This paper exploits an effective water extraction method using SAR imagery in preparation for flood mapping in unpredictable flood situations. The proposed method is based on the thresholding method using SAR amplitude, terrain information, and object-based classification techniques for noise removal. Since the water areas in SAR images have the lowest amplitude value, the thresholding method using SAR amplitude could effectively extract water bodies. However, the reflective properties of water areas in SAR imagery cannot distinguish the occluded areas caused by steep relief and they can be eliminated with terrain information. In spite of the thresholding method using SAR amplitude and terrain information, noises which interfered with users’ interpretation of water maps still remained and the object-based classification using an object size criterion was applied for the noise removal and the criterion was determined by a histogram-based technique. When only using SAR amplitude information, the overall accuracy was 83.67%. However, using SAR amplitude, terrain information and the noise removal technique, the overall classification accuracy over the study area turned out to be 96.42%. In particular, user accuracy was improved by 46.00%.
This paper proposes a practical calibration solution for estimating the boresight and lever-arm parameters of the sensors mounted on a Mobile Mapping System (MMS). On our MMS devised for conducting the calibration experiment, three network video cameras, one mobile laser scanner, and one Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) were mounted. The geometric relationships between three sensors were solved by the proposed calibration, considering the GNSS/INS as one unit sensor. Our solution basically uses the point cloud generated by a 3-dimensional (3D) terrestrial laser scanner rather than using conventionally obtained 3D ground control features. With the terrestrial laser scanner, accurate and precise reference data could be produced and the plane features corresponding with the sparse mobile laser scanning data could be determined with high precision. Furthermore, corresponding point features could be extracted from the dense terrestrial laser scanning data and the images captured by the video cameras. The parameters of the boresight and the lever-arm were calculated based on the least squares approach and the precision of the boresight and lever-arm could be achieved by 0.1 degrees and 10 mm, respectively.
A terrestrial Light Detection and Ranging (LIDAR) system has high productivity and accuracy for topographic mapping, but the harsh conditions of Antarctica make LIDAR operation difficult. Low temperatures cause malfunctioning of the LIDAR system, and unpredictable strong winds can deteriorate data quality by irregularly shaking co-registration targets. For stable and efficient LIDAR operation in Antarctica, this study proposes and demonstrates the following practical solutions: (1) a lagging cover with a heating pack to maintain the temperature of the terrestrial LIDAR system; (2) co-registration using square planar targets and two-step point-merging methods based on extracted feature points and the Iterative Closest Point (ICP) algorithm; and (3) a georeferencing module consisting of an artificial target and a Global Navigation Satellite System (GNSS) receiver. The solutions were used to produce a topographic map for construction of the Jang Bogo Research Station in Terra Nova Bay, Antarctica. Co-registration and georeferencing precision reached 5 and 45 mm, respectively, and the accuracy of the Digital Elevation Model (DEM) generated from the LIDAR scanning data was ±27.7 cm.
In unpredictable disaster scenarios, it is important to recognize the situation promptly and take appropriate response actions. This study proposes a cloud computing-based data collection, processing, and analysis process that employs a crowd-sensing application. Clustering algorithms are used to define the major damage types, and hotspot analysis is applied to effectively filter critical data from crowdsourced data. To verify the utility of the proposed process, it is applied to Icheon-si and Anseong-si, both in Gyeonggi-do, which were affected by heavy rainfall in 2020. The results show that the types of incident at the damaged site were effectively detected, and images reflecting the damage situation could be classified using the application of the geospatial analysis technique. For 5 August 2020, which was close to the date of the event, the images were classified with a precision of 100% at a threshold of 0.4. For 24–25 August 2020, the image classification precision exceeded 95% at a threshold of 0.5, except for the mudslide mudflow in the Yul area. The location distribution of the classified images showed a distribution similar to that of damaged regions in unmanned aerial vehicle images.
With the increasing demands of 3D spatial information in urban environment, the importance of point clouds generation techniques have been increased. In particular, for as-built BIM, the point clouds with the high accuracy and density is required to describe the detail information of building components. Since the terrestrial LiDAR has high performance in terms of accuracy and point density, it has been widely used for asbuilt 3D modelling. However, the high cost of devices is obstacle for general uses, and the image-based 3D reconstruction technique is being a new attraction as an alternative solution. This paper compares the imagebased 3D reconstruction technique and the terrestrial LiDAR in point of establishing the as-built BIM of outdoor structures. The point clouds generated from the image-based 3D reconstruction technique could roughly present the 3D shape of a building, but could not precisely express detail information, such as windows, doors and a roof of building. There were 13.2~28.9 cm of RMSE between the terrestrial LiDAR scanning data and the point clouds, which generated from smartphone and DSLR camera images. In conclusion, the results demonstrate that the image-based 3D reconstruction can be used in drawing building footprint and wireframe, and the terrestrial LiDAR is suitable for detail 3D outdoor modeling. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Geometric correction of SAR satellite imagery is the process to adjust the model parameters that define the relationship between ground and image coordinates. To achieve sub-pixel geolocation accuracy, the adoption of the appropriate geometric correction model and parameters is important. Until now, various geometric correction models have been developed and applied. However, it is still difficult for general users to adopt a suitable geometric correction models having sufficient precision. In this regard, this paper evaluated the orbit-based and time-offset-based models with an error simulation. To evaluate the geometric correction models, Radarsat-1 images that have large errors in satellite orbit information and TerraSAR-X images that have a reportedly high accuracy in satellite orbit and sensor information were utilized. For Radarsat-1 imagery, the geometric correction model based on the satellite position parameters has a better performance than the model based on time-offset parameters. In the case of the TerraSAR-X imagery, two geometric correction models had similar performance and could ensure sub-pixel geolocation accuracy.
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