PVANET-HOUGH: DETECTION AND LOCATION OF CENTER PIVOT IRRIGATION SYSTEMS FROM SENTINEL-2 IMAGES

• Published in: ISPRS annals of the photogrammetry, remote sensing and spatial information sciences Vol. V-3-2020; pp. 559 - 564
• Main Authors: Tang, J. W., Arvor, D., Corpetti, T., Tang, P.
• Format: Journal Article
• Language: English
• Published: Gottingen Copernicus GmbH 03-08-2020; Copernicus Publications
• Subjects: Agriculture; Center-pivot irrigation; Deep learning; False alarms; Hough transformation; Image classification; Irrigation; Irrigation efficiency; Irrigation systems; Land use; Lightweight; Machine learning; Object recognition; Water conservation; Water consumption
• ISSN: 2194-9050; 2194-9042; 2194-9050
• DOI: 10.5194/isprs-annals-V-3-2020-559-2020

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.