Tuong Thuy Vu

LIDAR-based change detection of buildings in dense urban areas

An automatic method for LIDAR-based (Light Detection And Ranging) change detection is proposed. Highly dense LIDAR point clouds are recommended as the most suitable gathered data for dense urban areas. The main goal is to develop an up-to-date building inventory database, which is in great demand for the earthquake-prone areas like Japan, using LIDAR as primary data. Two LIDAR surveying flights in 1999 and 2004 provide the test data over Roppongi, Tokyo, Japan. Detected results are visual evaluation using orthophoto produced by LIDAR surveying flights. The highly automated processing proved the efficiency of using LIDAR for a quick and reliable updating. Moreover, it also implies the feasibility for detection of damaged buildings due to earthquake.

Context-based mapping of damaged buildings from high-resolution optical satellite images

In the early stages of post-disaster response, a quick and reliable damage assessment map is essential. As time is a critical factor, automated damage mapping from remotely sensed images is the expected solution to drastically reduce data acquisition and computation time. Recently, high-resolution satellite images, such as QuickBird data, have been in high demand by damage assessment analysts and disaster management practitioners. However, the existing automated mapping approaches hardly accommodate such high-resolution data. This research aims at developing a new context-based automated approach for earthquake damage mapping from high-resolution satellite images. Relevant contextual information (including structure, shape, size, edge texture, spatial relations) describing the damage situation is formulated and up-scaled on a morphological scale-space. Speed optimization is achieved by parallel processing implementation. The developed approach was tested with two QuickBird images acquired on 26 June 2005 and 3 June 2008 over YingXiu town, Sichuan, China, which suffered the devastating 12 May 2008 earthquake. In comparison to the reference, the developed mapping approach could achieve over 80% accuracy for computation of the damage ratio. Future research is planned to test the approach on various disaster cases for both optical and radar images using a grid-computing platform towards a cost-effective damage mapping solution.

Automated Vehicle Extraction and Speed Determination From QuickBird Satellite Images

A new method has been developed to automatically extract moving vehicles and subsequently determine their speeds from a pair of QuickBird (QB) panchromatic (PAN) and multispectral (MS) images. Since the PAN and MS sensors of QB have a slight time lag (approximately 0.2 s), the speed of a moving vehicle can be determined from the difference in the positions of the vehicle observed in the PAN and MS images due to the time lag. An object-based approach can be used to extract a vehicle from the PAN image, which has a resolution of 0.6 m. However, it is difficult to accurately extract the position of a vehicle from an MS image because its resolution is 2.4 m. Thus, an area correlation method is proposed to determine the location of a vehicle from an MS image at a sub-pixel level. The speed of the moving vehicle can then be calculated by using the vehicle extraction results. This approach was tested on several parts of a QB image covering central Tokyo, Japan, and the accuracy of the results is demonstrated in this study.

Detection and Animation of Damage Using Very High-Resolution Satellite Data Following the 2003 Bam, Iran, Earthquake

The focus of this study was to thoroughly exploit the capability of very high-resolution (VHR) satellite imagery such as Ikonos and QuickBird for disaster mitigation. An efficient automated methodology that detects damage was implemented to derive the rich information available from VHR satellite imagery. Consequently, the detected results and the VHR satellite imagery are attractively presented through a fly-over animation and visualization. The aim is to assist the field-based damage estimation and to strengthen public awareness. The available Ikonos and QuickBird data captured after the Bam, Iran, earthquake in December 2003 was employed to demonstrate the competence of the automated detection algorithm and fly-over animation/visualization. These results are consistent with the field-based damage results.

Toward an Optimal Algorithm for LiDAR Waveform Decomposition

This letter introduces a new approach for light detection and ranging (LiDAR) waveform decomposition. First, inflection points are identified by the Ramer-Douglas-Peucker curve-fitting algorithm, and each inflection point has a corresponding baseline during curve fitting. Second, according to the spatial relation between the baseline and the inflection point, peaks are selected from the inflection points. The distance between each peak and its baseline and the maximum number of peaks are employed as a criterion to select a “significant” peak. Initial parameters such as width and boundaries of peaks provide restraints for the decomposition; right and left boundaries are estimated via a conditional search. Each peak is fitted by a Gaussian function separately, and other parts of the waveform are fitted as line segments. Experiments are implemented on waveforms acquired by both small-footprint LiDAR system LMS-Q560 and large-footprint LiDAR system Laser Vegetation Imaging Sensor. The results indicate that the algorithm could provide an optimal solution for LiDAR waveform decomposition.

Filtering Airborne Laser Scanner Data: A Wavelet-Based Clustering Method

Filtering the airborne laser scanner data is challenging due to the complex distribution of objects on Earth’s surface and it is still in development stage. This problem has been investigated so far with varieties of algorithms, but they suffer from different magnitudes of drawbacks. This study proposed a new and improved hybrid method based on multi-resolution analysis. Wavelet was adopted in this multi-resolution clustering approach. It enabled the classification of objects based on their size and the efficiency to filter out unwanted information at a specific resolution, and the proposed algorithm is named the ALSwave (Airborne Laser Scanner Wavelet) method. ALSwave has been tested on two data sets acquired over the urban areas of Tokyo, Japan and Stuttgart, Germany. The results showed a well-filtered, bare earth surface coupled with acceptable computational time. The accuracy assessment was carried out by comparison between the filtered bare earth surface by ALSwave and the manually filtered surface. The Root Mean Square Error (RMSE) follows a linear relationship with respect to terrain slope. This wavelet-based approach has opened a new way to filter the raw laser data that subsequently generates fast and more accurate digital terrain models.

Synergistic application of geometric and radiometric features of LiDAR data for urban land cover mapping.

Urban land cover map is essential for urban planning, environmental studies and management. This paper aims to demonstrate the potential of geometric and radiometric features derived from LiDAR waveform and point cloud data in urban land cover mapping with both parametric and non-parametric classification algorithms. Small footprint LiDAR waveform data acquired by RIEGL LMS-Q560 in Zhangye city, China is used in this study. A LiDAR processing chain is applied to perform waveform decomposition, range determination and radiometric characterization. With the synergic utilization of geometric and radiometric features derived from LiDAR data, urban land cover classification is then conducted using the Maximum Likelihood Classification (MLC), Support Vector Machines (SVM) and random forest algorithms. The results suggest that the random forest classifier achieved the most accurate result with overall classification accuracy of 91.82% and the kappa coefficient of 0.88. The overall accuracies of MLC and SVM are 84.02, and 88.48, respectively. The study suggest that the synergic utilization of geometric and radiometric features derived from LiDAR data can be efficiently used for urban land cover mapping, the non-parametric random forest classifier is a promising approach for the various features with different physical meanings.

Range determination for generating point clouds from airborne small footprint LiDAR waveforms

This paper presents a range determination approach for generating point clouds from small footprint LiDAR waveforms. Waveform deformation over complex terrain area is simulated using convolution. Drift of the peak center position is analyzed to identify the first echo returned by the illuminated objects in the LiDAR footprint. An approximate start point of peak in the waveform is estimated and adopted as the indicator of range calculation; range correction method is proposed to correct pulse widening over complex terrain surface. The experiment was carried out on small footprint LiDAR waveform data acquired by RIEGL LMS-Q560. The results suggest that the proposed approach generates more points than standard commercial products; based on field measurements, a comparative analysis between the point clouds generated by the proposed approach and the commercial software GeocodeWF indicates that: 1). the proposed approach obtained more accurate tree heights; 2). smooth surface can be achieved with low standard deviation. In summary, the proposed approach provides a satisfactory solution for range determination in estimating 3D coordinate values of point clouds, especially for correcting range information of waveforms containing deformed peaks.

Characterizing Radiometric Attributes of Point Cloud Using a Normalized Reflective Factor Derived From Small Footprint LiDAR Waveform

This paper presents a reflectance-like coefficient, normalized reflective factor (NRF) to characterize the radiometric attributes of point cloud generated from small footprint light detection and ranging (LiDAR) waveform data. The NRF is defined as a normalized ratio between the energy of emitted laser beam and the peak in return waveform in conjunction with the atmospheric attenuation and observation geometry. Based on the Gaussian parameters of the emitted and return waveforms, NRF is calculated with an empirical atmospheric model and user-defined standard observation geometry. To correct the radiometric measurement of point cloud in multipeak waveform, a semi-physical-based method is adopted to enhance the NRF of point cloud generated from multipeak waveform. Experiments are conducted with small footprint LiDAR waveform data acquired by RIEGL LMS-Q560. A curve-fitting-based approach is applied to decompose LiDAR waveform into three-dimensional (3-D) coordinates of point cloud, and the NRF are calculated using the Gaussian parameters of both emitted and return waveforms. The visualization of the radiometric attributes of point cloud data is carried out over the overlapping areas between different flight strips, it suggests that the NRF over overlapping area is much smooth than the normalized intensity. Quantitative comparison with Hyperion data indicates that the NRF has much higher correlation with surface reflectance than the normalized intensity data. Standard deviations of NRF and the normalized intensity of different land cover patches are analyzed to assess the homogeneity of the radiometric data. It is observed that NRF has less variability than the normalized intensity within the same land cover patches. Point cloud of two sample trees is also selected to assess the performance of the “sub-footprint” effect correction. It is observed that the proposed approach reduced the variability of radiometric attributes over tree canopies with increasing NRF values; which means the “sub-footprint” effect is mitigated. In summary, the proposed NRF can serve as a promising indicator to characterize radiometric attribute of LiDAR point cloud.

Wavelet-based extraction of building features from airborne laser scanner data

A new approach based on wavelet analysis to detect buildings in a dense urban area from airborne laser scanner data is presented in this paper. Without the spectral reflectance from the buildings, their detection from the laser cloud points is mainly based on the discrimination of the buildings elevation and its surroundings. This detection becomes more challenging in a dense urban area, which contains skyscrapers, interspersed with a myriad of low and small as well as large houses along with crowded outdoor human activities. Integration of the objects size and its elevation could mitigate the difficulty of detection. Wavelet analysis was proposed and adopted to build up the framework for size-based detection. The study focused on the detection of buildings and the generation of a three-dimensional (3D) building database. The extractable information from the aerial photographs is optional. The proposed approach was tested in Shinjuku-ku, Tokyo, Japan, and the result has successfully matched with the existing two-dimensional (2D) vector data. Wavelet-based multi-resolution has proved an appropriate approach in eliminating the unnecessary features surrounding buildings and in extracting the buildings.