Mapping Center Pivot Irrigation Systems in the Southern Amazon from Sentinel-2 Images - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Water Année : 2021

Mapping Center Pivot Irrigation Systems in the Southern Amazon from Sentinel-2 Images

Jiwen Tang
  • Fonction : Auteur
Ping Tang
  • Fonction : Auteur

Résumé

Irrigation systems play an important role in agriculture. Center pivot irrigation systems are popular in many countries as they are labor-saving and water consumption efficient. Monitoring the distribution of center pivot irrigation systems can provide important information for agricultural 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 is proposed. The proposed method combines a lightweight real-time object detection network (PVANET) based on deep learning, an image classification model (GoogLeNet) and accurate shape detection (Hough transform) to detect and accurately delineate center pivot irrigation systems and their associated circular shape. PVANET is lightweight and fast and GoogLeNet can reduce the false detections associated with PVANET, while Hough transform can accurately detect the shape of center pivot irrigation systems. Experiments with Sentinel-2 images in Mato Grosso achieved a precision of 95% and a recall of 95.5%, which demonstrated the effectiveness of the proposed method. Finally, with the accurate shape of center pivot irrigation systems detected, the area of irrigation in the region was estimated.
Fichier principal
Vignette du fichier
water-13-00298-v2.pdf (1.84 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03717193 , version 1 (11-01-2024)

Identifiants

Citer

Jiwen Tang, Damien Arvor, Thomas Corpetti, Ping Tang. Mapping Center Pivot Irrigation Systems in the Southern Amazon from Sentinel-2 Images. Water, 2021, 13 (3), pp.298. ⟨10.3390/w13030298⟩. ⟨hal-03717193⟩
42 Consultations
17 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More