文献类型： 外文期刊

作者： Zhou, Jingping ¹ ; Gu, Xiaohe ² ; Gong, Huili ¹ ; Yang, Xin ⁷ ; Sun, Qian ² ; Guo, Lin ¹ ; Pan, Yuchun ² ;

作者机构： 1.Capital Normal Univ, Coll Resources Environm & Tourism, Beijing 100048, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing 100097, Peoples R China

3.Capital Normal Univ, Beijing Lab Water Resources Secur, Beijing 100048, Peoples R China

4.Capital Normal Univ, Base State Key Lab Urban Environm Proc & Digital M, Beijing 100048, Peoples R China

5.Capital Normal Univ, Key Lab Mech Prevent & Mitigat Land Subsidence, MOE, Beijing 100048, Peoples R China

6.MNR, Observat & Res Stn Groundwater & Land Subsidence B, Beijing 100048, Peoples R China

7.Renmin Univ China, Sch Stat, Beijing 100872, Peoples R China

8.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China

关键词： Straw type; UAV; Deep transfer learning; DenseNet201; RGB image

期刊名称：ECOLOGICAL INDICATORS （ 影响因子：7.4； 五年影响因子：7.2 ）

ISSN： 1470-160X

年卷期： 2024 年 166 卷

页码：

收录情况： SCI

摘要： China has abundant straw resources, but challenges in utilization persist. Utilization rates need improvement, and environmental pollution from straw burning remains a significant issue. Accurate and intelligent remote sensing classification of straw types is crucial for enhancing straw utilization and preventing straw burning. This paper proposed a new approach for the intelligent classification of maize straw types, using the DenseNet201 deep transfer learning algorithm based on RGB images captured by Unmanned Aerial Vehicle (UAV). The sample labels dataset was established for maize straw types, utilizing DenseNet201 deep transfer learning algorithm to pre-train the sample set. This pre-training facilitated model transfer and parameter initialization. Subsequently, the second round of deep transfer learning was performed to construct the final maize straw type remote sensing classification models using DenseNet201 deep transfer learning algorithm. This model and results were subsequently compared with maize straw type classification by the ResNet50 and GoogLeNet deep transfer learning algorithms, as well as maize straw type classification using DenseNet201, ResNet50, and GoogLeNet deep learning algorithms. The results showed that the accuracy of the pre-trained maize straw type deep transfer learning remote sensing classification model surpassed that of the untrained maize straw type deep learning remote sensing classification model, resulting in an enhancement of accuracy by 8.59%, 7.38%, and 1.28%, respectively. The DenseNet201 deep transfer learning model for maize straw types exhibited the highest accuracy with the overall accuracy of 95.57%, and the kappa coefficient of 0.9410. Hence, the DenseNet201 deep transfer learning classification of maize straw types enabled the attainment of intelligent remote sensing recognition of maize straw types. The classification methodology, model, and results presented in this paper can serve as valuable technical references, offering essential information support for agricultural and environmental protection departments actively involved in the comprehensive utilization of straw resources and atmospheric environmental protection efforts.