With increasing consumption, plastic mulch benefits agriculture by promoting crop quality and yield, but the environmental and soil pollution is becoming increasingly serious. Therefore, research on the monitoring of plastic mulched farmland (PMF) has received increasing attention. Plastic mulched farmland in unmanned aerial vehicle (UAV) remote images due to the high resolution, shows a prominent spatial pattern, which brings difficulties to the task of monitoring PMF. In this paper, through a comparison between two deep semantic segmentation methods, SegNet and fully convolutional networks (FCN), and a traditional classification method, Support Vector Machine (SVM), we propose an end-to-end deep-learning method aimed at accurately recognizing PMF for UAV remote sensing images from Hetao Irrigation District, Inner Mongolia, China. After experiments with single-band, three-band and six-band image data, we found that deep semantic segmentation models built via single-band data which only use the texture pattern of PMF can identify it well; for example, SegNet reaching the highest accuracy of 88.68% in a 900 nm band. Furthermore, with three visual bands and six-band data (3 visible bands and 3 near-infrared bands), deep semantic segmentation models combining the texture and spectral features further improve the accuracy of PMF identification, whereas six-band data obtains an optimal performance for FCN and SegNet. In addition, deep semantic segmentation methods, FCN and SegNet, due to their strong feature extraction capability and direct pixel classification, clearly outperform the traditional SVM method in precision and speed. Among three classification methods, SegNet model built on three-band and six-band data obtains the optimal average accuracy of 89.62% and 90.6%, respectively. Therefore, the proposed deep semantic segmentation model, when tested against the traditional classification method, provides a promising path for mapping PMF in UAV remote sensing images.

Main problem of stitching unmanned aerial vehicle (UAV) thermal infrared remote sensing (TIRS) images lies that the cumulative error caused by the inaccurate alignment of image matching pairs easily leads to deformation and even failure. Few studies in the literature are reported in stitching TIRS images. For the first time, we propose a simple and robust TIRS image stitching method by exploring prior information during flight. Firstly, according to the position and orientation system (POS) information and parameter of camera, the overlap ratio of adjacent images is estimated, and the image pairs with high overlap ratio and high matching confidence in different directions are selected. Then, they are added into the alignment term of global similarity prior (GSP) model. Therefore, each image has more matching pairs constraints compared to traditional construction method of matching pairs, which greatly improves the local registration capability of GSP and then prevents it from converging to the local optimal solution. The proposed method was extensively evaluated on a dataset including to twenty-four groups of large-scale farmland TIRS images collected in four experimental areas under different crop growth periods and meteorological conditions. Compared with two commercial tools and two representative stitching algorithms, the proposed method significantly improves the local alignment ability and overall stitching quality on both qualitative and quantitative evaluation. Besides, when the front overlap ratio is reduced from 85% to 70%, the proposed method still shows obvious advantages over the related methods and commercial tools, which improves the acquisition efficiency of UAV TIRS images.