Kiyun Yu

A New Adaptive Component-Substitution-Based Satellite Image Fusion by Using Partial Replacement

Preservation of spectral information and enhancement of spatial resolution are regarded as important issues in remote sensing satellite image fusion. In previous research, various algorithms have been proposed. Although they have been successful, there are still some margins of spatial and spectral quality that can be improved. In addition, a new method that can be used for various types of sensors is required. In this paper, a new adaptive fusion method based on component substitution is proposed to merge a high-spatial-resolution panchromatic (PAN) image with a multispectral image. This method generates high-/low-resolution synthetic component images by partial replacement and uses statistical ratio-based high-frequency injection. Various remote sensing satellite images, such as IKONOS-2, QuickBird, LANDSAT ETM+, and SPOT-5, were employed in the evaluation. Experiments showed that this approach can resolve spectral distortion problems and successfully conserve the spatial information of a PAN image. Thus, the fused image obtained from the proposed method gave higher fusion quality than the images from some other methods. In addition, the proposed method worked efficiently with the different sensors considered in the evaluation.

Registration of aerial imagery and aerial LiDAR data using centroids of plane roof surfaces as control information

This study proposes using centroid of the plane roof surface of a building as control information for registering the aerial im agery relative to the aerial LiDAR data. A majority of the roofs in South Korea are plane roofs. The centroid of the plane roof is extracted from aerial imagery by using the Canny Edge Detector and from aerial LiDAR data by using Local Maxima Filtering. These extracted centroids from LiDAR data are used as control information and exterior orientation parameters of aerial imagery are estimated. Also, exterior orientation parameters of aerial imagery are estimated by using GCPs and the accuracy of registration is evaluated. From the experimental results, the positional accuracy satisfied the error range of 1/5,000 digital map which was prescribed by National Geographic Information Institute of South Korea. From this study, it is found that centroid could be useful source of control information.

Adjustment of Discrepancies Between LIDAR Data Strips Using Linear Features

Despite the recent developments in light detection and ranging systems, discrepancies between strips on overlapping areas persist due to the systematic errors. This letter presents an algorithm that can be used to detect and adjust such discrepancies. To achieve this, extracting conjugate features from the strips is a prerequisite step. In this letter, linear features are chosen as conjugate features because they can be accurately extracted from man-made structures in urban area and more easily extracted than the point features. Based on such a selection strategy, a simple and robust algorithm is proposed that is generally applicable for extracting such features. The algorithm includes methods that can be used to establish observation equations from similarity measurements of the extracted features. Then, several transformations are selected and used to adjust the strips. Following the transformation, the fitness of linear features is tested to determine whether the discrepancies have been resolved; the results are then evaluated statistically. The results demonstrate that the algorithm is effective in reducing the discrepancies between the strips.

Detecting conjugate-point pairs for map alignment between two polygon datasets

Abstract When the same objects in different datasets have different positions and shapes, map alignment is necessary to minimise these geometric inconsistencies for successful map integration. In this paper, we propose a method to detect conjugate-point pairs for aligning two polygon datasets by matching the contours of corresponding polygons. This method comprises three processes, including identification of the corresponding polygon pairs, shape approximation with virtual corner-vertices and detection of conjugate-point pairs with our modified vertex-attributed-string-matching (VASM) algorithm. We applied this method to two distinct spatial datasets; a cadastral map and a topographical map of the same urban area. Then, the performance of our method was assessed visually and statistically. Both evaluations showed satisfactory results.

A new method for matching objects in two different geospatial datasets based on the geographic context

Although several methods of handling object matching problems across different datasets have been developed, there is still a need to design new approaches to address the diverse matching applications. Such cases include those where the coordinate differences in datasets are significant, where the shapes of the same objects are dissimilar, or even where the shapes are too similar for different objects. This is especially true, as many large portals worldwide are opening their spatial databases to public access by providing an open application programming interface (API). With this understanding, we propose in this paper a new method for matching objects in different datasets based on geographic context similarity measures. The proposed method employs and combines a set of concepts such as buffer growing, Voronoi diagrams, triangulation, and geometric measurements. This approach is simple in its algorithm but powerful in resolving situations when two datasets have significant coordinate discrepancies. In addition, the concept is highly effective regardless of the shapes of objects. After testing the method for the two major digital datasets in Korea, we found that the matching success rate reached 99.4%.

Hybrid line simplification for cartographic generalization

The performance of a line simplification algorithm can be different depending on the shape characteristics of the line data. Methodologies for segmenting a line feature into homogeneous sections and simplifying the segments with a proper algorithm are of great importance in linear generalization. This study proposes a methodology for segmenting and simplifying linear features based on the quantitative characteristics of a line. We analyzed the performance of existing simplification algorithms based on the geometrical attributes of line shapes. The analyzed data was used as a criterion for segmenting line data and selecting simplification algorithms appropriate for each segment. Then, we implemented segmentation and hybrid line simplification on topographic map data with a 1:1000 scale. To evaluate the performance of this methodology, visual and statistical assessments were implemented. As a result, this hybrid approach preserves more of the shape characteristics and produces less positional errors than the individual application of existing algorithms.

Development of a hashing-based data structure for the fast retrieval of 3D terrestrial laser scanned data

The volume of point cloud data obtained by 3-dimensional terrestrial laser scanners has grown very large as a result of scanner enhancements and application extensions. Quick point querying is therefore essential for efficient point cloud processing, and several data structures are applicable for that purpose. Octree, for example, is utilized in similar approaches and is considered a good candidate. This paper introduces hashing-based virtual grid (HVG), both as a competitor for octree and an improvement on the 3-dimensional virtual grid (3DVG). Whereas 3DVG is defined as a 3-dimensional array, HVG substitutes hashes for 3DVGs vertical indices. The performance of HVG was evaluated against those of octree and 3DVG by a point-querying operation. The selected operation finds neighboring points residing within a given radius for every individual point in the point cloud. HVG proved its balancing aspects throughout the operation, showing reasonable performance and memory efficiency. 3DVG, while its performance was excellent, required a significantly larger amount of memory. In summary, HVG is a suitable alternative to octree, and is expected to be effectively utilized as a base data structure for any application dealing with a massive amount of 3-dimensional point cloud data.

Bundle Block Adjustment with 3D Natural Cubic Splines.

Point-based methods undertaken by experienced human operators are very effective for traditional photogrammetric activities, but they are not appropriate in the autonomous environment of digital photogrammetry. To develop more reliable and accurate techniques, higher level objects with linear features accommodating elements other than points are alternatively adopted for aerial triangulation. Even though recent advanced algorithms provide accurate and reliable linear feature extraction, the use of such features that can consist of complex curve forms is more difficult than extracting a discrete set of points. Control points that are the initial input data, and break points that are end points of segmented curves, are readily obtained. Employment of high level features increases the feasibility of using geometric information and provides access to appropriate analytical solutions for advanced computer technology.

Machine Learning Classification of Buildings for Map Generalization

A critical problem in mapping data is the frequent updating of large data sets. To solve this problem, the updating of small-scale data based on large-scale data is very effective. Various map generalization techniques, such as simplification, displacement, typification, elimination, and aggregation, must therefore be applied. In this study, we focused on the elimination and aggregation of the building layer, for which each building in a large scale was classified as “0-eliminated,” “1-retained,” or “2-aggregated.” Machine-learning classification algorithms were then used for classifying the buildings. The data of 1:1000 scale and 1:25,000 scale digital maps obtained from the National Geographic Information Institute were used. We applied to these data various machine-learning classification algorithms, including naive Bayes (NB), decision tree (DT), k-nearest neighbor (k-NN), and support vector machine (SVM). The overall accuracies of each algorithm were satisfactory: DT, 88.96%; k-NN, 88.27%; SVM, 87.57%; and NB, 79.50%. Although elimination is a direct part of the proposed process, generalization operations, such as simplification and aggregation of polygons, must still be performed for buildings classified as retained and aggregated. Thus, these algorithms can be used for building classification and can serve as preparatory steps for building generalization.

An Improved Map-Matching Technique Based on the Fréchet Distance Approach for Pedestrian Navigation Services.

Wearable and smartphone technology innovations have propelled the growth of Pedestrian Navigation Services (PNS). PNS need a map-matching process to project a user’s locations onto maps. Many map-matching techniques have been developed for vehicle navigation services. These techniques are inappropriate for PNS because pedestrians move, stop, and turn in different ways compared to vehicles. In addition, the base map data for pedestrians are more complicated than for vehicles. This article proposes a new map-matching method for locating Global Positioning System (GPS) trajectories of pedestrians onto road network datasets. The theory underlying this approach is based on the Fréchet distance, one of the measures of geometric similarity between two curves. The Fréchet distance approach can provide reasonable matching results because two linear trajectories are parameterized with the time variable. Then we improved the method to be adaptive to the positional error of the GPS signal. We used an adaptation coefficient to adjust the search range for every input signal, based on the assumption of auto-correlation between consecutive GPS points. To reduce errors in matching, the reliability index was evaluated in real time for each match. To test the proposed map-matching method, we applied it to GPS trajectories of pedestrians and the road network data. We then assessed the performance by comparing the results with reference datasets. Our proposed method performed better with test data when compared to a conventional map-matching technique for vehicles.