MATLAB Tools for LIDAR Data Conversion, Visualization, and Processing

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1 MATLAB Tools for LIDAR Data Conversion, Visualization, and Processing Xiao Wang a, Kaijing Zhou a, Jie Yang a, Yilong Lu *a a Nanyang Technological University, 50 Nanyang Avenue, Singapore ABSTRACT LIDAR (LIght Detection and Ranging) [1] is an optical remote sensing technology that has gained increasing acceptance for topographic mapping. LIDAR technology has higher accuracy than RADAR and has wide applications. The relevant commercial market for LIDAR has developed greatly in the last few years. LAS format is approved to be the standard data format for interchanging LIDAR data among different software developers, manufacturers and end users. LAS data format reduces the data size compared to ASCII data format. However, LAS data file can only be visualized by some expensive commercial software. There are some free tools available, but they are not user-friendly and have less or poor visualization functionality. This makes it difficult for researchers to investigate and use LIDAR data. Therefore, there is a need to develop an efficient and low cost LIDAR data toolbox. For this purpose we have developed a free and efficient Matlab tool for LIDAR data conversion, visualization and processing. Keywords: LIDAR, LIDAR data conversion, LIDAR data visualization, LIDAR data processing 1. INTRODUCTION LIDAR is the short term for LIght Detection and Ranging, and an optical remote sensing technology that measures properties of scattered light to find the range to and/or other information of a distant target. LIDAR data is often called cloud data due to its irregular pattern as a result of aircraft motion and variations of targets. Although the ASCII format has been widely accepted and used, it has brought some difficulties in data transfer from one system or process flow to another as file sizes can be extremely large, even for small amount of data. Thus, LAS data has reduced data size compared to ASCII data. However, LAS data file can only be visualized with some expensive commercial software. LIDAR technology has been widely accepted and applied in archaeology, geography, geology, geomorphology, remote sensing, atmospheric physics, and transportation because of higher accuracy than conventional RADAR. Thus, the commercial market for LIDAR data processing has developed rapidly in these years. However, there is still a lack of simple, user-friendly and low cost software for raw LIDAR data conversion, visualization and processing. In this situation, this paper aims to develop a functional toolbox for LIDAR data conversion, visualization and processing. The tool is developed on the platform of Matlab due to its excellent graphic performance and high popularity among scholars and students [2]. In this paper, tools for data conversion and 2D direct mapping is presented first, followed by the discussion on the tools for interpolation methods, and digital elevation model (DEM) generation and processing. Besides, morphological filter, which is able to generate local digital terrain model (DTM), is also necessary for GIS related analysis and to fulfill more application requirements. However, when only certain range of height information of objects is needed, Digital Surface Model (DSM) is less accurate to visualize. In this situation, the toolbox should also include an elevation filter to intercept objects with a certain height range. 2. DATA CONVERSION AND DIRECT MAPPING TOOL Due to the fact that LAS data are not human readable and need expensive commercial software to process, developing user-friendly and high performance tools to convert LAS data file into human readable ASCII file is essential for scientists and engineers who are engaged in LIDAR research and applications. * eylu@ntu.edu.sg; phone ; fax ; ntu.edu.sg International Symposium on Lidar and Radar Mapping 2011: Technologies and Applications, edited by Xiufeng He, Jia Xu, Vagner G. Ferreira, Proc. of SPIE Vol. 8286, 82860M 2011 SPIE CCC code: X/11/$18 doi: / Proc. of SPIE Vol M-1

2 A converter is developed in this paper named LAS2TXT under MATLAB environment. The output of the converter is data files supported by many free plotting software. After that, it comes out with 2D mapping from TXT data using a fast direct mapping method other than Delaunay Triangulation and Mesh. A user-friendly Graphical User Interface (GUI) is finally designed to integrate these functions. 2.1 LAS to TXT conversion With LAS2TXT, users are able to view different data versions available in the market as well as a number of total points easily. In addition to the X Y Z data, more information, like intensity and RGB is contained in the text file. 2.2 Direct 2D mapping Due to the fact that LIDAR data are highly dense point cloud, it is close enough to form a surface in MATLAB plotting in 2D mapping. Thus only the X and Y values are plotted with colors indicating height (the Z value). As a result, different colors can help users with a visual impact of the topology in the area, and feature extraction can be carried out from the 2D plotting by edge detection, corner detection or ridge detection. 2.3 LAS-BOX The central control GUI developed is named LAS-BOX, which generally provides users with immediate, visual feedback about the effect of each action. It has advantages of making computer operation more intuitive and easier to learn and use over available commercial LIDAR software. Graphical views of the GUI and converter are shown in Fig. 1. Fig. 1. (a) LAS-BOX (b) LAS2TXT Converter 2.4 Singapore example A conversion and direct 2D mapping example, as shown in Fig.2, using LAS-BOX is demonstrated in this section. The data used for conversion is with 475,937 points and file size 23,227 KB taken by DSO LIDAR system over an area in Singapore. Fig. 3 shows the LIDAR data mapping generated by the commercial software ArcGIS [3] and Fig. 4 is the Google Map [4] aerial photo of the same area for reference. Proc. of SPIE Vol M-2

3 Fig. 2. Dirrect LIDAR datta mapping of an a area in Singaapore after conv version using LAS-BOX L with the user specifi fied 64 heighht levels/color. F 3. LIDAR data Fig. d mapping of an area in Sin ngapore (generaated by ArcGIS)) Fig. 4. Gooogle Earth aeriial photo of the same area as inn Fig. 2 Proc. of SPIE Vol M-3

4 By comparing Fig.2 with Fig.4, it can be found that LAS-BOX can generate detailed maps without any distortion and with less processing time and lower complexity. A more desirable 3D visualization tool is to be presented in the next section after research on suitable and efficient interpolation methods for generating digital elevation model from LIDAT cloud data. 3. INTERPOLATION METHODS Generation of the desired gridded DEM for 3D visualization and other data processing from LIDAR irregular cloud data can be a CPU intensive task. In order to improve computing efficiency, various interpolation methods are investigated and compared with the criteria of speed as well as accuracy. Interpolation is generally defined as process of estimating values of a specific attribute at un-sampled locations based on the values of attribute at the sampled locations [5]. Two different interpolation methods are compared. 3.1 The Natural neighbor interpolation The value of an un-sampled point is determined through a weighted average of the values of the interpolation point neighbor within the sample set [6]. The interpolated value of point v is calculated by: neighbor of v areastolen α area of the Voronoi cell of v (1) Fig. 5 shows the view of the 3D DEM using same LIDAR data in Section 2. The time spent to generate this plot is seconds, which is much faster than the other interpolation method. (a) (b) Fig. 5. 3D DEM generated by the Natural Neighbor Interpolation: (a) Top view; (b) Perspective view 3.2 The Triangulation based linear interpolation This method creates a triangular irregular network (TIN) [5] structure from the LIDAR points using Delaunay Triangulation. The irregular-spaced data points are converted into regular-spaced gridded dataset first. Then the original data points are so connected that no triangle edges are intersected by other triangles, following which a sequential search is carried out. This method is an exact interpolator since heights of data are preserved [7]. The result is shown in Fig. 6 with no visible difference compared with that using the Natural Neighbor Interpolation seconds is taken to run the algorithm. Proc. of SPIE Vol M-4

5 (a) (b) Fig. 6. 3D DEM generated by Linear Interpolation (a) Top view (b) Perspective view 3.3 Comparison of two interpolation methods The natural neighbor interpolation method is a weighted average of the neighboring observations using the weights determined by Voronoi polygon concept. While, the triangulation based linear interpolation method is based on the values at vertices of triangles in Delaunay triangulation network and the nearest neighbor interpolation selects the grid value of the nearest neighbor data points. From figures shown above, it can be concluded that the natural neighbor interpolation is primarily used for dense and irregular spatial data, which has a number of attractive features, such that it is a very accurate implementation method. Moreover, the implementation of the natural neighbor does not provide extrapolation. On the other hand, the linear interpolation is more suitable for medium to large data set to generate acceptable results. All these two methods are exact interpolation and the data processing speed is fast. In one word, the natural interpolation method provides a more robust and accurate results with faster data processing time. 4. FILTERING ALGORITHM In some cases, single visualization function may not satisfy the demand for further analysis of data. Thus, morphological filter and elevation filter are developed in this paper in order to plot local morphology and extract user-specified range of height information. 4.1 Morphological filter Morphological filter is used to extract urban features from LIDAR surface model [8] and then generates digital terrain model for a number of GIS related analysis and visualization. To perform morphological filter, two fundamental morphology operations are employed: dilation and erosion. The dilation of elevation z at x is defined as: d p = max( Z p [ x p, y p belong to ω] (2),where points ( x p, y p, z p ) represents p s neighbors with in a window ω, which can be a one dimensional line or twodimensional rectangle or other shapes. Erosion is defined as: d p = min( Z p Proc. of SPIE Vol M-5 (3)

6 The first step of morphological filter is to identify ground and non-ground features, followed by removing the non-ground objects. This is achieved by performing opening operation, which is to perform erosion of the data set followed by dilation. Opening operation is able to preserve features larger than the window size. Fig. 7 shows the performance of the opening operation. Fig. 7. Demonstration of the opening operation [9] As Fig. 7 demonstrates, erosion operation removed tree objects of sizes smaller than the window size, while dilation restored the shapes of large building objects. It is shown that morphological filter can remove the measurements of buildings and trees from LIDAR data, but it is difficult to detect all non-ground objects of various sizes using a fixed filtering window size. This problem can be solved by increasing the window size of morphological filter gradually. To reduce the number of iterations and increase efficiency, the window size can be increased exponentially: k k W = 2b + 1 (4) where k is the number of iterations. The k value need to be carefully adjusted, for a large k will results in too much operating time, while a small k will cause poor visualization outcome. The data tested here is the same as in Fig. 5 and is shown in Fig. 8, where k=5. (a) Fig. 8. 3D view of the morphology generated by Morphological Filter (a)top view (b)perspective view 4.2 Elevation filter Elevation filter is used to obtain more detailed elevation information from LIDAR data. It generates graphs with only interested elevation range and filter out unwanted height range. (b) Proc. of SPIE Vol M-6

7 To be clear, the elevation discussed in this section is the elevation relative to the local morphology. This is because absolute elevation value is only valid in a flat urban area, while it is meaningless for visualization and analysis in wild areas, where local morphology may varies significantly in a small area. Thus, the algorithm is written based on morphology filter. For a point p ( xp, yp, z p ), it has where z p MOR if zp else zp MOR z p ELE= z p MOR +filter range, +filter range; z p ELE= z p, (5) is p s morphological elevation and z p is its processed elevation generated by elevation filter. ELE The data used is the same as in Fig. 5 and the result with 10m elevation scale is shown in Fig. 9: (a) Fig. 9. 3D view of the 10m scale elevation model (a) Top view (b) Perspective view (b) 5. CONCLUSION AND FUTURE WORK In this paper, a set of MATLAB tools is developed and presented for easy data conversion, direct 2D accurate mapping and 3D DEM generation/visualization of LIDAR raw data. It avoids high costs involving in LIDAR mapping process and the user friendly GUI helps users to plot LIDAR mapping with fewer difficulties in following instructions than commercial LIDAR software. Although there are still a lot of shortcomings remaining, the set of tools provides a good platform for providing essential details for feature extraction in many practical applications. The first part of the toolbox is LAS-BOX, including two functions: data conversion and 2D direct mapping. The data conversion tool is able to convert LAS data file into human readable ASCII data file, which is in TXT format. In terms of 2D direct mapping, the relative elevation is indicated by different colors. Thus, LAS-BOX is able to give the users a general idea about the raw LIDAR data quickly. 3D DEM can be generated by the methods of both the natural neighbor interpolation and the triangulation based interpolation. The both methods are exact interpolation methods, which are able to produce a surface that passes through all the data points. By running the algorithm of these two methods, it is proven that the both methods are efficient and with excellent visual effect. As the outcomes of the two methods are similar with insignificant variance, the natural neighbor interpolation is proven to be more efficient because of less time consuming. Based on the above algorithms, morphological filter is developed to generate DTM to fulfill more application requirements, such as local topography analysis, etc. It involves two basic morphology mathematical operations: erosion and dilation. The combination of these two operations generates opening operation, based on which, morphological filter is built so that the non-ground objects will be eliminated while preserves the ground features. Proc. of SPIE Vol M-7

8 Another function of this toolbox is elevation filter. When the information of objects in a certain range of relative height is needed, the objects within the height range can be intercepted using elevation filter since the DSM is not accurate to visualize in these cases. Elevation filter is built based on morphological filter with only height relative to the morphology is interested. In the future research, it is advised to investigate a human interactive filtering method to refine filtering results since a few commission and omission errors did occur during filtering. More potential applications should be developed in the future, for highly accurate and high-resolution LIDAR data have particular utility in topographic maps where terrain is generally flat and subtle elevation changes often have significant importance. ACKNOWLEDGEMENT The funding support from Nanyang Technological University under the Undergraduate Research Experience on Campus (URECA) programme is appreciated.. REFERENCES [1] LIDAR, NOAA Coastal Service Center, National Oceanic and Atmospheric Administration. Available: [2] MathWorks-MATLAB and Simulink for Technical Computing, [3] ArcGIS, [4] Google Map, [5] Ali, T. A., On the selection of an interpolation method for creating a terrain model from LiDAR data. Available: [6] Harman, C., Johns, M., Voronoi Natural Neighbors Interpolation. Available: [7] Gonçalves, G., Analysis of interpolation errors in urban digital surface models created from Lidar data, Proc. 7th Int. Symp. on Spatial Accuracy Assessment in Natural Resources and Environ. Sciences, (2006). [8] Prisetnall, G. Jaafar, J., Duncan, A., Extracting Urban features from LIDAR digital surface models, Computers, Environment and Urban Systems, 24(2), (2000). [9] Zhang, K, Chen, S.-C., Shyu, M.-L., Yan, J., and Zhang, C., A Progressice Morphological Filter for Removing Noground Measurements From Airborne LIDAR Data, IEEE Trans. on Geoscience and Remote Sensing, 41(4) (2003). Proc. of SPIE Vol M-8