ENGLISH

21卷/1期

21卷/1期

華藝線上圖書館

Pages:

1-12

論文名稱

應用空載光達數值地形模型於基隆河之河流地形研究

Title

Using airborne LiDAR derived DEMs to analyze river morphology in Keelung stream

作者

陳柔妃, 詹瑜璋, 張國楨, 謝有忠

Author

Rou-Fei Chen, Yu-Chang Chan, Kuo-Jen Chang, Yu-Chung Hsieh

中文摘要

本研究利用空載雷射掃描技術(Airborne LiDAR)製作高精度數值地形模型,探討基隆河流域河階分佈及水系發育,結果顯示河川水系受控於地質及地形條件,主要支流發育在中游的八堵向斜,北側的集水區較南側平緩,對稱性的水系暗示集水區內並無差異性的地殼變動。基隆河自暖暖到侯硐之間,各次集水區之面積、形態差異較大,較無法以單一河流階地或構造整體抬升來解釋,而在暖暖附近之坡地地形具有崩塌特徵,導致古山崩造成河道的堰塞並影響次集水區發育。經野外勘查結果,判定本區可能坡面破壞形式以岩石傾翻(toppling)或楔型岩石崩落(wedge failure)型態存在。

Abstract

We organized and performed airborne LIDAR mapping in the Metropolitan Taipei area in order to produce high-resolution and high-precision digital elevation models for geological research. Applying the newly acquired LIDAR DEMs from the MOEA Central Geological Survey, we analyzed geologic and geomorphic features of the Keelung River drainage area. The studied features include topographic scarps and lineaments, river terraces, drainage basins, and landslide scarps. Based on our analysis of the acquired LIDAR DEMs, we arrived at the following findings and conclusions: 1) The river basin analysis based on the LIDAR DTM indicates that the development of the Keelung River was influenced by regional structural patterns and topographic slopes. Tectonic influences appear to be relatively small for the development of the Keelung River. 2) Along the Keelung River, the LIDAR DTM indicates anomalous terrace morphology and river drainage at the middle section of the river. We interpreted this area to be caused by paleo-landslides, which form a dammed lake in the river and subsequently developed the main terrace of the same level for large area.

關鍵字

空載雷射掃描技術、數值高程模型、構造地形、古山崩

Keywords

airborne LIDAR, digital elevation model, river geomorphology, Paleo-landslide

附件檔名

華芸線上圖書館

N / A

備註說明

N / A

Pages:

13-30

論文名稱

二次微分法於空載全波形光達之高斯波形擬合與地物分類

Title

Second Derivative for Gaussian Waveform Fitting and Land Cover Classification with Airborne Full-waveform LiDAR

作者

盧佑樺, 蔡富安, 賴哲儇

Author

Yu-Hua Lu, Fu-an Tsai, Jhe-Syuan Lai

中文摘要

全波形光達(Full Waveform LiDAR)可完整記錄每條雷射光束在不同時間回傳的反射能量,若能藉由這些記錄下來的波形衍生出額外資訊,可望增進地形三維重建及地物分類的成效。波形擬合(fitting)與特徵萃取(feature extraction)是處理和分析全波形光達資料的重要過程。本研究利用高斯函數擬合空載全波形光達資料之波形,並以二次微分法搜尋迭代計算之初始值。基於波形擬合成果,可以衍生出振幅(amplitude)、波寬(width)、背向散射截面(backscatter cross-section)等波形參數,且配合強度(intensity) 、正規化高程(normalized height)等傳統光達特徵與同步搭載之中像幅正射影像,作為地物分類之依據。另外,本研究亦比較簡易貝氏(naïve Bayesian)與隨機森林(random forests)等兩種分類器之成效。研究結果顯示,運用二次微分法決定高斯函數迭代計算之初始值,並搭配隨機森林分類器,能提供較佳的擬合及分類成果,且波形參數有助於植物類別的辨識。

Abstract

Full-waveform LiDAR is an emerging active data acquisition tool in many applications. In addition to accurate positions of point clouds, full-waveform LiDAR provides complete wave signals of returned laser ray, which can be subsequently used to derive important characteristics of the targets. The waveform parameter extraction and analysis are two important operations for full-waveform LiDAR applications. In this research, the Gaussian modeling function with second derivative method was utilized for waveform fitting. Features extracted from the waveform, including width, amplitude, and backscatter cross-section, in conjunction with traditional LiDAR features, such as normalized height and intensity, and greenness index from image were used as primary attributes for land cover classification. Two classifiers were used and compared in this study, including Naïve Bayes and Random Forests. Experimental results demonstrate that using the second derivative method can produce higher fitting success rate and better classification results. The land cover classification results indicate that full-waveform features are helpful for distinguishing different vegetation targets. In addition, the decision-tree-based Random Forests classifier is more suitable for land cover classification of LiDAR data used in this study.

關鍵字

全波形光達、波形擬合、地物分類、二次微分法、隨機森林分類器

Keywords

full-waveform LiDAR, waveform fitting, land cover classification, second derivative, random forests

附件檔名

華芸線上圖書館

N / A

備註說明

N / A

Pages:

31-42

論文名稱

多測站具GPS定位之地面光達資料之網形平差

Title

Multi-station Network Adjustment of Terrestrial LiDAR Data with GPS positioning

作者

簡廷宇, 曾義星

Author

Ting-Yu Chien, Yi-Hsing Tseng

中文摘要

地面光達可以快速獲得高精度且大量的三維點雲資訊,由於各掃瞄站點雲資料均參考於區域坐標系統,而每個掃瞄站的掃瞄範圍有限,往往需要結合多站點雲資料才能完整涵蓋掃描場景,某些應用還需要將點雲資料轉換到地面坐標系統(地理定位),傳統上至少要利用三個地面控制點進行坐標轉換,但使用地面控制點造成測量工作上的不便。本研究利用點特徵加入以e-GPS求得之觀測量進行多測站之聯合平差,評估是否可以不需要地面控制點進行地理定位,在我們實驗的兩個案例中,利用e-GPS觀測量和連結點觀測量進行多站聯合平差在E、N、h方向之精度皆小於10公分,透過實驗得知利用e-GPS觀測量進行多站聯合平差,可以在不使用地面控制點的情況下,達到地理定位之目的,其精度足夠做為應用。

Abstract

Terrestrial LiDAR can be used to acquire high accuracy 3D spatial data in rapid way. However, point clouds delivered by a terrestrial LiDAR are referenced to a local coordinate system defined by the laser scanner. With the limitation of viewing coverage of a single scanning station, combining point clouds acquired from multiple scanning stations is usually needed to completely cover the target landscape. For most applications, raw point clouds should be transformed into a global coordinate system (geo-referencing). The geo-referencing of terrestrial LiDAR data is currently relied on known control points. But using control points for geo-referencing is often troublesome and laborious. In this research, we perform network adjustment with GPS observations, and we focus on the problem that whether the role of control points in network adjustment can be substituted by GPS observation of scanning station or not. There are two cases in our experiments. The accuracy of the results are under 10 centimeters in E, N and h directions. By the validation of our experiments, we can know that it can perform network adjustment with GPS observations without control points. The accuracy are good enough for application.

關鍵字

地面光達、地理定位、GPS、點雲套疊

Keywords

terrestrial LiDAR, geo-referencing, GPS, point cloud registration

附件檔名

華芸線上圖書館

N / A

備註說明

N / A

Pages:

43-54

論文名稱

臺灣歷史航照影像糾正對位

Title

Rectification and Registration of Taiwan Historical Aerial Images

作者

江若慈, 賴柏溶, 曾義星

Author

Jo-Tzu Chiang, Po-Jung Lai, Yi-Hsing Tseng

中文摘要

航照像片是地表現象的忠實記錄,歷史航照影像直接地記錄過去某個時間點的地表現象,包括當時的土地利用、自然資源、道路及聚落等空間分布,能夠呈現過去人文社會、經濟活動與自然環境的變遷。臺灣主要的歷史航照包括美軍(1943~1952年)、空軍(1954~1976年)以及農林航空測量所(1976年至今)所拍攝累積的照片。要利用這些歷史航照影像,以萃取時空地理資訊,必須將這些影像糾正對位到共同的坐標系統上。本文以台江內海區域為例,將不同時期的空照影像糾正對位到現行的國家坐標系統(TWD97)。農林航空測量所的影像屬製圖應用的標準航測影像,可應用空中三角測量的方法進行幾何糾正並產製正射影像。然而美軍及空軍的航拍目的主要為軍事偵察,所使用的相機無明確的內方位資訊,影像的重疊也不符合航測的標準,無法以制式的空中三角測量法進行糾正對位。本研究嘗試以聯合解算方式,求得同時期所有重疊影像的仿射轉換參數,獲得糾正對位的資訊。此方法雖然不如空中三角平差嚴謹,但是可以在無相機內方位資訊的情形下,獲得整體的對位成果。實驗結果顯示,應用此方法於美軍空照影像,其定位內部精度可達6.5m,相對於以空三解算農林航空測量所影像的定位精度(2m),雖然較低,但此對位精度相信已經符合於時空地理資訊分析的需求,可推廣歷史航照的應用。

Abstract

Aerial images are faithful records of landscape at that time. Historical aerial images directly witness landscape phenomena at that time of the covered region, including land-use, natural source, roads and residential area, etc. The study of historical aerial images has proven an effective way to reveal some historical phenomena related to cultural, economic, and natural changes, which are linked to the spatial information in the past. Abundant historical aerial images have been acquired in Taiwan for a variety of missions. The earliest aerial images were taken by U.S. military during and after World War II (from 1943 to 1952). Later than that many images were acquired routinely by Taiwan Air Force (1954 to 1976), Aerial Survey Office (since 1976). For the purpose of retrieving spatial information from the historical images, image registration and rectification corresponding to the ground coordinates should be done in advance. In this study, we take Tai-jiang Inner Sea as an example to register and rectify temporal aerial images into nowadays national coordinate system (TWD97). Images acquired by Aerial Survey Office are standard aerial images which can be registered through a computation process of bundle adjustment. However, the main purposes of images taken by U.S. military and Taiwan Air Force were mainly applied for military reconnaissance. These images are lack of camera parameters and the overlapped of them are not standard. So they do not fit the process of bundle adjustment. In this study, we attempt to obtain the information of registration and rectification by solving affine transformation parameters from all overlapped images at the same period. Although this method are not as strict as aerial triangulation, it can work without camera parameters. The experimental results showed that the accuracy of images taken by U.S. military can up to 6.5 m by applying affine transformation. The accuracy was poor than images taken by Aerial Survey Office using aerial triangulation (2m). But we believe this can meet the analysis of temporal geographic information and promote the application of historical aerial images.

關鍵字

歷史航照影像、對位、空中三角測量、仿射轉換

Keywords

historical aerial images, registration, triangulation, affine transformation

附件檔名

華芸線上圖書館

N / A

備註說明

N / A

Pages:

55-64

論文名稱

仿生計算在水稻田影像判釋的差異性研究

Title

The Study of Paddy-rice Field Image Classification by Bionics Computation

作者

萬絢, 張士勳, 陳祐耕

Author

Shiuan Wan, Shih-Hsun Chang, Yu-Keng Chen

中文摘要

近年來科技日益進步,遙測技術也不斷的改良與精進,應用衛星影像於處理大範圍面積的地表判釋也越趨廣泛。因此,如何有效運用衛星影像提升地表判釋的精度,便成為當前遙測技術持續發展的重點之一。過去對於水稻田的判斷都是以實地探勘方式進行耕地坵塊圖數化與編修,這些過程通常需要大量人力、物力與時間,然而透過遙測影像與資料探勘的分類技術,可以避免現場探勘之困境,並獲得合理可靠的結果。本研究使用遙測影像為素材,並隨機選取400樣本點,利用模糊粒子群聚類演算法獲取判釋規則,進行地表類別之判釋,所得正確率結果為80.75%,在同樣設計的研究中,使用蜂群聚類演算法及模糊粒子群演算法進行比較,結果發現蜂群聚類演算法較佳,其正確率結果為89.5%,所以此方法對於水稻田的判釋有良好的成果。另外研究中加入紋理資訊與植生指標作為輔助判釋的資訊,結果顯示可以有效提升判釋率,對於建構一套有效率之水稻田分類器有相當的幫助。

Abstract

Paddy-rice is an important food source in Taiwan, and government devotes a lot of resource to estimate annual food production. In the past, the paddy-rice field area estimation is obtained by field survey, which is both time and man-power consuming and requires a much higher budget. Land cover classification through remote sensing imagery and data mining technique is a well-accepted and popular approach for crop production estimation, thanks to the advances of remote sensing technology in recent decades. It is thus an important research topic that how to develop a methodology or algorithm which can improve image classification accuracy rate. In the present study an aerial photo over the Tanzi county of Taichung city was analyzed. Two different optimization algorithms, Artificial Bee Colony and Fuzzy C-Means Particle Swarm Optimization, were incorporated to the clustering analysis. The result indicates Artificial Bee Colony based clustering approach renders better classification accuracy rate than that based on Fuzzy C-Means Particle Swarm Optimization, which are 89.5% and 80.75% respectively. This study shows bionics computation is a feasible approach and alternative for remote sensing image classification.

關鍵字

仿生計算、影像判釋、蜂群演算法、模糊粒子群演算法

Keywords

bionics computation, image classification, artificial bee colony algorithm, fuzzy C-Means particle swarm optimization algorithm

附件檔名

華芸線上圖書館

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備註說明

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