The geolocation of skyline provides an important application in unmanned vehicles, unmanned aerial vehicles, and other fields. However, the existing methods are not effective in hilly areas. In this paper, we analyze the difficulties to locate in hilly areas and propose a new geolocation method. According to the vegetation in hilly area, two new skyline features, enhanced angle chain code and lapel point, are proposed. In order to deal with the skyline being close to the camera, we also propose a matching method which incorporates skyline distance heatmap and skyline pyramid. The experimental results show that the proposed method is highly effective in hilly area and has a robust performance against noise and rotation effects.
Viewshed computation of a digital elevation model (DEM) plays an important role in a geographic information system, but the required high computational time is a serious problem for a practical application. Hitherto, the mainstream methods of viewshed computing include line-of-sight method, reference planes method, etc. Based on these classical algorithms, a new algorithm for viewshed computation is proposed in this paper: the Matryoshka doll algorithm. Through a pooling operation, the minimum expected height of the DEM is introduced as max-pooling with minimum expected height in the viewshed computing optimization. This is to increase the efficiency and adaptability of the computation of the visibility range. The experimental results demonstrate that the algorithm has obvious advantages in computing speed, but with the accuracy only slightly reduced.
The information unavailability of a global navigation satellite system (GNSS) for geolocalization in many military and tourism applications implies the need to employ other applicable types of information, such as automatic image-based location recognition. Unlike most existing localization methods that use GNSS information to locate an initial position, this paper proposes an automatic geo-localization framework that does not require GNSS data. The proposed framework is a two-stage pipeline that uses a query image and digital elevation model (DEM) data as input. The authors frame automatic geolocalization without GNSS recordings as a skyline matching problem. By extracting the skyline from the DEM data and query images, the query image can be localized by matching the query skyline feature to the DEM skyline database. It has been demonstrated that this low-cost approach can perform efficiently in mountainous or hilly areas to produce reliable localization results. The system was tested on 50 testing site points within a largescale area (China, 202.6 km 2 ), and an average position error of 43.13 m was detected within 4.5 s.
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