协同多源国产高分影像和面向对象方法的南方农作物遥感识别

doi:10.3864/j.issn.0578-1752.2023.13.004

Abstract

Abstract:

【Objective】 Due to frequent cloudy and rainy weather, it is challenging to map crop types over South China with fragmented agricultural landscapes. The Gaofen (GF) series satellites developed by China have high spatial-temporal resolution and good image quality. This study exploited the spatial and temporal advantages of multi-source GF images for fine crop classification in heterogeneous agricultural areas with frequent clouds and rain. 【Method】This study characterized the spatial geometry of agricultural fields based on GF-2 data with high spatial resolution, and cooperated with the encrypted observations from GF-1 and GF-6 time series to fully characterize the spectral seasonal variations of crops. By constructing three-dimensional classification features of spectrum-time-space, the crop classification based on random forest classifier were conducted, and the importance scores of different features were calculated. Furthermore, several classification scenarios were set based on different satellite combinations and different classification units, for further analyzing the performances of integrating different GF datasets on crop type mapping. 【Result】The overall accuracy of synergistic GF-1, GF-2 and GF-6 by object-oriented crop classification was 95.49% with Kappa of 0.94 in Qianjiang city; the overall accuracy in Zaoyang city was 93.78% with Kappa of 0.92. The accuracy of crop classification by integrating GF-2 and GF-6 was higher than that by GF-2 and GF-1. In addition, the object-oriented crop classification based on GF-2 outperformed the pixel-oriented one, in which the overall accuracy improved by 1.4% and 1.32% in Qianjiang and Zaoyang, respectively. Compared with the spectral and spatial features of GF-1 and GF-2, the GF-6 spectral bands had the largest contribution to crop type identification, and the cumulative importance score accounted for 82% (Qianjiang) and 77% (Zaoyang) of all spectral bands. Among them, the four new spectral bands of GF-6, namely, red-edge I band (B5), red-edge Ⅱ band (B6), purple band (B7) and yellow band (B8), accounted for 47% (Qianjiang) and 33% (Zaoyang) of all spectral bands for crop type identification. 【Conclusion】Integrating multi-source GF images by taking advantages of their spectral, spatial and temporal features could not only alleviate the "mixed pixel" problem caused by the fragmented agricultural landscape, but also reduce the uncertainty of crop type identification in cloudy and rainy areas, providing great potential for accurate crop mapping in South China.

Key words: GF data, object-oriented, random forest, remote sensing identification, crop classification

WANG JiaYue, CAI ZhiWen, WANG WenJing, WEI HaoDong, WANG Cong, LI ZeXuan, LI XiuNi, HU Qiong. Integrating Multi-Source Gaofen Images and Object-Based Methods for Crop Type Identification in South China[J].Scientia Agricultura Sinica, 2023, 56(13): 2474-2490.

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参数Parameter		GF-2 PMS	GF-1 WFV	GF-6 WFV
光谱范围 Spectral range (μm)	全色Panchromatic band	0.45-0.90	—	—
	蓝Blue（B₁）	0.45-0.52	0.45-0.52	0.45-0.52
	绿Green（B₂）	0.52-0.59	0.52-0.59	0.52-0.59
	红Red（B₃）	0.63-0.69	0.63-0.69	0.63-0.69
	近红外NIR（B₄）	0.77-0.89	0.77-0.89	0.77-0.89
	红边 I Red-edge I（B₅）	—	—	0.69-0.73
	红边Ⅱ Red-edge Ⅱ（B₆）	—	—	0.73-0.77
	紫Purple（B₇）	—	—	0.40-0.45
	黄Yellow（B₈）	—	—	0.59-0.63
幅宽Width (km)		45	800
空间分辨率Spatial resolution (m)		1（全色PAN）/4（多光谱MSS）	16
重访周期 Revisit cycle (d)		5	2（GF-1和GF-6组网运行 Joint operation with GF-1 and GF-6 satellites）

方案 Scenarios	数据源与分类单元 Data source and classification units	光谱特征 Spectral features GF-1/GF-6	空间特征 Spatial features GF-2	影像数量 Number of images			特征总数 Number of features
方案 Scenarios	数据源与分类单元 Data source and classification units	光谱特征 Spectral features GF-1/GF-6	空间特征 Spatial features GF-2		潜江 Qianjiang	枣阳 Zaoyang	潜江 Qianjiang	枣阳 Zaoyang
1 试验组 Experimental group	GF-2（面向对象 Object-oriented） &GF-1+GF-6	蓝、绿、红、近红外、红边Ⅰ、红边Ⅱ、紫、黄 Blue, Green, Red, NIR, Red-edge I, Red-edge Ⅱ, Purple, Yellow	范围、方向、最大边长、最小边长 Extent, Orientation, Major length, Minor length	GF-1	3	8	56	116
				GF-6	5	10
				GF-2	1	1
2 对照组 Control group	GF-2（面向对象 Object-oriented） &GF-1	蓝、绿、红、近红外 Blue, Green, Red, NIR	范围、方向、最大边长、最小边长 Extent, Orientation, Major length, Minor length	GF-1	3	8	16	36
3 对照组 Control group	GF-2（面向对象 Object-oriented） &GF-6	蓝、绿、红、近红外、红边Ⅰ、红边Ⅱ、紫，黄 Blue, Green, Red, NIR, Red-edge I, Red-edge Ⅱ, Purple, Yellow	范围、方向、最大边长、最小边长 Extent, Orientation, Major length, Minor length	GF-6	5	10	44	84
3 对照组 Control group	GF-2（面向对象 Object-oriented） &GF-6			GF-2	1	1	44	84
4 对照组 Control group	GF-1+GF-6 （面向像元 Pixel-oriented）	蓝、绿、红、近红外、红边Ⅰ、红边Ⅱ、紫、黄 Blue, Green, Red, NIR, Red-edge I, Red-edge Ⅱ, Purple, Yellow	/	GF-1	3	8	52	112
4 对照组 Control group	GF-1+GF-6 （面向像元 Pixel-oriented）		/	GF-6	5	10	52	112

	方案1 Scenario 1 GF-2（面向对象Object-oriented） &GF-1+GF-6			方案2 Scenario 2 GF-2（面向对象Object-oriented） &GF-1			方案3 Scenario 3 GF-2（面向对象Object-oriented） &GF-6			方案4 Scenario 4 GF-1+GF-6 （面向像元Pixel-oriented）
	PA (%)	UA (%)	F1-score	PA (%)	UA (%)	F1-score	PA (%)	UA (%)	F1-score	PA (%)	UA (%)
小麦 Wheat	96.20	98.70	0.97	92.40	92.40	0.92	96.30	97.43	0.95	97.50	98.73
油菜 Rape	98.77	97.56	0.97	96.29	95.12	0.96	97.53	97.53	0.96	98.79	97.61
早稻 Early rice	98.57	94.52.	0.98	92.85	86.66	0.91	97.14	94.44	0.96	98.57	93.24
虾稻Rice-crayfish	98.79	92.13	0.92	90.36	91.46	0.88	97.59	90.00	0.91	97.59	88.04
其他作物 Other crops	86.00	91.49	0.84	74.00	75.51	0.77	86.00	89.58	0.81	75.47	90.90
总体精度OA (%)	95.49			90.12			94.42			94.09
Kappa	0.94			0.88			0.93			0.93

	方案1 Scenario 1 GF-2（面向对象Object-oriented） &GF-1+GF-6			方案2 Scenario 2 GF-2（面向对象Object-oriented） &GF-1			方案3 Scenario 3 GF-2（面向对象Object-oriented） &GF-6			方案4 Scenario 4 GF-1+GF-6 （面向像元Pixel-oriented）
	PA (%)	UA (%)	F1-score	PA (%)	UA (%)	F1-score	PA (%)	UA (%)	F1-score	PA (%)	UA (%)
小麦 Wheat	94.85	98.92	0.97	91.75	96.73	0.94	92.78	98.90	0.95	93.00	96.87
早稻 Early rice	92.86	85.53	0.89	88.57	79.48	0.84	91.42	85.33	0.88	90.24	85.05
其他作物 Other crops	80.36	83.33	0.82	73.21	78.84	0.76	82.14	79.31	0.80	77.04	79.66
总体精度OA (%)	93.78			91.35			92.97			92.46
Kappa	0.92			0.89			0.91			0.90

Integrating Multi-Source Gaofen Images and Object-Based Methods for Crop Type Identification in South China

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