Browsing by Author "Teo, Tee-Ann"

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AN INTRODUCTION OF SURVEYING AND GEOMATICS EDUCATION WITH E-PLATFORM IN NCTU, TAIWAN
(2011-01-01)
Teo, Tee-Ann
;
Shih, Peter Tian-Yuan
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土木工程學系
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Department of Civil Engineering
This article presents the current status of Surveying and Geomatics education provided by Department of Civil Engineering at National Chiao Tung University, Taiwan. The Surveying and Geomatics Education at NCTU is introduced first. Then the current status of using E-learning platform for Surveying and Geomatics courses is described. This platform, also known as E-Campus, is designed and implemented by NCTU Digital Content Production Center. This paper also shows some statistical numbers of Surveying and Geomatics courses using E-Campus. The practical results indicated that the average login for undergraduate student is from 38 to 60 times per student for each course while the average login for graduate student is from 69 to 105.
Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System
(2017-07-01)
Teo, Tee-Ann
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Wu, Hsien-Ming
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土木工程學系
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Department of Civil Engineering
The airborne multi-wavelength light detection and ranging (LiDAR) system measures different wavelengths simultaneously and usually includes two or more active channels in infrared and green to acquire both topographic and hydrographic information. The reflected multi-wavelength energy can also be used to identify different land covers based on physical properties of materials. This study explored the benefits of multi-wavelength LiDAR in object-based land cover classification, focusing on three major issues: (1) the evaluation of single- and multi-wavelength LiDARs for land cover classification; (2) the performance of spectral and geometrical features extracted from multi-wavelength LiDAR; and (3) the comparison of the vegetation index derived from active multi-wavelength LiDAR and passive multispectral images. The three-wavelength test data were acquired by Optech Titan in green, near-infrared, and mid-infrared channels, and the reference data were acquired from Worldview-3 image. The experimental results show that the multi-wavelength LiDAR provided higher accuracy than single-wavelength LiDAR in land cover classification, with an overall accuracy improvement rate about 4-14 percentage points. The spectral features performed better compared to geometrical features for grass, road, and bare soil classes, and the overall accuracy improvement is about 29 percentage points. The results also demonstrated the vegetation indices from Worldview-3 and Optech Titan have similar characteristics, with correlations reaching 0.68 to 0.89. Overall, the multi-wavelength LiDAR system improves the accuracy of land cover classification because this system provides more spectral information than traditional single-wavelength LiDAR.
Automatic Co-Registration of Optical Satellite Images and Airborne Lidar Data Using Relative and Absolute Orientations
(2013-10-01)
Teo, Tee-Ann
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Huang, Shih-Han
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土木工程學系
;
Department of Civil Engineering
Co-registration of 2-D images and 3-D lidar points in a common area is an important task in data fusion as well as other applications. As the information acquired by image and lidar systems are not the same, the registration of heterogeneous sensors is a challenging issue. The objective of this study is to perform the co-registration of 2-D images and 3-D lidar points using relative and absolute orientations. The proposed method performs image matching between stereo images for relative orientation modeling and generates a matched 3-D surface model. Then, an automatic least squares 3-D surface matching is applied between matched 3-D surface model and lidar 3-D points. Finally, the precise object-to-image transformation and orthoimages can be generated via relative and absolute transformation. The test data include WorldView-2 image, QuickBird image and lidar data. The experimental results indicate that the relative orientation may reach subpixel accuracy while the absolute orientation may reach 1 pixel accuracy. Moreover, the geometric consistency between orthoimages is better than 0.5 m on the ground.
Bias Compensation in a Rigorous Sensor Model and Rational Function Model for High-Resolution Satellite Images
(2011-12-01)
Teo, Tee-Ann
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土木工程學系
;
Department of Civil Engineering
This paper presents three bias-compensated models for the geometric correction of high-resolution satellite images. The proposed models include the bias-compensated rigorous sensor model (RSM) in the orbital space, the bias-compensated RSM in the image space, and the bias-compensated rational function model (RFM) in the image space. The RSM and RSM use the on-board data and sensor-oriented rational polynomial coefficients (RPCs) provided in imagery metadata respectively. Test images include Quick Bird, World View-1, and World View-2 Basic images. Experimental results indicate that the bias-compensated RSM using the zero order polynomials function in the orbital space provides higher accuracy. A comparison of the bias-compensated RSM and RFM in the image space shows that these models behave similarly, and the Maximum difference in root-mean-square error is less than 0.1 m. These results show that all the proposed methods obtain accuracy of better than 1 pixel, except for the translation in the image space.
BIM-oriented indoor network model for indoor and outdoor combined route planning
(2016-08)
Teo, Tee-Ann
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Cho, Kuan-Hsun
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土木工程學系
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Department of Civil Engineering
Emergency response and pedestrian route planning rely highly on indoor-outdoor geospatial data and a network model of the data; however, indoor geospatial data collection is time-consuming. Several studies have used the architecture, engineering, and construction (AEC) model to generate indoor network models. These models are subject to the input data types, and the attributes of interior building objects are usually incomplete; hence, the integration of building information modeling (BIM) and geographic information systems (GIS) can benefit indoor-outdoor integrated applications. To achieve data interoperability, an open BIM standard called Industry Foundation Classes (IFC) is maintained by buildingSMART. In this study, we propose a multi-purpose geometric network model (MGNM) based on BIM and explore the strategy of indoor and outdoor network connections. To achieve the goals, the IFC-to-MGNM conversion includes the following: (1) extraction of building information from IFC, (2) isolation of the MGNM information from the aforementioned building information, and (3) build up the topological relationships of MGNM into GIS Geodatabase. In addition, an entrance-to-street strategy is proposed to connect indoor networks, entrances, and outdoor networks for detailed route planning. The experimental results indicate that the MGNM could be generated from BIM automatically and applied to connect indoor and outdoor features for the multi-purpose application. Two use-case scenarios were developed to validate the proposed methods. Compared to actual distance, the relative error was improved by 5.1% and 65.5% in the horizontal and vertical routes, respectively, over the conventional indoor network model from 2D ground plan. In addition, the computational time taken by the proposed coarse-to-fine route planning method was 25% that of the traditional single-scale route planning method. (C) 2016 Elsevier Ltd. All rights reserved.
Cloud component removal for shallow water depth retrieval with multi-spectral satellite imagery
(2019-08-01)
Tsou, Po-Yao
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Shih, Peter Tian-Yuan
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Teo, Tee-Ann
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土木工程學系
;
Department of Civil Engineering
Water depth and the topography under water provide important information for near shore human activities. With the intensification of international territory concerns, bathymetric mapping is also gaining attention. Optical satellite imagery is an efficient tool for estimating shallow water depth as compared to the traditional field surveying because of its wide coverage area. On the other hand, the existence of cloud and haze contaminates the spectral signatures, which introduces errors to the depth data retrieved. In this research, the contaminated pixels are treated as a mixture of water and cloud. Linear Spectral Unmixing (LSU) procedure is applied for estimating the cloud abundance in mixed pixels. The cloud component is then removed with a linear function and the "purified" water component used for depth retrieval. In this research, water depth is estimated with two methods, namely, artificial neural network (ANN) and physical model. The former demands in-situ bathymetric samples for training, the latter requires site information of inherent optical properties (IOPs) and apparent optical properties (AOPs). The experiments reveal that retrieving depth with ANN generates better results than the physical model, but with a few extremely large errors. As for mixed pixels, the error of depth estimation becomes higher as cloud abundance increases. The precision of depth retrieval is higher for mixed pixels at the reef flat level (within 10 m in depth) than those in the lagoon (about 20 m in depth), and the precision generally agrees with those retrieved from water pixels without cloud or haze.
DEEP-LEARNING FOR LOD1 BUILDING RECONSTRUCTION FROM AIRBORNE LIDAR DATA
(2019-01-01)
Teo, Tee-Ann
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土木工程學系
;
Department of Civil Engineering
A three-dimensional building model is an important geospatial information for a smart city. The objective of this study is to reconstruct OGC CityGML LOD1 prismatic building models from 3D lidar points automatically. A deep learning approach (i.e. Fully Convolutional Network, FCN) is developed to detect initial building regions for lidar data. After refinement, the building boundary needs regularization to reshape the irregular boundary into a regular building primitive. Finally, a 3D plane fitting is applied to shape the rooftop using lidar points inside building primitive. The test data was an urban area with the size of 1800m by 1200m. The lidar point density was 4 pt/m(2). The experimental result indicated that the proposed method automatically reconstruct the LOD1 block model from lidar data. The accuracy of building detection reached 72% using lidar object height and intensity. The reconstruction showed high similarity with reference LOD1 building model.
DEM-Aided Block Adjustment for Satellite Images With Weak Convergence Geometry
(2010-04-01)
Teo, Tee-Ann
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Chen, Liang-Chien
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Liu, Chien-Liang
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Tung, Yi-Chung
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Wu, Wan-Yu
To acquire the largest possible coverage for environmental monitoring, it is important in most situations that the overlapping areas and the convergent angles of respective satellite images be small. The traditional bundle adjustment method used in aerial photogrammetry may not be the most suitable for direct orientation modeling in situations characterized by weak convergence geometry. We propose and compare three block adjustment methods for the processing of satellite images using the digital elevation model (DEM) as the elevation control. The first of these methods is a revised traditional bundle adjustment approach. The second is based on the direct georeferencing approach. The third is a rational function model with sensor-oriented rational polynomial coefficients. A collocation technique is integrated into all three methods to improve the positioning accuracy. Experimental results indicate that using the DEM as an elevation control can significantly improve the geometric accuracy as well as the geometric discrepancies between images. This is the case for all three methods. Moreover, the geometric performance of the three methods is similar. There is a significant improvement in geometric consistency between overlapping SPOT images with respect to single image adjustment for steep areas.
Empirical Radiometric Normalization of Road Points from Terrestrial Mobile Lidar System
(2015-05-01)
Teo, Tee-Ann
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Yu, Hui-Lin
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土木工程學系
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Department of Civil Engineering
Lidar data provide both geometric and radiometric information. Radiometric information is influenced by sensor and target factors and should be calibrated to obtain consistent energy responses. The radiometric correction of airborne lidar system (ALS) converts the amplitude into a backscatter cross-section with physical meaning value by applying a model-driven approach. The radiometric correction of terrestrial mobile lidar system (MLS) is a challenging task because it does not completely follow the inverse square range function at near-range. This study proposed a radiometric normalization workflow for MLS using a data-driven approach. The scope of this study is to normalize amplitude of road points for road surface classification, assuming that road points from different scanners or strips should have similar responses in overlapped areas. The normalization parameters for range effect were obtained from crossroads. The experiment showed that the amplitude difference between scanners and strips decreased after radiometric normalization and improved the accuracy of road surface classification.
FEATURE-BASED IMAGE REGISTRATION OF ALOS PALSAR AND AVNIR-2 IMAGES
(2011)
Teo, Tee-Ann
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Chen, Shin-Yu
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土木工程學系
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Department of Civil Engineering
The integration of optical and radar sensors in a common area is an important task in data fusion as well as other applications. The objective of this study is to perform the feature-based registration of ALOS PALSAR and AVNIR-2 images. The proposed method iteratively minimizes the discrepancy between the feature edges in PALSAR and AVNIR-2 images. A coarse-to-fine strategy is also adopted by applying an image pyramid. The accuracy of feature-based matching has increased from low to high resolution. The test images are synchronous PALSAR and AVNIR-2 images in Japan. The experimental results indicate that one pixel accuracy can be achieved using feature-based matching.
Geometric Integration of Heterogeneous Models for Multisatellite Image Positioning
(2012-07-01)
Chen, Liang-Chien
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Chang, Wen-Chi
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Teo, Tee-Ann
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土木工程學系
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Department of Civil Engineering
With the wider availability of more and more images acquired from various satellites, the integration of different orientation models for different sensors has become an important task in order to maintain geometrical consistency. This study combines two heterogeneous geometric correction models, namely, direct georeferencing and the rational function model for multi-satellite image positioning. Two types of adjustment models, collocation-based block adjustment (CBBA) and collocation-aided block adjustment (CABA), are proposed to reach the goal. The former is based on the concept that the adjustment is first performed on individual images, and then, all images are integrated. Discrepancies are compensated for by least squares collocation (LSC). The latter method entails simultaneous adjustment of the two heterogeneous models followed by LSC. Experimental results indicate that the proposed method can significantly improve the geometric accuracy as well as reduce geometric discrepancies between images. We also demonstrate that CABA is slightly better than CBBA.
Geomorphological Change Detection Using an Integrated Method: A Case Study on the Taan River Watershed, Taiwan
(2016-08-01)
Wu, Jyun-Yi
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Teo, Tee-Ann
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Shih, Peter Tian-Yuan
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土木工程學系
;
Department of Civil Engineering
The Chi-Chi earthquake of 1999 in Central Taiwan caused significant vertical uplift and ignited a dynamic fluvial erosion system in the Taan River. In order to understand the geomorphologic changes in the Taan River after the Chi-Chi earthquake, this study uses multi-temporal aerial photos to produce orthoimages and surface models to derive regional three dimensional movements from 2001 - 2009. The Particle Image Velocimetry (PIV) technique is applied to identify the horizontal movements from both orthoimages and digital surface models (DSMs). While PIV can provide estimates for the channel change direction and magnitude; vertical analysis such as river incision rate derivation could be performed using the height difference between multi-temporal surface models. The results from these two approaches are then integrated for river geomorphological interpretation. It is observed that the proposed scheme is capable of identifying three river evolution stages from the extracted movements in the Taan case. These stages are: years 2001 to 2003, 2004 to 2006, and 2007 to 2009, respectively. The total incision between 2001 and 2009 is about 10 m. This study demonstrates that both PIV and DSM subtraction are effective in river geomorphological change identification. The integration of these two approaches could provide more information when observing the evolution of river morphology.
Geomorphological Change Detection Using an Integrated Method: A Case Study on the Taan River Watershed, Taiwan
(2016-08)
Wu, Jyun-Yi
;
Teo, Tee-Ann
;
Shih, Peter Tian-Yuan
;
土木工程學系
;
Department of Civil Engineering
The Chi-Chi earthquake of 1999 in Central Taiwan caused significant vertical uplift and ignited a dynamic fluvial erosion system in the Taan River. In order to understand the geomorphologic changes in the Taan River after the Chi-Chi earthquake, this study uses multi-temporal aerial photos to produce orthoimages and surface models to derive regional three dimensional movements from 2001 - 2009. The Particle Image Velocimetry (PIV) technique is applied to identify the horizontal movements from both orthoimages and digital surface models (DSMs). While PIV can provide estimates for the channel change direction and magnitude; vertical analysis such as river incision rate derivation could be performed using the height difference between multi-temporal surface models. The results from these two approaches are then integrated for river geomorphological interpretation. It is observed that the proposed scheme is capable of identifying three river evolution stages from the extracted movements in the Taan case. These stages are: years 2001 to 2003, 2004 to 2006, and 2007 to 2009, respectively. The total incision between 2001 and 2009 is about 10 m. This study demonstrates that both PIV and DSM subtraction are effective in river geomorphological change identification. The integration of these two approaches could provide more information when observing the evolution of river morphology.
INTEGRATION OF IMAGE-DERIVED AND POS-DERIVED FEATURES FOR IMAGE BLUR DETECTION
(2016)
Teo, Tee-Ann
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Zhan, Kai-Zhi
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土木工程學系
;
Department of Civil Engineering
The image quality plays an important role for Unmanned Aerial Vehicle (UAV)\'s applications. The small fixed wings UAV is suffering from the image blur due to the crosswind and the turbulence. Position and Orientation System (POS), which provides the position and orientation information, is installed onto an UAV to enable acquisition of UAV trajectory. It can be used to calculate the positional and angular velocities when the camera shutter is open. This study proposes a POS-assisted method to detect the blur image. The major steps include feature extraction, blur image detection and verification. In feature extraction, this study extracts different features from images and POS. The image-derived features include mean and standard deviation of image gradient. For POS-derived features, we modify the traditional degree-of-linear-blur (b(linear)) method to degree-of-motion-blur (b(motion)) based on the collinear condition equations and POS parameters. Besides, POS parameters such as positional and angular velocities are also adopted as POS-derived features. In blur detection, this study uses Support Vector Machines (SVM) classifier and extracted features (i.e. image information, POS data, b(linear) and b(motion)) to separate blur and sharp UAV images. The experiment utilizes SenseFly eBee UAV system. The number of image is 129. In blur image detection, we use the proposed degree-of-motion-blur and other image features to classify the blur image and sharp images. The classification result shows that the overall accuracy using image features is only 56%. The integration of image-derived and POS-derived features have improved the overall accuracy from 56% to 76% in blur detection. Besides, this study indicates that the performance of the proposed degree-of-motion-blur is better than the traditional degree-of-linear-blur.
Lidar-based change detection and change-type determination in urban areas
(2013)
Teo, Tee-Ann
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Shih, Tian-Yuan
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土木工程學系
;
生物科技學系
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Department of Civil Engineering
Change detection of objects, such as buildings, is essential for map updating. Traditionally, detection is usually performed through spectral analysis of multi-temporal images. This article proposes a method that employs multi-temporal interpolated lidar data. The objective of this study is to perform change detection and change-type determination via geometric analysis. A shape difference map is generated between the digital surface models in two different time periods. The areas with small shape differences are treated as non-changed areas and are excluded from the segmentation. The object's properties are then applied to determine the change types. Experimental results demonstrate that the proposed scheme achieves accuracy as high as 80%. Most of the errors from this study occurred in small or vegetation areas.
LINE-BASED MULTI-IMAGE MATCHING FOR FACADE RECONSTRUCTION
(2012-01-01)
Teo, Tee-Ann
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Kao, Chung-Hsuan
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土木工程學系
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Department of Civil Engineering
This research integrates existing LOD 2 building models and multiple close-range images for facade structural lines extraction. The major works are orientation determination and multiple image matching. In the orientation determination, Speeded Up Robust Features (SURF) is applied to extract tie points automatically. Then, tie points and control points are combined for block adjustment. An object-based multi-images matching is proposed to extract the facade structural lines. The 2D lines in image space are extracted by Canny operator followed by Hough transform. The role of LOD 2 building models is to correct the tilt displacement of image from different views. The wall of LOD 2 model is also used to generate hypothesis planes for similarity measurement. Finally, average normalized cross correlation is calculated to obtain the best location in object space. The test images are acquired by a non-metric camera Nikon D2X. The total number of image is 33. The experimental results indicate that the accuracy of orientation determination is about 1 pixel from 2515 tie points and 4 control points. It also indicates that line-based matching is more flexible than point-based matching.
Line-based rational function model for high-resolution satellite imagery
(2013)
Teo, Tee-Ann
;
土木工程學系
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應用數學系
;
Department of Civil Engineering
;
Department of Applied Mathematics
The object-to-image transformation of high-resolution satellite images often involves a rational functional model (RFM). Traditionally, RFM uses point features to obtain the transformation coefficients. Since control lines offer greater flexibility than control points, this study proposes a new RFM approach based on linear features. The proposed methods include direct RFM and bias-compensated RFM using control lines. The former obtains the rational polynomial coefficients (RPCs) directly from control lines, whereas the latter uses sensor-orientated RPCs and control lines to determine compensated coefficients. The line-based RFMs include vector and parametric line representations. The experiments in this study analysed the effects of line number, orientation, and length using simulation and real data. The real data combined three-dimensional building models and high-resolution satellite images, such as IKONOS and QuickBird images. Experimental results show that the proposed algorithms can achieve pixel-level accuracy.
Multi-type change detection of building models by integrating spatial and spectral information
(2012)
Chen, Liang-Chien
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Huang, Chih-Yuan
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Teo, Tee-Ann
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土木工程學系
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Department of Civil Engineering
Detecting building changes followed by updates is preferable for efficient revisions to building models. Additionally, more change types can be detected with spatial information provided by building models for reducing land surveying work. Therefore, for efficient building of model revision and land surveys, this work applies a new multi-type change detection scheme with new light detection and ranging (LIDAR) point clouds, new aerial images and existing building models. By integrating the spatial information from LIDAR data and image-based spectral information, this work identifies changes to existing buildings and identifies newly built and changed buildings. To provide an initial value for further revisions, new building regions are generated from change detection results. Experimental results demonstrate that the proposed scheme has high accuracy for both change type determination and building region generation. To provide comprehensive observations, experimental results deemed unreliable are scrutinized.
Object-Based Land Cover Classification Using Airborne Lidar and Different Spectral Images
(2016-08-01)
Teo, Tee-Ann
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Huang, Chun-Hsuan
;
土木工程學系
;
Department of Civil Engineering
Both land cover spectral information and 3D surface information can be obtained efficiently via remote sensing technologies. Spectral images provide spectral features whereas lidar point clouds contain 3D spatial features. Therefore, the multi sensor data can be integrated to obtain useful information for different applications. This study integrates lidar with different spectral features for land cover classification. Because different spectral images have different characteristics, this study used hyperspectral images, 4- and 8-band WorldView-2 multispectral images, to distinguish different land covers. The major works include features selection, object-based classification, and evaluation. In features selection appropriate features were selected according to the land cover characteristics. Object-based classification was implemented using image segmentation and supervised classification. Finally, different combinations were evaluated using reference data to provide comprehensive analyses. We use ITRES CASI-1500 airborne hyperspectral images, WorldView-2 multispectral images and Optech ALTM Pegasus in this study. The experiment compared the results with and without data fusion. The importance of different spectral features is also discussed. In summary, different land covers with similar spectral features can be identified using lidar spatial features. Spectral image integration with lidar data may improve land cover classification accuracy.
Object-Based Land Cover Classification Using Airborne Lidar and Different Spectral Images
(2016-08)
Teo, Tee-Ann
;
Huang, Chun-Hsuan
;
土木工程學系
;
Department of Civil Engineering
Both land cover spectral information and 3D surface information can be obtained efficiently via remote sensing technologies. Spectral images provide spectral features whereas lidar point clouds contain 3D spatial features. Therefore, the multi sensor data can be integrated to obtain useful information for different applications. This study integrates lidar with different spectral features for land cover classification. Because different spectral images have different characteristics, this study used hyperspectral images, 4- and 8-band WorldView-2 multispectral images, to distinguish different land covers. The major works include features selection, object-based classification, and evaluation. In features selection appropriate features were selected according to the land cover characteristics. Object-based classification was implemented using image segmentation and supervised classification. Finally, different combinations were evaluated using reference data to provide comprehensive analyses. We use ITRES CASI-1500 airborne hyperspectral images, WorldView-2 multispectral images and Optech ALTM Pegasus in this study. The experiment compared the results with and without data fusion. The importance of different spectral features is also discussed. In summary, different land covers with similar spectral features can be identified using lidar spatial features. Spectral image integration with lidar data may improve land cover classification accuracy.