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Generation of a Large-Scale Surface Sediment Classification Map Using Unmanned Aerial Vehicle (UAV) Data: A Case Study at the Hwang-do Tidal Flat, Korea
Tidal flats are associated with complicated depositional and ecological environments, and have changed considerably as a result of the erosion and sedimentation caused by tidal energy; consequently, the surface sediment distribution in tidal flats must be constantly monitored and mapped. Although several studies have been conducted with the aim of classifying intertidal surface sediments using various remote sensing methods combined with field survey, most of these studies were unable to consider various sediment types, due to the low spatial resolution of remotely sensed data. Therefore, previous studies were unable to efficiently describe precise surface sediment distribution maps. In the present study, unmanned aerial vehicle (UAV) red, green, blue (RGB) orthoimagery was used in combination with a field survey (232 samples) to produce a large-scale classification map for surface sediment distribution, in accordance with sedimentology standards, using an object-based method. The object-based method is an effective technique that can classify surface sediment distribution by analyzing its correlations with spectral reflectance, grain size, and tidal channels. Therefore, we distinguished six sediment types based on their spectral reflectance and sediment properties, such as grain composition and statistical parameters. The accuracy assessment of the surface sediment classification based on these six types indicated an overall accuracy of 72.8%, with a kappa coefficient of 0.62 and 5-m error range related to the Global Positioning System (GPS) device. We found that 11 samples were misclassified due to the effects of sun glint and cloud caused by the UAV system and shellfish beds, while 14 misclassified samples were influenced by surface water related to the elevation, tidal channels, and sediment properties. These results indicate that large-scale classification of surface sediment with high accuracy is possible using UAV RGB orthoimagery.
Journal Article
TOWARDS A SCALE DEPENDENT FRAMEWORK FOR CREATING VARIO-SCALE MAPS
Traditionally, the content for vario-scale maps has been created using a ‘one fits all’ approach equal for all scales. Initially only the delete/merge operation was used to create the vario-scale data using the importance and the compatibility functions defined at class level (and evaluated at instance level) to create the tGAP structure with planar partition as basis. In order to improve the generalization quality other operators and techniques have been added during the past years; e.g. simplify, collapse (change area to line representation), split, attractiveness regions and the introduction of the concept of linear network topology. However, the decision which operation to apply has been hard coded in our software, making it not very flexible. Further, we want to include awareness of the current scale when deciding what generalization operation to apply. For this purpose we propose the scale dependent framework (SDF), which at its core contains the encoding of the generalization knowledge in the SDF conceptual model. This SDF model covers the representation of scale dependent class importance, scale dependent class compatibility values, scale dependent attractiveness regions and last but not least specification of generalization operations that are scale and class dependent. By changing the settings in the SDF configuration and re-running the vario-scale generalization process, we can easily experiment in order to find best settings (for specific map user needs). In this paper we design the SDF conceptual model and explicitly motivate and define the scope of its expressiveness. We further present the improved scale dependent tGAP creation software and present initial results in the form of better created vario-scale map content.
Journal Article
Accurate 3D LiDAR SLAM System Based on Hash Multi-Scale Map and Bidirectional Matching Algorithm
Simultaneous localization and mapping (SLAM) is a hot research area that is widely required in many robotics applications. In SLAM technology, it is essential to explore an accurate and efficient map model to represent the environment and develop the corresponding data association methods needed to achieve reliable matching from measurements to maps. These two key elements impact the working stability of the SLAM system, especially in complex scenarios. However, previous literature has not fully addressed the problems of efficient mapping and accurate data association. In this article, we propose a novel hash multi-scale (H-MS) map to ensure query efficiency with accurate modeling. In the proposed map, the inserted map point will simultaneously participate in modeling voxels of different scales in a voxel group, enabling the map to represent objects of different scales in the environment effectively. Meanwhile, the root node of the voxel group is saved to a hash table for efficient access. Secondly, considering the one-to-many (1 ×103 order of magnitude) high computational data association problem caused by maintaining multi-scale voxel landmarks simultaneously in the H-MS map, we further propose a bidirectional matching algorithm (MSBM). This algorithm utilizes forward–reverse–forward projection to balance the efficiency and accuracy problem. The proposed H-MS map and MSBM algorithm are integrated into a completed LiDAR SLAM (HMS-SLAM) system. Finally, we validated the proposed map model, matching algorithm, and integrated system on the public KITTI dataset. The experimental results show that, compared with the ikd tree map, the H-MS map model has higher insertion and deletion efficiency, both having O(1) time complexity. The computational efficiency and accuracy of the MSBM algorithm are better than that of the small-scale priority matching algorithm, and the computing speed of the MSBM achieves 49 ms/time under a single CPU thread. In addition, the HMS-SLAM system built in this article has also reached excellent performance in terms of mapping accuracy and memory usage.
Journal Article
A Pathfinding Algorithm for Large-Scale Complex Terrain Environments in the Field
Pathfinding for autonomous vehicles in large-scale complex terrain environments is difficult when aiming to balance efficiency and quality. To solve the problem, this paper proposes Hierarchical Path-Finding A* based on Multi-Scale Rectangle, called RHA*, which achieves efficient pathfinding and high path quality for large-scale unequal-weighted maps. Firstly, the original map grid cells were aggregated into fixed-size clusters. Then, an abstract map was constructed by aggregating equal-weighted clusters into rectangular regions of different sizes and calculating the nodes and edges of the regions in advance. Finally, real-time pathfinding was performed based on the abstract map. The experiment showed that the computation time of real-time pathfinding was reduced by 96.64% compared to A* and 20.38% compared to HPA*. The total cost of the generated path deviated no more than 0.05% compared to A*. The deviation value is reduced by 99.2% compared to HPA*. The generated path can be used for autonomous vehicle traveling in off-road environments.
Journal Article
Generalizing Simultaneously to Support Smooth Zooming: Case Study of Merging Area Objects
When users zoom in or out on a digital map, the map should change correspondingly to present geographical information at proper levels. A way to help map users better keep track of their interested objects is to change the map smoothly instead of discretely switching between several levels of detail. This paper focuses on the problem of providing smooth merging of area objects. We propose to merge multiple areas simultaneously to share their animation durations. In this way, each merging operation can be prolonged, and it is visually smoother. We present a greedy algorithm to decide which areas should be merged at each step. The merging process is pre-computed and is recorded into a space-scale cube (SSC). When a user accesses our web map, the SSC is sent to the client side so that the map can be generated by slicing the SSC in the graphics processing unit (GPU). We also explain how to snap the zooming to valid states so that the zooming will not stop halfway of the merging operations. Our case study shows that it is visually smoother to merge simultaneously than to sequentially merge each pair of areas.
Journal Article
An efficient approach for texture smoothing by adaptive joint bilateral filtering
Image decomposition into its structure and texture components is widely used in various image processing and computer vision applications. It is challenging to extract the structure component from an image having intricate texture since it is difficult to extract the structure from the texture that shares similar color intensity or scale. The aim of this work is to smooth the texture component from the image without affecting the significant image structures and to serve the purpose a structure- aware adaptive joint bilateral texture filtering has been employed. Main contribution in this paper is the designing of the guidance image, used in joint bilateral filtering for texture smoothing. To obtain high efficiency by using the proposed method, authors designed a scale map, which provides the size of the spatial kernel at each pixel using the characteristics of the structure and texture components. The experimental section demonstrates the supremacy of the proposed method over the state-of-the-art methods.
Journal Article
Soil Salinization Map of the Ust’-Orda Buryat Okrug, Irkutsk Oblast
AbstractA series of soil salinization maps of the Ust’-Orda Buryat okrug of Irkutsk oblast on a scale of 1 : 300 000 was compiled for the first time on the basis of the GIS project in the ArcInfo environment. The maps enabled us to answer a number of questions about the distribution of saline and solonetzic soils and the chemical composition of salts. These maps were used to calculate the areas of salt-affected soils taking into account the depth and degree of salinization and the chemical composition of salts. The maps were compiled on the basis of a cross-analysis of 73 soil maps: 68 large-scale soil maps of farms on a scale of 1 : 25 000 for six districts of the okrug, two medium-scale soil maps of the okrug, and three small-scale soil maps, and two small-scale maps of soil salinization. The boundaries of mapping areas were delineated with due account for remote sensing data (RSD), topographic maps, and digital elevation model (SRTM). The compiled maps were verified using 136 georeferenced soil pits with analytical information on salinization. We identified 346 mapping areas containing different proportions of saline soils with a total area of 364 300 ha. Slightly saline soils sulfate salinization in the surface layer predominated. Sulfate salinization with gypsum and sulfate salinization with toxic alkalinity determined by magnesium bicarbonate were identified on the maps for the first time. Alkalinity associated with soda rarely occurs in soils with predominating alkaline sulfate–chloride salinization. Predominantly chloride salinization is also not widespread. Soils saline in the upper 1-m-thick layer occupy 45 200 ± 17 300 ha (slightly saline) and 17 800 ± 7900 ha (moderately and strongly saline), and the area of solonchaks is 4700 ± 2500 ha. The area of solonetzes is 2800 ± 2200 ha, and solonetzic soils occupy 25 100 ± 12 500 ha.
Journal Article
Quantitative Relations between Morphostructural Similarity Degree and Map Scale Change in Contour Clusters in Multi-Scale Map Space
This paper aims to propose a new approach to calculate the quantitative relations between morphostructural similarity degree and map scale change in multi-scale contour clusters for automatic contour generalization. Terrain lines were extracted by pre-processing of unclosed contour lines, and an indirect quantitative expression method of morphostructural similarity relation was proposed based on terrain line hierarchical trees. Thirteen groups of multi-scale contour clusters with different drainage areas of loess geomorphy were employed to explore the changing regularity of morphostructural similarity indices with map scale. Finally, the quantitative relations between morphostructural similarity degree and map scale change were calculated using 52 groups of points. The results show that power function is the best fit among the candidate functions, and the quantitative relations between the morphostructural similarity degree and map scale change can be expressed using the same power function, which facilitates the automation of contour generalization.
Journal Article
Single shot object detection with refined feature
Object classification and localization are two significant aspects of object detector based on the Single Shot MultiBox Detector (SSD). In general, the more feature maps there are, the better the object classification performance will be. However, when the information of excessive feature maps are sparse and unnecessary, the performance of object detection is slightly improved or maybe precisely opposite, which is instead harmful to the production of object localization. The performance of object detectors is not only related to the number of feature maps but also relies partly on the bounding box regression and Non-Maximum Suppression (NMS). In this paper, a detector is constructed based on SSD, called Detection with Refined Feature (DRF), involving center map and scale map, the detection loss is reshaped. Our motivation is to improve the accuracy of classification and localization by searching for central points and predicting the scales of the object points. Center map is used to predict the Intersection over Union (IoU) between the prediction box and ground truth box, while scale map considers the relationships among the different scales. Experimental results on both Pascal VOC and MS COCO 2014 instance datasets demonstrate the effectiveness of DRF. Using Darknet53, we achieve an 86.4% mean Average Precision (mAP) on Pascal VOC2007 and an 87.4% mAP on Pascal VOC2007 and VOC2012. On MS COCO, the DRF with ResNet50 still achieves moderate improvement.
Journal Article