Simultaneous extracting area and quantity of agricultural greenhouses in large scale with deep learning method and high-resolution remote sensing images.

Greenhouses are an important part of modern facility-based agriculture. While creating well-being for human society, greenhouses also bring negative impacts such as air pollution, soil pollution, and water pollution. Therefore, it is of great significance to obtain information such as the area and quantity of greenhouses. It is still a challenging task to find a low-cost, high-efficiency, and easy-to-use method for the dual extraction of greenhouse area and quantity on a large scale. In this study, relatively easy-to-obtain high-resolution Google Earth remote sensing images are used as the experimental data source, and an area and quantity simultaneous extraction framework (AQSEF) is constructed to extract both the area and quantity of greenhouses. The AQSEF uses UNet and YOLO v5 series networks as core operators to complete model training and prediction, and main components such as SWP, OSW&NMS and GCA complete data postprocessing. To evaluate the feasibility of our method, we take Beijing, China, as the research area and select multiple accuracy evaluation indicators in the two branches for accuracy verification. The results show that the mIoU, OA, Kappa, Recall and Precision with the best performance model in the area extraction branch can reach 0.931, 0.987, 0.867, 0.91 and 0.914, respectively. Additionally, the Recall, Precision, AP@0.5 and mAP@0.5: 0.95 values of the best performance model are 0.781, 0.891, 0.812 and 0.509, respectively, in the extraction of the quantity of greenhouses. Finally, in Beijing, the area covered by greenhouses is approximately 85.443 km2, and the quantity of greenhouses is approximately 155,464. With the proposed method, the time consumed for area extraction and quantity extraction is 6.73 h and 12.97 h, respectively. The experimental results show that AQSEF helps to overcome the spatiotemporal diversity of greenhouses and quickly and accurately map a high-spatial-resolution greenhouse distribution product within the research area.

On-Orbit Absolute Radiometric Calibration and Validation of ZY3-02 Satellite Multispectral Sensor.

This study described the on-orbit vicarious radiometric calibration of Chinese civilian high-resolution stereo mapping satellite ZY3-02 multispectral imager (MUX). The calibration was based on gray-scale permanent artificial targets, and multiple radiometric calibration tarpaulins (tarps) using a reflectance-based approach between July and September 2016 at Baotou calibration site in China was described. The calibration results reveal a good linear relationship between DN and TOA radiances of ZY3-02 MUX. The uncertainty of this radiometric calibration was 4.33%, indicating that radiometric coefficients of ZY3-02 MUX are reliable. A detailed discussion on the validation analysis of the comparison results between the different radiometric calibration coefficients is presented in this paper. To further validate the reliability of the three coefficients, the calibrated ZY3-02 MUX was compared with Landsat-8 Operational Land Imager (OLI). The results also indicate that radiometric characteristics of ZY3-02 MUX imagery are reliable and highly accurate for quantitative applications.