Pine pest detection using remote sensing satellite images combined with a multi-scale attention-UNet model |
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| Institution: | 1. School of Integrated Circuits, Guangdong University of Technology, Guangzhou, Guangdong 510006, China;2. Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407102, Taiwan;3. School of Civil and Engineering Management, Guangzhou Maritime University, Guangzhou, Guangdong, 510725, China;4. Chemical Geological Prospecting Institute of Liaoning Province Co., Ltd, 121000, China;5. Pengcheng Laboratory, Shenzhen 518055, China;1. School of Computer Science and Engineering, Central South University, Changsha 410083, China;2. ChangSha XiangFeng Intelligent Equipment Co., Ltd, Changsha 410083, China;1. Ben-Gurion University of the Negev, The Jacob Blaustein Institutes for Desert Research, The French Associates Institute for Agriculture and Biotechnology of Drylands, Midreshet Ben-Gurion, Israel;2. Ben-Gurion University of the Negev, The Jacob Blaustein Institutes for Desert Research, The Swiss Institute for Dryland Environmental and Energy Research, Midreshet Ben-Gurion, Israel;3. Ben-Gurion University of the Negev, Faculty of Humanities and Social Sciences, Geography and Environmental Development, Beer-Sheva, Israel;1. National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China;2. School of Information Science and Technology, Fudan University, Shanghai 200433, China;1. Lushan Botanical Garden, Chinese Academy of Sciences, Jiangxi Province 332900, PR China;2. College of Forestry, Nanjing Forestry University, Nanjing 210037, PR China;3. Jiangsu Wiscom Technology Co. Ltd, Nanjing 211100, PR China |
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| Abstract: | Pest monitoring of forest areas is essential to pest control. The existing remote sensing satellite image methods have been widely used in detecting pine wilt disease due to their low cost and large detection range. However, most existing methods for pine wilt disease detection are based on multi-phase remote sensing satellite imagery and use manually designed features or machine learning-based algorithms. This makes these methods time-consuming and does not allow early detection of pest-infested forests and can also lead to further spread of the disease. In addition, machine learning-based algorithms can have poor detection performance and generalization ability. To address these shortcomings, this paper uses the pine forest in the Qingyuan area of Liaoning Province in China as a study area to analyze the physiological characteristics of pine pests based on the aerial photography data collected by a Quadrotor-type unmanned aerial vehicle (UAV). By combining these data with the artificial field survey data, the pest-infested areas of forest are marked in the Landsat 8 satellite remote sensing (SRS) images. Further, an end-to-end automatic pest detection framework is designed based on a multi-scale attention-UNet (MA-UNet) model and monophasic images. In addition, the detection performance of the developed model is further optimized using the data augmentation technique to extend the labeled dataset. Compared with the traditional model, the proposed model achieves a much better recall rate of 57.38% in detecting pest-infested forest areas, while the recall rates of the Support Vector Machine (SVM), UNet, attention-UNet, and MedT models are 14.38%, 49.33%, 48.02%, and 33.64%, respectively. According to the results, the proposed model can achieve timely detection and screening of pest-infested forest areas, improving forest management efficiency. |
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