Abstract
Abstract. Shapes and surface roughness, which are considered as key indicators in understanding Arctic sea-ice, can be measured from the digital surface model (DSM) of the target area. Unmanned aerial vehicle (UAV) flying at low altitudes enables theoretically accurate DSM generation. However, the characteristics of sea-ice with textureless surface and incessant motion make image matching difficult for DSM generation. In this paper, we propose a method for effectively detecting incorrect matches before correcting a sea-ice DSM derived from UAV images. The proposed method variably adjusts the size of search window to analyze the matching results of DSM generated and distinguishes incorrect matches. Experimental results showed that the sea-ice DSM produced large errors along the textureless surfaces, and that the incorrect matches could be effectively detected by the proposed method.
Highlights
The demand for Arctic sea-ice information is increasing rapidly for predicting Arctic climate change and for exploring northern sea route
We have studied a method for effectively detecting incorrect matches in a sea-ice digital surface model (DSM) derived from Unmanned aerial vehicle (UAV) images
In order to analyze the performance of the proposed method, we generated a DSM using UAV images taken at sea-ice camp, and registered with laser scanning data acquired for the same target area
Summary
The demand for Arctic sea-ice information is increasing rapidly for predicting Arctic climate change and for exploring northern sea route. In Arctic regions, it is not easy to obtain field data due to severe weather, inaccessible zones, and limited human resources For these reasons, in the recent polar region researches, field data acquisition using unmanned aerial vehicles (UAVs) is being considered (Hagen et al, 2014; Divine et al, 2016). In the recent polar region researches, field data acquisition using unmanned aerial vehicles (UAVs) is being considered (Hagen et al, 2014; Divine et al, 2016) This acquisition method is limited in terms of the type and quality of the field data compared to the field survey by human investigators, it is possible to obtain more extensive and dense data. In analysing Arctic sea-ice, shape and surface roughness are treated as key indicators These can be measured from the digital surface model (DSM) for Arctic sea-ice. acquisition of accurate DSM is important in developing satellite remote sensing techniques. In our method, search window size is variably adjusted so that matching results can be reliably distinguished
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