Domestic Multispectral Image Classification Based on Multilayer Perception Convolutional Neural Network

Ruifei Zhu; Jingyu Ma; Zhuqiang Li; Dong Wang; Yuan An; Xing Zhong; Fang Gao; Xiangyu Meng

doi:10.3788/AOS202040.1528003

Acta Optica Sinica, Volume. 40, Issue 15, 1528003(2020)

Domestic Multispectral Image Classification Based on Multilayer Perception Convolutional Neural Network

Ruifei Zhu^1,2, Jingyu Ma¹, Zhuqiang Li^1、*, Dong Wang^1,2, Yuan An^1,2, Xing Zhong^1,2, Fang Gao¹, and Xiangyu Meng³

¹Jilin Key Laboratory of Satellite Remote Sensing Application Technology, Chang Guang Satellite Technology Co., Ltd., Changchun, Jilin 130012, China

²Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin 130033, China

³Jilin Institute of Land Survey & Planning, Changchun, Jilin 130061, China;

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Figures & Tables(14)

Fig. 1. Flow chart of MPCNet classification algorithm

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Fig. 2. Multispectral remote sensing image classification model based on MPCNet

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Fig. 3. Introduction of experimental data. (a) True color image of Jilin-1GP01; (b) true color image of Landsat8; (c) true color image of Sentinel-2A; (d) true color image of HJ-1A; (e) high resolution image of Google Earth; (f) land cover product of FROM-GLC-2017

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Fig. 4. Reflectivity curves of samples in Nashik research area of Jilin-1GP01. (a) Uncultivated land; (b) cultivated land; (c) building; (d) grassland; (e) bare land; (f) road; (g) forest land; (h) water; (i) cloud; (j) shadow

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Fig. 5. Classification results using MPCNet algorithm. (a) Jilin-1GP01 image; (b) Landsat8 image; (c) Sentinel-2A image; (d) HJ-1A image

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Fig. 6. Classification detail results of surface features at local Nashik area. (a) Pseudo-color composite image; (b) local enlargement image; (c) classification result by Jilin-1GP01; (d) classification result by Sentinel-2A; (e) remote sensing image superimposed vector data

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Fig. 7. Classification results of different algorithms on Jilin-1GP01 images in Nashik. (a) Local image; (b) SVM; (c) LGBM-GBDT; (d) shallow CNN; (e) MPCNet

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Fig. 8. Classification results of different algorithms on Jilin-1GP02 images in Xintai city. (a) Local image; (b) SVM; (c) LGBM-GBDT; (d) shallow CNN; (e) MPCNet

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Table 1. Band selection, spatial resolution, and shooting time of multispectral satellite

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Table 1. Band selection, spatial resolution, and shooting time of multispectral satellite

Satellite type	Available/selective band number	Wavelength range /nm	Spatial resolution /m	Shooting time
Jilin-1GP01	26/10	400-13500	5	2019-01-22
Landsat8	11/11	430-12510	30	2019-01-13
Sentinel-2A	13/13	443-2190	10	2019-01-20
HJ-1A	4/4	430-900	30	2019-01-14

Table 2. Band selection, spatial resolution, and shooting time of Jilin-1GP02
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Table 2. Band selection, spatial resolution, and shooting time of Jilin-1GP02
Satellite type Sensor Scene ID Available/selective band number Wavelength range /nm Spatial resolution /m Shooting time
Jilin-1GP02 PMS1PMS1PMS2PMS2 0001000200010002 26/10 400-13500 5 2019-03-15

Table 3. Classification accuracy evaluation index of different satellite data based on MPCNet algorithm

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Table 3. Classification accuracy evaluation index of different satellite data based on MPCNet algorithm

Area type	Jilin-1GP01		Landsat8		Sentinel-2A			HJ-1A
Area type	P/R	F₁	P/R	F₁	P/R		F₁	P/R	F₁
Uncultivated land	1.00/0.93					0.96				0.91/0.83	0.87	0.96/0.89	0.92	0.76/0.74	0.75
Cultivated land	0.94/0.89					0.91				0.81/0.88	0.84	0.83/0.98	0.90	0.52/0.70	0.60
Building	0.96/0.99					0.97				0.94/0.79	0.86	0.97/0.94	0.96	0.85/0.61	0.71
Grassland	1.00/0.88					0.94				1.00/0.73	0.85	1.00/0.82	0.90	0.92/0.69	0.79
Bare land	0.96/1.00					0.98				0.96/0.93	0.94	0.94/0.99	0.96	0.75/0.86	0.80
Road	0.95/0.80					0.87				0.43/0.80	0.56	0.85/0.83	0.84	0.14/0.23	0.18
Forest land	0.71/1.00					0.83				0.62/1.00	0.76	0.69/1.00	0.82	0.27/0.68	0.38
Water	1.00/1.00					1.00				1.00/0.97	0.98	1.00/0.93	0.96	0.91/0.60	0.72
K	0.948									0.828		0.920		0.595
P_OA	0.958									0.859		0.935		0.667

Table 4. Classification accuracy evaluation index of different algorithms on Jilin-1GP01 images

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Table 4. Classification accuracy evaluation index of different algorithms on Jilin-1GP01 images

Area type	SVM		LGBM-GBDT		Shallow CNN			MPCNet
Area type	P/R	F₁	P/R	F₁	P/R		F₁	P/R	F₁
Uncultivated land	0.91/0.70					0.78				0.90/0.83	0.86	0.91/0.85	0.88	1.00/0.93	0.96
Cultivated land	0.90/0.89					0.89				0.91/0.90	0.91	0.98/0.90	0.94	0.94/0.89	0.91
Building	0.86/1.00					0.92				0.93/0.99	0.96	0.91/1.00	0.95	0.96/0.99	0.97
Grassland	0.99/0.79					0.88				1.00/0.69	0.82	1.00/0.89	0.94	1.00/0.88	0.94
Bare land	0.91/0.99					0.95				0.90/0.99	0.94	0.92/1.00	0.96	0.96/1.00	0.98
Road	0.64/0.28					0.39				0.88/0.62	0.73	0.76/0.40	0.53	0.95/0.80	0.87
Forest land	0.60/0.85					0.71				0.65/1.00	0.79	0.67/1.00	0.80	0.71/1.00	0.83
Water	1.00/0.96					0.98				1.00/0.97	0.99	1.00/0.96	0.98	1.00/1.00	1.00
K	0.857									0.899		0.901		0.948
P_OA	0.886									0.919		0.921		0.958

Table 5. Classification accuracy evaluation index of different algorithms on Jilin-1GP02 images

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Table 5. Classification accuracy evaluation index of different algorithms on Jilin-1GP02 images

Area type	SVM		LGBM-GBDT		Shallow CNN			MPCNet
Area type	P/R	F₁	P/R	F₁	P/R		F₁	P/R	F₁
Uncultivated land	0.56/0.66					0.61				0.99/0.92	0.95	0.96/0.99	0.98	0.99/0.98	0.99
Cultivated land	0.97/0.98					0.97				0.71/0.67	0.69	0.88/0.85	0.86	0.96/0.94	0.95
Forest land	0.95/0.87					0.90				0.85/0.85	0.85	0.97/0.97	0.97	0.97/0.98	0.97
Shrub	0.56/0.85					0.67				0.44/0.87	0.59	0.75/0.98	0.85	0.88/0.97	0.92
Water	1.00/0.78					0.88				0.75/0.68	0.71	0.92/0.78	0.84	0.95/0.93	0.94
Building	0.62/0.69					0.65				0.80/0.76	0.78	0.83/0.97	0.90	0.92/0.94	0.93
Bare land	0.57/0.49					0.53				0.70/0.70	0.70	0.85/0.78	0.81	0.83/0.89	0.86
K	0.731									0.724		0.877		0.932
P_OA	0.763									0.756		0.891		0.940

Table 6. Processing efficiency of different algorithms on Jilin-1GP01 images

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Table 6. Processing efficiency of different algorithms on Jilin-1GP01 images

Algorithm	Image size	Image storage /Mbit	Feature extraction time /s	Model training time /min	Inference time /min	Total process time /min
SVMLGBM-GBDTShallow CNNMPCNet	5368×4565	888.04	713.424.338.037.5	47.451.3414.9210.80	63.427.422.963.56	122.769.1718.5114.98

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Ruifei Zhu, Jingyu Ma, Zhuqiang Li, Dong Wang, Yuan An, Xing Zhong, Fang Gao, Xiangyu Meng. Domestic Multispectral Image Classification Based on Multilayer Perception Convolutional Neural Network[J]. Acta Optica Sinica, 2020, 40(15): 1528003

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Paper Information

Category: Remote Sensing and Sensors

Received: Apr. 1, 2020

Accepted: May. 6, 2020

Published Online: Aug. 5, 2020

The Author Email: Li Zhuqiang (skybelongtous@foxmail.com)

DOI:10.3788/AOS202040.1528003

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Band selection, spatial resolution, and shooting time of multispectral satellite

Table 1. Band selection, spatial resolution, and shooting time of multispectral satellite

Table 2. Band selection, spatial resolution, and shooting time of Jilin-1GP02

Table 2. Band selection, spatial resolution, and shooting time of Jilin-1GP02

Table 3. Classification accuracy evaluation index of different satellite data based on MPCNet algorithm

Table 3. Classification accuracy evaluation index of different satellite data based on MPCNet algorithm

Table 4. Classification accuracy evaluation index of different algorithms on Jilin-1GP01 images

Table 4. Classification accuracy evaluation index of different algorithms on Jilin-1GP01 images

Table 5. Classification accuracy evaluation index of different algorithms on Jilin-1GP02 images

Table 5. Classification accuracy evaluation index of different algorithms on Jilin-1GP02 images

Table 6. Processing efficiency of different algorithms on Jilin-1GP01 images

Table 6. Processing efficiency of different algorithms on Jilin-1GP01 images