.337	50.82	92.58
Rural	Image 5	90.43	82.53	89.68	81.30	85.86	75.22	84.84	73.66	87.70	78.17	86.48
	Image 6	91.13	83.71	89.70	81.33	86.79	76.67	86.65	76.44	88.57	79.54	87.43
	Image 7	89.73	81.37	88.01	78.59	86.30	75.91	85.89	75.28	87.48	77.79	88.13
	Image 8	80.86	67.86	80.05	66.73	81.63	68.97	80.95	67.99	80.87	67.89	82.27
	All images	88.04	78.87	86.86	76.99	85.15	74.19	84.58	73.34	86.16	75.85	86.08
（b）
Region	Image	Cultivated land		Grassland		Forest		Aquatic vegetation		Mean F1 score	Mean IOU	OA
Region	Image	F1	IOU	F1	IOU	F1	IOU	F1	IOU	Mean F1 score	Mean IOU	OA
City	Image 1	16.53	9.01	18.22	10.02	63.32	46.33	/	/	32.69	21.79	87.02
	Image 2	15.67	8.50	13.59	7.29	67.49	50.93	67.28	50.70	41.01	29.36	89.79
	Image 3	/	/	12.44	6.63	90.27	82.27	/	/	51.36	44.45	94.88
	Image 4	/	/	14.94	8.07	83.56	71.77	/	/	49.25	39.92	93.90
	All images	16.51	8.76	14.80	8.00	76.16	62.83	67.28	50.70	43.58	33.88	91.40
Rural	Image 5	89.05	80.26	87.80	78.26	79.20	65.56	78.07	64.02	83.53	72.03	78.49
	Image 6	86.19	75.74	81.89	69.33	77.21	62.87	77.32	63.03	80.65	67.74	77.90
	Image 7	86.71	76.54	84.44	70.07	80.99	68.05	80.68	67.61	83.21	70.57	82.60
	Image 8	72.08	56.35	70.97	55.00	61.11	44.00	60.25	43.11	66.10	49.62	61.16
	All images	83.51	72.22	81.28	68.17	74.63	60.12	74.08	59.44	78.37	70.11	75.04
（c）

Table 6. Extraction accuracy of vegetation classification by proposed method

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Table 6. Extraction accuracy of vegetation classification by proposed method

Region	Cultivated land		Grassland		Forest		Aquatic vegetation		Mean F1 score	Mean IOU	OA	Kappa
Region	F1	IOU	F1	IOU	F1	IOU	F1	IOU	Mean F1 score	Mean IOU	OA	Kappa
GF-6 Hefei image 1	89.04	80.24	85.99	75.42	84.60	73.30	84.17	72.66	86.53	75.41	88.42	82.97
GF-6 Hefei image 2	59.79	42.64	62.56	45.51	84.22	72.74	84.17	72.67	72.69	58.39	93.55	84.14
GF-2 Beijing image 1	89.08	80.31	86.63	76.41	88.89	80.00	88.64	79.60	88.31	79.08	88.95	82.78
GF-2 Beijing image 2	71.40	55.52	64.89	48.03	85.82	75.17	85.71	75.00	76.95	63.43	92.41	83.32

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Mingzhu Xu, Hao Xu, Peng Kong, Yanlan Wu. Remote Sensing Vegetation Classification Method Based on Vegetation Index and Convolution Neural Network[J]. Laser & Optoelectronics Progress, 2022, 59(24): 2428005

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Paper Information

Category: Remote Sensing and Sensors

Received: Sep. 14, 2021

Accepted: Nov. 3, 2021

Published Online: Nov. 28, 2022

The Author Email: Wu Yanlan (wuyanlan@ahu.edu.cn)

DOI:10.3788/LOP202259.2428005

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology