Pixel–Scene–Pixel–Object Sample Transferring: A Labor-Free Approach for High-Resolution Plastic Greenhouse Mapping

As an important agriculture technique, plastic greenhouse (PG) has been widely used to increase crop yield and improve food security status in the world. The high-resolution spatial information of PG is of great significance to precise agricultural management and quantitative environmental assessment. Many studies have examined the role that remote sensing (RS) technology could play in mapping and monitoring PG coverage. However, these methods, which employ either the traditional machine learning algorithms or the deep learning models, depend on massive manually labeled samples. To address this problem, this article proposes a new cross-scale sample transferring method to generate high-resolution samples for automated PG mapping. The proposed method aims to transfer reliable label information from Sentinel-2 images (10 m) to high-resolution images (0.2 m) in a pixel–scene–pixel–object (PSPO) transferring process. In the proposed PG mapping workflow, the low-resolution label information of PG/non-PG can be obtained from an advanced plastic greenhouse index (APGI) which is calculated in Sentinel-2 images, and then, the label information is transferred to the corresponding high-resolution images using the proposed PSPO transferring method. Finally, the transferred high-resolution samples are used to train the deep semantic segmentation model and produce PG mapping results. The whole process is labor-free which requires no manually labeled samples. The experimental results on three collected datasets show that the proposed approach can automatically generate accurate and reliable high-resolution samples, and the final PG mapping results can achieve an overall accuracy (OA) of 89.52%–97.65% and F1 score of 84.13%–94.03%, which is comparable to the fully supervised semantic segmentation model.