| Summary: | 博士 === 國立成功大學 === 測量及空間資訊學系 === 103 === Compared with discrete LiDAR systems, state-of-the-art airborne waveform LiDAR systems provide richer information on illuminated surfaces. Waveform data contains both the spatial and physical information of the surfaces. The geospatial surfaces can be located by detecting the reflected laser signal stored in the waveform with the information of the laser travelling path. The process to detect the reflected signal is known as echo detection. The physical characteristics of surfaces such as the reflectance or surface roughness will deform the shape of the transmitting laser pulse resulting in different waveform features. Such features can be used for land cover classification. For waveform information extraction, the echoes are usually detected before the waveform features are extracted for further analysis. For echo detection, conventional discrete LiDAR systems often use an on-the-fly process to detect points. This process usually misdetects weak or overlapping echoes, thus resulting in poor geometry when the structure of a scanned area is complex, such as a forest area. This study proposes an echo detection approach based on wavelet transformation that is capable of detecting weak returns and resolving overlapping echoes. Simulated and real waveform datasets of a forest area were both used in this study. The simulated waveform data were utilized to compare the proposed detector with two other popular detectors, namely, zero crossing (ZC) and Gaussian decomposition (GD), in terms of their ability to deal with weak or overlapping echoes. The real waveform dataset were used to demonstrate the wavelet-based (WB) algorithm for exploring missing echoes. Experiments using simulated data showed that the WB and GD detectors are superior to the ZC detector in finding overlapping echoes. The WB algorithm performs well when dealing with overlapping echoes with low signal-to-noise ratio. The proposed WB algorithm was then applied to the real waveform dataset to test its effectiveness in detecting missing echoes. Results show that the WB algorithm can find more than 31.5% number of points than that of the used LiDAR system. An automatic filtering process was applied to the point clouds extracted from the waveform data to classify the ground points. This paper presents assessment methods based on the visual analyses of point classification and on the elevation difference of generated digital elevation models. Results show that the filtering accuracy and the accuracy of the digital elevation model are both improved because an enhanced geometry of the landscape can be obtained from the detected points. For land cover classification, features that can be derived from waveform data to describe land covers are divided into two categories, namely, echo-based and waveform-based features. Echo-based features have been widely used by previous studies to effectively classify land covers when the waveform has a single return. When the waveform contains multi-returns, echo-based features would fail to distinguish some land covers. Thus, waveform-based features are used and investigated in this study to complement the disadvantages of echo-based features. Experiments show that land cover classification can be improved with the integration of echo-based and waveform-based features.
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