期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2020
卷号：V-3-2020
页码：559-564
DOI：10.5194/isprs-annals-V-3-2020-559-2020
语种：English
出版社：Copernicus Publications
摘要：Irrigation systems play an important role in agriculture. As being labor-saving and water consumption efficient, center pivot irrigation systems are popular in many countries. Monitoring the distribution of center pivot irrigation systems can provide important information for agriculture production, water consumption and land use. Deep learning has become an effective method for image classification and object detection. In this paper, a new method to detect the precise shape of center pivot irrigation systems, PVANET-Hough, is proposed. The proposed method combines a lightweight real-time object detection network PVANET based on deep learning and accurate shape detection Hough transform to detect and accurately locate center pivot irrigation systems. The method proposed in this paper does not need any preprocessing, PVANET is lightweight and fast, Hough transform can accurately detect the shape of center pivot irrigation systems, and reduce the false alarms of PVANET at the mean time. Experiments with the Sentinel-2 images in Mato Grosso demonstrated the effectiveness of the proposed method.
