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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (2) : 242-250     DOI: 10.6046/zrzyyg.2021135
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Recognition of cotton distribution based on GF-2 images and Unet model
ERPAN Anwar1(), MAMAT Sawut1,2,3(), MAIHEMUTI Balati1,2,3
1. College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
2. Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China
3. Key Laboratory for Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830046, China
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Abstract  

The typical crop cotton in the Ugan-Kuqa River Delta Oasis was used as the research object to study the applicability and optimization process of the deep learning method in the identification of cotton distribution in arid areas. Based on the domestic GF-2 images and the field survey data, the Unet deep learning method was adopted, in which the characteristics of the Unet network’s multiple convolution operations were fully utilized to explore the deep-level characteristics of cotton in remote sensing images, thereby improving the precision of cotton extraction. The results show that the recognition effect of the Unet model to extract cotton, corn, and peppers in the study area is better than the classification results of the object-oriented method and the traditional machine learning algorithms. The overall precision is 84.22%, and the Kappa coefficient is 0.804 7. Compared with the object-oriented method and the traditional machine learning algorithms SVM and RF, the overall precision has increased by 7.94 percentage points,11.93 percentage points, and 11.73 percentage points, respectively, and the Kappa coefficient has increased by 10.13%, 14.72%, and 14.60%, respectively. In the classification results of the Unet model, both the mapping precision and the user precision of cotton are higher than those of the other three methods, which are 94.95% and 89.07%, respectively. Therefore, it is feasible and reliable to use the Unet model to extract high-precision cotton spatial distribution information of arid areas on GF-2 high-resolution remote sensing images.
Keywords deep learning      cotton recognition      Unet model      GF-2 images     
ZTFLH:  TP79  
Corresponding Authors: MAMAT Sawut     E-mail: erpan_edu@163.com;korxat@xju.edu.cn
Issue Date: 20 June 2022
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Anwar ERPAN
Sawut MAMAT
Balati MAIHEMUTI
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Anwar ERPAN,Sawut MAMAT,Balati MAIHEMUTI. Recognition of cotton distribution based on GF-2 images and Unet model[J]. Remote Sensing for Natural Resources, 2022, 34(2): 242-250.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021135     OR     https://www.gtzyyg.com/EN/Y2022/V34/I2/242
Fig.1  Schematic diagram of the geographic location, interpretation points, sample distribution and target categories of the study area
Fig.2  Unet model structure diagram
Fig.3  Training loss function value of different optimizers varies with the number of iterations
Fig.4  Loss function and accuracy change curves during model training
地类 Unet 面向对象 SVM RF
PA/% UA/% PA/% UA/% PA/% UA/% PA/% UA/%
棉花 94.95 89.07 69.31 79.86 64.36 79.08 69.48 72.86
玉米 94.81 91.38 88.88 94.81 68.75 65.90 68.92 67.00
辣椒 81.69 91.55 47.06 57.54 69.69 59.16 58.30 45.44
果园 68.30 79.70 74.05 56.51 59.50 64.24 57.33 67.33
林地 61.25 68.30 63.82 87.39 62.21 63.01 64.91 72.24
其他 82.36 72.06 98.23 77.43 97.76 88.45 98.73 88.46
总体精度/% 84.22 76.28 72.29 72.49
Kappa系数 0.804 7 0.703 4 0.657 5 0.658 7
Tab.1  Classification result accuracy evaluation
Fig.5  Classification results in the study area
作物 Unet 面向对象 SVM RF
棉花 0.895 7 0.547 8 0.717 2 0.675 5
玉米 0.847 9 0.819 5 0.561 0 0.577 0
辣椒 0.826 9 0.502 0 0.359 9 0.409 1
果园 0.744 1 0.414 3 0.511 0 0.504 1
林地 0.545 8 0.194 7 0.280 8 0.303 8
其他 0.520 1 0.200 7 0.270 2 0.294 1
平均值 0.730 1 0.446 5 0.450 0 0.460 6
Tab.2  IOU statistic of local areas and objects
区域 假彩色影像 Unet结果 面向对象结果 SVM结果 RF结果
Ⅰ
Ⅱ
Ⅲ
Ⅳ
Tab.3  Schematic diagram of local area classification results
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