Width is one of the key parameters of a shelterbelt. Traditional methods to acquire this width are mainly based on field measurement, which is impractical for monitoring shelterbelts at regional scale. There are many studies analyzing linear objects, but they are not directly applicable to width detection of such objects. In this paper, we analyzed relationships among vegetation fractions retrieved from SPOT5 remote sensing imagery with 10 m 9 10 m spatial resolution, shelterbelt area, and shelterbelt width in one pixel. Based on this analysis, we developed a method for recognizing shelterbelt width from a remote sensing image of central western Jilin Province, China. The result was validated by field measurement data and measurement from an aerial image of 0.5 m 9 0.5 m spatial resolution. Mean absolute error was 2.40 and 2.73 m respectively, suggesting that the proposed method is feasible and its accuracy is acceptable. The study provides a valuable method for monitoring shelterbelt width across large spatial scales and an accurate input parameter for the recognition of shelterbelt porosity from remote sensing data in future research.
ABSTRACT:In recent years, extensive research has been conducted to automatically generate high-accuracy and high-precision road orthophotos using images and laser point cloud data acquired from a mobile mapping system (MMS). However, it is necessary to mask out nonroad objects such as vehicles, bicycles, pedestrians and their shadows in MMS images in order to eliminate erroneous textures from the road orthophoto. Hence, we proposed a novel vehicle and its shadow detection model based on Faster R-CNN for automatically and accurately detecting the regions of vehicles and their shadows from MMS images. The experimental results show that the maximum recall of the proposed model was high-0.963 (intersection-over-union>0.7) -and the model could identify the regions of vehicles and their shadows accurately and robustly from MMS images, even when they contain varied vehicles, different shadow directions, and partial occlusions. Furthermore, it was confirmed that the quality of road orthophoto generated using vehicle and its shadow masks was significantly improved as compared to those generated using no masks or using vehicle masks only.
<p><strong>Abstract.</strong> Ground objects can be regarded as a combination of structures of different geometries. Generally, the structural geometries can be grouped into linear, planar and scatter shapes. A good segmentation of objects into different structures can help to interpret the scanned scenes and provide essential clues for subsequent semantic interpretation. This is particularly true for the terrestrial static and mobile laser scanning data, where the geometric structures of objects are presented in detail due to the close scanning distances. In consideration of the large data volume and the large variation in point density of such point clouds, this paper presents a structural segmentation method of point clouds to efficiently decompose the ground objects into different structural components based on supervoxels of multiple sizes. First, supervoxels are generated with sizes adaptive to the point density with minimum occupied points and minimum size constraints. Then, the multi-size supervoxels are clustered into different components based on a structural labelling result obtained via Markov random field. Two datasets including terrestrial and mobile laser scanning point clouds were used to evaluate the performance of the proposed method. The results indicate that the proposed method can effectively and efficiently classify the point clouds into structurally meaningful segments with overall accuracies higher than 96%, even with largely varying point density.</p>
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