Abstract
Abstract. 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.
Highlights
Irrigation systems have import impact on the quality of agriculture production
We evaluate the results with the 77 images in Mato Grosso, the size of the image is 10980 * 10980 pixels, the image is cropped into blocks of 500 * 500 pixels with an overlap of 200 pixels between the neighborhood blocks, these blocks of images are fed into PVANET to detect the center pivot irrigation systems
Since Hough transform used in this paper is to exclude those PVANET mistakenly detected center pivot irrigation systems, so we mainly examine the ability of Hough transform to reduce the false detection rate
Summary
Irrigation systems have import impact on the quality of agriculture production. The use of irrigation systems is an important part of the modernization of agriculture production and intensification management (Arvor et al, 2012). Other means are needed to detect the location of objects accurately, which is why (Zhang et al, 2018) uses variance based approach to locate the center pivot irrigation systems and requires preprocessing to reduce false alarms. The proposed method combines the deep learning method of object detection and accurate shape detection Hough transform, which integrates a lightweight real-time object detection network PVANET (Kim et al, 2016) and accurate shape detecting Hough voting to The method proposed in this paper does not need any preprocessing, PVANET is fast, Hough voting can accurately locate the center and shape of center pivot irrigation systems, and at the mean time reduce the false alarms of the results of PVANET as it cannot detect the shape precisely.
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