• 遥感科学与应用技术 •

### 设施农业典型地物改进Faster R-CNN识别方法

1. 1. 南京师范大学虚拟地理环境教育部重点实验室,南京 210023
2. 江苏省地理信息资源开发与利用协同创新中心,南京 210023
3. 江苏省地理环境演化国家重点实验室培育建设点,南京 210023
4. 江西理工大学建筑与测绘工程学院,赣州 341000
• 收稿日期:2018-12-29 修回日期:2019-05-14 出版日期:2019-09-25 发布日期:2019-09-24
• 通讯作者: 刘学军 E-mail:liuxuejun@njnu.edu.cn
• 作者简介:王 兴（1992-）,男,安徽宿州人,博士生,主要从事深度学习、视频GIS、无线传感网络研究。E-mail: jwangxing0719@163.com
• 基金资助:
国家自然科学基金项目(41771420);国家高技术研究发展计划项目(2015AA123901);江苏高校优势学科建设工程资助项目

### Improving the Faster R-CNN Method for Recognizing Typical Objects of Modern Agriculture based on Remote Sensing Imagery

WANG Xing1,2,3,KANG Junfeng4,LIU Xuejun1,2,3,*(),WANG Meizhen1,2,3,ZHANG Chao4

1. 1. Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
2. State Key Laboratory Cultivation base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
4. Faculty of Architectural and Surveying Engineering, Jiang Xi University of Science and Technology, Ganzhou 341000, China;
• Received:2018-12-29 Revised:2019-05-14 Online:2019-09-25 Published:2019-09-24
• Contact: LIU Xuejun E-mail:liuxuejun@njnu.edu.cn
• Supported by:
National Natural Science Foundation of China(41771420);National High-tech R&D Program of China(2015AA123901);Funded by the Priority Academic Program Development of Jiangsu Higher Education Institution

Abstract:

The development of modern agriculture is directly related to the transformation of the traditional agriculture. The recognition and extraction of typical objects of modern agriculture (TOMA) through remote sensing imagery has many advantages and has become the mainstream of current applications. Since traditional recognition methods are easily affected by external environmental factors (e.g., the shape, size, color, and texture of TOMA, and the distance, angle, and weather conditions for obtaining the remote sensing imagery), the accuracy of recognition results is usually difficult to meet application requirements. In the recent years, deep learning methods have seen wide applications in many fields, which greatly promote the advancement of artificial intelligence. Convolutional Neural Network (CNN) has acquired breakthrough research results in image classification, object detection, semantic segmentation, and so on. Based on the structure of CNN, many excellent network structures have been developed, such as Regions with CNN, Fast R-CNN, Mask R-CNN, etc. In particula, Faster R-CNN is one of the mainstream algorithms for target detection. However, when directly applied to the recognition of TOMA, the Faster R-CNN still has some drawbacks to be improved, especially the problem of small targets with large background. By taking the image features of TOMA into account, a DRTOMA (Deep Residual TOMA) algorithm was proposed in this paper based on the idea of deep residual network and Faster R-CNN. Firstly, the deep residual network was used as the basic feature extraction network to obtain deeper features and suppress the network degenerate problems. Secondly, an improved spatial pyramid pooling layer was added between the residual unit and the fully connected layer to remove the fixed size limit of the input image while increasing the sensitivity to the scale of the network. Lastly, a dropout layer was added between the fully connected layers to reduce the complexity of the network and improve the over-fitting effect. Simulation results showed that compared with some existing algorithms, the average recognition accuracy and recall rate of the DRTOMA algorithm were optimal, being 91.87% and 90.643%, respectively. The recognition accuracy of the DRTOMA algorithm and that of Faster R-CNN were similar. However, the DRTOMA algorithm had a recall rate of about 2% higher than the Faster R-CNN algorithm, and the network was easier to converge and can be trained for a shorter time. Our findings suggest that the DRTOMA algorithm is an effective and feasible TOMA detection method.