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%.

An enormous amount of transit data, created when people use their transportation cards, is being used effectively as smart card-based fare payment systems are being widely implemented. In Korea, the transportation card was first introduced by the Seoul Special City government in 1996 on its local bus services and has been compatible with the metropolitan subway system since 2000. Public transit card data can be used for many studies including determining the flow of transit passengers, which makes it possible to establish effective land development and transportation policies at a national level. In this paper, we aim to identify the daily travel patterns of metropolitan subway passengers as they move through stations using clustering analysis and reveal the relationship between land cover and demographic factors by using regression analysis. The main functions are confirmed in accordance with the intended use of each station and classified into five types. Moreover, we identified the features and influencing factors of the station influence area utilizing demographic data and a land cover map. The results obtained by clustering and regression analysis showed the clear relationship between travel patterns and the other characteristics in the Seoul Metropolitan Area. The results of this study could be important for national development strategies and traffic planning.

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.

In this study, a parallel processing method using a PC cluster and a virtual grid is proposed for the fast processing of enormous amounts of airborne laser scanning (ALS) data. The method creates a raster digital surface model (DSM) by interpolating point data with inverse distance weighting (IDW), and produces a digital terrain model (DTM) by local minimum filtering of the DSM. To make a consistent comparison of performance between sequential and parallel processing approaches, the means of dealing with boundary data and of selecting interpolation centers were controlled for each processing node in parallel approach. To test the speedup, efficiency and linearity of the proposed algorithm, actual ALS data up to 134 million points were processed with a PC cluster consisting of one master node and eight slave nodes. The results showed that parallel processing provides better performance when the computational overhead, the number of processors, and the data size become large. It was verified that the proposed algorithm is a linear time operation and that the products obtained by parallel processing are identical to those produced by sequential processing.