Accurate crop distribution maps provide important information for crop censuses, yield monitoring and agricultural insurance assessments. Most existing studies apply low spatial resolution satellite images for crop distribution mapping, even in areas with a fragmented landscape. Unmanned aerial vehicle (UAV) imagery provides an alternative imagery source for crop mapping, yet its spectral resolution is usually lower than satellite images. In order to produce more accurate maps without losing any spatial heterogeneity (e.g., the physical boundary of land parcel), this study fuses Sentinel-2A and UAV images to map crop distribution at a finer spatial scale (i.e., land parcel scale) in an experimental site with various cropping patterns in Heilongjiang Province, Northeast China. Using a random forest algorithm, the original, as well as the fused images, are classified into 10 categories: rice, corn, soybean, buckwheat, other vegetations, greenhouses, bare land, water, roads and houses. In addition, we test the effect of UAV image choice by fusing Sentinel-2A with different UAV images at multiples spatial resolutions: 0.03 m, 0.10 m, 0.50 m, 1.00 m and 3.00 m. Overall, the fused images achieved higher classification accuracies, ranging between 10.58% and 16.39%, than the original images. However, the fused image based on the finest UAV image (i.e., 0.03 m) does not result in the highest accuracy. Instead, the 0.10 m spatial resolution UAV image produced the most accurate map. When the spatial resolution is less than 0.10 m, accuracy decreases gradually as spatial resolution decreases. The results of this paper not only indicate the possibility of combining satellite images and UAV images for land parcel level crop mapping for fragmented landscapes, but it also implies a potential scheme to exploit optimal choice of spatial resolution in fusing UAV images and Sentinel-2A, with little to no adverse side-effects.

Accurate localization of moving sensors is essential for many fields, such as robot navigation and urban mapping. In this paper, we present a framework for GPS-supported visual Simultaneous Localization and Mapping with Bundle Adjustment (BA-SLAM) using a rigorous sensor model in a panoramic camera. The rigorous model does not cause system errors, thus representing an improvement over the widely used ideal sensor model. The proposed SLAM does not require additional restrictions, such as loop closing, or additional sensors, such as expensive inertial measurement units. In this paper, the problems of the ideal sensor model for a panoramic camera are analysed, and a rigorous sensor model is established. GPS data are then introduced for global optimization and georeferencing. Using the rigorous sensor model with the geometric observation equations of BA, a GPS-supported BA-SLAM approach that combines ray observations and GPS observations is then established. Finally, our method is applied to a set of vehicle-borne panoramic images captured from a campus environment, and several ground control points (GCP) are used to check the localization accuracy. The results demonstrated that our method can reach an accuracy of several centimetres.

The segmentation of crop disease zones is an important task of image processing since the knowledge of the growth status of crops is critical for agricultural management. Nowadays, images taken by unmanned aerial vehicles (UAVs) have been widely used in the segmentation of crop diseases, and almost all current studies use the study paradigm of full supervision, which needs a large amount of manually labelled data. In this study, a weakly supervised method for disease segmentation of UAV images is proposed. In this method, auxiliary branch block (ABB) and feature reuse module (FRM) were developed. The method was tested using UAV images of maize northern leaf blight (NLB) based on image-level labels only, i.e., only the information as to whether NBL occurs is given. The quality (intersection over union (IoU) values) of the pseudo-labels in the validation dataset achieved 43% and the F1 score reached 58%. In addition, the new method took 0.08 s to generate one pseudo-label, which is highly efficient in generating pseudo-labels. When pseudo-labels from the train dataset were used in the training of segmentation models, the IoU values of disease in the test dataset reached 50%. These accuracies outperformed the benchmarks of the ACoL (45.5%), RCA (36.5%), and MDC (34.0%) models. The segmented NLB zones from the proposed method were more complete and the boundaries were more clear. The effectiveness of ABB and FRM was also explored. This study is the first time supervised segmentation of UAV images of maize NLB using only image-level data was applied, and the above test results confirm the effectiveness of the proposed method.