ENGLISH

14卷/2期

14卷/2期

華藝線上圖書館

Pages:

83-94

論文名稱

整合空載光達點雲與地形圖模塑房屋之分治策略

Title

A Divide-and-Conquer Strategy for Building Reconstruction Using Airborne LIDAR Point Clouds and Topographic Maps

作者

張智安, 陳良健

Author

Tee-Ann Teo, Liang-Chien Chen

中文摘要

數碼城市是真實城市於資訊系統中建構的數位式虛擬版,數碼城市可應用於城市之規劃、設計、建設、及管理等。房屋模型為數碼城市中重要的元件之一。在傳統航測製圖作業中,使用航空影像立體對重建房屋模型。近年來,光達系統技術漸趨成熟,提供了另一類資料進行房屋建模,因此,本研究之目的為使用空載光達點雲進行房屋重建。本研究的主要貢獻提出以分治策略進行複雜房屋模型之重建,研究中結合空載光達點雲及地形圖重建房屋模型。主要工作包含三個步驟:(1)房屋分解,(2)房屋基元形塑,及(3)房屋基元合併。在房屋分解時,使用空載光達資料偵測屋頂結構線,並利用該屋頂結構線分解地形圖之房屋輪廓,以產生簡單的二維房屋基元。接著,使用每一個房屋基元內的空載光達點雲形塑平面或圓弧面屋頂。最後,考量基元間之共面及共線特性將三維房屋基元合併為一房屋模型。實驗中使用台北地區資料進行測試,重建之成功率可達92%且漏授率低於5%,房屋模塑誤差為55公分,房屋模型重建之平面精度為40公分,高程精度為70公分。實驗結果顯示,本研究所提出的方法可產生高可靠度之房屋模型。

Abstract

The cyber city, a replica of the real one, has demonstrated its potentials in urban and environmental planning, design, construction, and management. Building model is one of the most important elements in a cyber city. Traditionally, the reconstruction of building models is performed by using aerial photography. As an emerging technology, the airborne LIDAR (LIght Detection And Ranging) system provides a promising alternative. Hence, this investigation utilizes the airborne LIDAR point clouds for building reconstruction. The major contribution of this investigation is to propose a solution for the reconstruction of complex buildings. This investigation presents a scheme for the reconstruction of building models from airborne LIDAR point clouds and topographic maps by using a divide-and-conquer strategy. The proposed scheme comprises three major parts: (1) decomposition of building boundaries (2) shaping of building primitives, and (3) combination of building primitives. In the decomposition of building boundaries, the airborne LIDAR data is selected to extract the inner structure lines. Then, the split procedure divides the building boundaries into several building primitives using the extracted feature lines. In the shaping of building primitives, the parameters fitting is applied to shape the roof for each building primitive from airborne LIDAR point clouds. The roof shapes include both planar and circular types. Finally, a least squares adjustment considering the co-planarity and co-linearity is used to merge the 3-D building primitives into building models. The proposed method has been tested with the data collected in Taipei city of Taiwan. The reconstruction rate is better than 92% while the omission error is smaller than 5%. The shaping error is 55cm. The planimetric and vertical accuracy of the reconstructed models are better than 40cm and 70cm, respectively. The experimental results confirm that the proposed scheme produces high fidelity models.

關鍵字

空載光達、地形圖、房屋重建、分治策略

Keywords

LIDAR, topographic maps, building reconstruction, divide-and-conquer strategy

附件檔名

華芸線上圖書館

N / A

備註說明

200906-14-2-83-94

Pages:

95-113

論文名稱

結合空載光達點雲與航照影像進行房屋模型之變遷偵測

Title

Detection of Building Model Changes from LIDAR Data and Aerial Imagery

作者

陳良健、黃智遠

Author

Liang-Chien Chen , Chih-Yuan Huang

中文摘要

三維房屋模型可提供真實物空間三維資訊與決策支援。針對有變遷的房屋區域進行重新建置,將可減少資料更新所需成本且提升效率。傳統上常利用多時期影像之光譜差異進行變遷偵測,此方法僅有二維光譜資訊而缺乏三維形狀資訊。隨著空載光達系統成熟,使三維形狀資訊取得容易。本研究使用後期空載光達點雲及航照影像進行前期房屋模型之變遷偵測。主要工作項目包含資料前處理及判定房屋模型變遷型態。資料前處理工作包括資料套合、偵測地面與植生區域、剔除地面與植生區域之光達點雲、且計算前後期高程差。判定房屋模型變遷型態工作主要是結合光譜及形狀資訊進行判定;本研究設定五種變遷型態,分別為未改變、主結構改變、副結構改變、拆除、及植生遮蔽。本研究成果於判定房屋模型變遷型態可達85%整體精度。為詳細了解影響研究成果的因素,文中針對錯誤例進行分析。造成錯誤的原因中,研究方法的限制佔38%、資料本身之限制佔11%、資料品質造成的影響佔51%。

Abstract

Three dimensional building models provide spatial information for decision support. It is more preferable to maintain a building database by the firstly detecting the changes followed by a reconstruction procedure. Change detection is traditionally done using multi-temporal images through the spectral analyses. Those images provide two-dimensional spectral information without including shape in the third dimension. As the availability and quality of emerging airborne LIDAR systems that make the acquisition of shape information convenient, we use new airborne LIDAR point clouds and aerial photos to detect changes for building models. The proposed scheme comprises data pre-processing and change detection on building areas. In the first step spatial registration is performed, ground area and vegetation area are detected, the Lidar points on the ground surface and in vegetation areas are removed, and height differences are calculated. In the second step, shape and spectral information are integrated to determine types of change. The validation for determination of change types shows that the results can reach 85% overall accuracy. To provide comprehensive observations, those unreliable results are scrutinized. The ratios of the “limitation of method”, “limitation of data”, and “data quality effect” categories are 38%, 11%, and 51%, respectively.

關鍵字

變遷偵測、房屋模型、空載光達點雲、航照影像

Keywords

Change Detection, Building Model, Airborne Lidar, Aerial Imagery.

附件檔名

華芸線上圖書館

N / A

備註說明

200906-14-2-95-113

Pages:

115-125

論文名稱

應用經驗模分解技術進行高光譜影像之雜訊過濾

Title

Noise Reduction of Hyperspectral Image Using Emperical Mode Decomposition

作者

張立雨、 陳繼藩

Author

L. Y. Chang , C. F. Chen

中文摘要

高光譜遙測影像一般而言較其他遙測資料可提供更為詳盡之光譜資訊,因此在用於辨識各種不同地物時有助於精度之提升。而對於此種影像來說,線性光譜混合模型(Linear Spectral Mixing Model)為常用之模式來對影像中之光譜值與其對應各種地物間之光譜反應加以描述,並且進而反演得到所對應各種地物之含量。基本上,線性光譜混合模型可利用最小二乘法加以求解來達到解算各種地物之含量之目的,並且同時使得在光譜上所產生之誤差達到最小之狀態。理論上,若能有效過濾高光譜影像本身所具有之雜訊時,求解所得之地物之含量在精度上應會有所提升。通常對高光譜遙測影像而言,因大氣中各種分子在不同波段上具有其特定之吸收與散射特性,因此會對感測器接收來自於地物之輻射能量造成增減而產生雜訊。在本研究中使用經驗模分解法(Empirical Mode Decomposition, EMD)來對原始高光譜影像進行分解組合,並且進而過濾其雜訊,以期能在最小二乘求解時能降低其殘差,以達到提高求解地物含量精度之目的。由研究成果顯示,在模擬的高光譜影像資料上進行雜訊過濾後,求解所得之含量誤差較未過濾前有顯著之降低。此外若直接應用所提方法於真實影像上時,就求解過程所得之殘差上而言,雜訊過濾後所得結果之殘差亦較未濾前為低。

Abstract

Hyperspectral images are able to provide the detailed spectral information necessary for the discrimination of different land targets in various kinds of remotely sensed images. The linear spectral mixing model is a widely used discrimination method for modeling the variety of multiple land targets in hyperspectral images. Basically, the linear spectral mixing model is solved by least squares adjustment to acquire the minimal-error solutions. It is believed that the reduction of the noise that inherently exists in each spectral band of the hyperspectral image can increase the discrimination accuracy. In general, the noise recorded in the hyperspectral sensor is caused by the absorption or scattering effect of the atmospheric particles during the energy transportation process. In this study, a noise filtering preprocessing based on empirical mode decomposition (EMD) is proposed. The purpose is to reduce the inherent noise and further minimize the residuals of least squares solutions for hyperspectral images. The test results with simulated images indicate that the proposed noise filtering process can effectively decrease the abundance errors. Moreover, in the experiment with real image, the residuals of least square adjustment for noise reduced data set are also smaller than that for original data set.

關鍵字

高光譜影像、雜訊過濾、經驗模分解

Keywords

Noise Reduction, Hyperspectral Images, Empirical Mode Decomposition

附件檔名

華芸線上圖書館

N / A

備註說明

200906-14-2-115-125

Pages:

127-140

論文名稱

以區塊化物件分類法萃取ADS-40影像中水稻田坵塊資訊之研究

Title

The Study on Regional Object Classification for Paddy Rice Patch Extraction from ADS-40 Image

作者

雷祖強、李哲源、葉惠中、萬絢

Author

Tsu Chiang Lei, Che-Yuan Li, Hui-Chung Yeh, Shiuan Wan

中文摘要

影像應用時最為重要的研究議題就是影像分類。基本上人類分類影像程序是將影像中區塊特徵(Region Feature)萃取出來,合併其特徵後進而形成有效的識別物件。然而,目前機器分類影像時是以逐像元概念(Pixel-based)進行特徵萃取之工作,兩者程序並不相同。為解決上述問題本研究提出以區塊為主(Region-based)的區塊化物件分類 (Regional Object Classification, ROC)法,這是一個混合非監督程序(區域成長法)與監督式程序(最大概似分類法)的新概念,這個概念可解決逐像元分類法應用於高解析度影像時所造成之嚴重的椒鹽效應(Salt and Pepper Effect)。本研究在雲林縣農業區內選擇高解析度數值航照影像(ADS-40),針對水稻這個類別作為實證項目,以逐像元與區域化分類概念進行問題比較。研究結果顯示,透過區塊化物件分類模式可萃取出完整度極高的水稻坵塊田,分類精度可從92.6% (Pixel-based)提高到95.3% (Region-based)。

Abstract

The image classification is the most important issue in Remote Sensing. In essence, extraction of regional feature is an important process on human’s image classification process to form their effective identification of the target objects. However, vision cognition of machine learning follows Pixel-based which disobeys the rule of human’s image classification process. To resolve the forgoing problems, this study proposes a Region-based approach for Regional Object Classification (ROC). This method is a hyper module which applies an unsupervised method (Region-Growing, RG) to a supervised approach (Maximum Likelihood Classifier, MLC). New concept can effectively reduces a Salt and Pepper effect of classification result from very high resolution image by conventional pixel base classifier. Accordingly, a module of Regional concept is applied to improve its classification accuracy. Our outcomes are used by mean of the ADS-40 data to present the classification performance. Finally, this study utilized the ROC classifier and the overall accuracy of region based concepts (95.3%) is better than those of the pixel-based concepts (92.6%) on the evaluation of paddy rice from ADS-40 image. This result shows that an appropriate progress through regional module can effectively improve the accuracy of image classification.

關鍵字

物件、區域成長法、影像判釋

Keywords

Object, Region Growing Method, Image Recognition

附件檔名

華芸線上圖書館

N / A

備註說明

200906-14-2-127-140

Pages:

141-155

論文名稱

基於尺度不變特徵點轉換之精密多元影像套合

Title

Precise Multisource Image Registration Based on Scale Invariant Feature Transform Points

作者

吳 究、張 奇、劉銘哲

Author

Joz Wu ,Chi Chang , Ming-Che Liub

中文摘要

隨著科技的進步與衛星的多元化,影像套合在遙感探測之影像處理方面是一個非常基本的應用,後續應用包括了影像融合、變遷偵測等。因此,本研究將利用尺度不變特徵轉換、Canny特徵萃取、最小二乘匹配法等技術,進行多來源遙測影像匹配與套合。首先利用人工點選或是利用尺度不變特徵轉換(SIFT)運算元,尋找主影像與附屬影像之間的初始對應關係,接著分別利用Canny運算元萃取兩張影像的邊緣,並且清理較破碎的邊緣後,利用成本函數之梯度方位與距離進行邊緣點匹配。之後再以匹配成功的初始共軛點為中心開啓影像窗,進行影像分塊與輻射參數校正,主影像窗與校正後之附屬影像窗以最小二乘匹配法進行更精確的定位。最後將所有共軛點對利用薄板樣條法(TPS),並結合粗差濾除後建立套合影像,各項實驗結果證明上述方法能順利完成不同資源衛星影像之套合任務。

Abstract

Image registration is a key issue in many image processing applications in remote sensing. Examples of these applications include change detection using multiple images acquired at different times, and fusion of image data from multiple sensor types.SIFT (Scale Invariant Feature Transform), Canny feature matching and least-squares area matching method are proposed in this research. At first, the initial matching point pairs are detected from manual adjustment or the SIFT algorithm, which is invariant to image scale. And then, edges in both images are located by using the Canny algorithm and broken contours are cleaned. Furthermore, more matching point pairs are selected using a cost function that measures the gradient orientation and distance between all possible pairs of the points. Pairing image windows are built and segmented to get radiometric parameters, and the radiometric parameters are used here to modulate the slave image window. Finally, master image window and modulated slave image window are matched by least-squares matching, and conjugate points are found. The Thin-Plate Splines (TPS) and blunder removal methods are used to register master and slave images. Experimental results show that numerous matching points can be obtained correctly and automatically, and different satellite images can be registered precisely.

關鍵字

影像匹配、影像套合、尺度不變特徵轉換演算法、薄板樣條法

Keywords

image matching, image registration, SIFT algorithm, thin-plate splines

附件檔名

華芸線上圖書館

N / A

備註說明

200906-14-2-141-155

Pages:

157-170

論文名稱

光達資料在臺灣海岸地形變遷上的應用

Title

Application of LiDAR-derived data on Detecting Coastal Geomorphic Change in Taiwan

作者

陳映璇、沈淑敏、詹瑜璋、謝有忠

Author

Yin-Hsuen Chen, Su-Min Shen , Yu-Chang Chan, Yu-Chung Hsieh

中文摘要

光達技術生產的高精度數值地形模型,是監測海岸地形變遷的利器。我國已於近年引進光達技術,雖然尚未建置專為海岸監測而產製的規範,但所生產之高精度DTM,仍為海岸變遷研究的寶貴資料,須善加利用。本文回顧國際上如何應用光達資料在海岸變遷研究上,討論臺灣早期圖資中哪些可能和光達資料套疊比對,並以淡水河口南岸八里海岸為樣區,透過實作探討光達資料在監測海岸地形變遷上,有哪些限制或需注意之處。本研究結合1980年代之1:1000海岸像片地形圖和既有之光達資料,並以高程濱線指標為濱線定義,進行濱線變化及地形變化量的探討。比對結果顯示,該段海岸濱線位置在這20年期間,有53-65公尺不等的變動量。以兩種資料直接或間接生產之DTM,套疊進行地形起伏變化量時,則需特別注意因兩種資料的測量、製作方式不同,所產生的問題。簡而言之,臺灣海岸現有光達資料雖然大多只有一期,但應可和海岸像片地形圖或現場實測斷面所生產的DTM進行套疊比對,量化海岸地形之變遷量。展望未來,筆者期盼政府相關單位應儘速建置濱線製圖(shoreline mapping)的規範,包括從現場拍攝、資料生產到濱線指標的訂定,才可能為建立海岸地形(或濱線)變遷監測,奠定堅實的基礎。

Abstract

LiDAR is a very useful tool on monitoring coastal geomorphic change and has been introduced into Taiwan in recent years. For making the best use of the LiDAR-derived data on long-term coastal change in Taiwan, this research intends to reveal the possibility and limitation of overlapping LiDAR-derived DTM with preexisting pictorial materials, in particular Orthophoto Base Map in the coastal area (OBMC, scale 1:1000, published in 1980s). The study area is the Bali coast, located to the south of the Dan-shui River mouth.Two types of LiDAR data, provided by Central Geological Survey, MOEA, - LAS file and 2m DTM were used, and 1-meter contour line was adopted as the datum-based shoreline indicator. DTM were also constructed by interpolating contour lines of Orthophoto Base Map. Results show that to combine these two sets of pictorial materials to detect shoreline change and calculate volume change of beach and sand dunes is practicable. The amount of shoreline change is from 53 m to 65 m over two decades along the study area. Many details should be taken into account, however, when detecting volume change of beach/sand dune due to the different resolution and accuracy of the two sets of data.So far, most pictorial materials in Taiwan are not produced for the sea shore zone in purpose, and cause many problems on shoreline mapping work. Thus, it is expected that the authority can set up standards and/or guidelines of shoreline mapping (including LiDAR) in the near future.

關鍵字

光達、海岸像片地形圖、高程濱線指標、數值地形模型

Keywords

LiDAR, Orthophoto Base Map in the coastal area, datum-based shoreline indicator, DTM

附件檔名

華芸線上圖書館

N / A

備註說明

200906-14-2-157-170

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