東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

A framework for automatic feature ex...

Yan, Jianhua.

FindBook

Google Book

Amazon

博客來

A framework for automatic feature extraction from airborne light detection and ranging data.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	A framework for automatic feature extraction from airborne light detection and ranging data./
作者:	Yan, Jianhua.
面頁冊數:	113 p.
附註:	Adviser: Shu-Ching Chen.
Contained By:	Dissertation Abstracts International68-08B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3279239
ISBN:	9780549206477

A framework for automatic feature extraction from airborne light detection and ranging data.
Yan, Jianhua.

A framework for automatic feature extraction from airborne light detection and ranging data. - 113 p.

Adviser: Shu-Ching Chen.

Thesis (Ph.D.)--Florida International University, 2007.

Recent advances in airborne Light Detection and Ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. Airborne LIDAR systems usually return a 3-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. This technology is becoming a primary method for extracting information of different kinds of geometrical objects, such as high-resolution digital terrain models (DTMs), buildings and trees, etc. In the past decade, LIDAR gets more and more interest from researchers in the field of remote sensing and GIS. Compared to the traditional data sources, such as aerial photography and satellite images, LIDAR measurements are not influenced by sun shadow and relief displacement. However, voluminous data pose a new challenge for automated extraction the geometrical information from LIDAR measurements because many raster image processing techniques cannot be directly applied to irregularly spaced LIDAR points.

ISBN: 9780549206477Subjects--Topical Terms:

626642
Computer Science.

A framework for automatic feature extraction from airborne light detection and ranging data.
LDR:03635nam 2200301 a 45 001 940009
005 20110517
008 110517s2007 ||||||||||||||||| ||eng d
020 $a 9780549206477
035 $a (UMI)AAI3279239
035 $a AAI3279239
040 $a UMI $c UMI
100 1 $a Yan, Jianhua. $3 1264116
245 1 2 $a A framework for automatic feature extraction from airborne light detection and ranging data.
300 $a 113 p.
500 $a Adviser: Shu-Ching Chen.
500 $a Source: Dissertation Abstracts International, Volume: 68-08, Section: B, page: 5375.
502 $a Thesis (Ph.D.)--Florida International University, 2007.
520 $a Recent advances in airborne Light Detection and Ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. Airborne LIDAR systems usually return a 3-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. This technology is becoming a primary method for extracting information of different kinds of geometrical objects, such as high-resolution digital terrain models (DTMs), buildings and trees, etc. In the past decade, LIDAR gets more and more interest from researchers in the field of remote sensing and GIS. Compared to the traditional data sources, such as aerial photography and satellite images, LIDAR measurements are not influenced by sun shadow and relief displacement. However, voluminous data pose a new challenge for automated extraction the geometrical information from LIDAR measurements because many raster image processing techniques cannot be directly applied to irregularly spaced LIDAR points.
520 $a In this dissertation, a framework is proposed to filter out information about different kinds of geometrical objects, such as terrain and buildings from LIDAR automatically. They are essential to numerous applications such as flood modeling, landslide prediction and hurricane animation. The framework consists of several intuitive algorithms. Firstly, a progressive morphological filter was developed to detect non-ground LIDAR measurements. By gradually increasing the window size and elevation difference threshold of the filter, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Then, building measurements are identified from no-ground measurements using a region growing algorithm based on the plane-fitting technique. Raw footprints for segmented building measurements are derived by connecting boundary points and are further simplified and adjusted by several proposed operations to remove noise, which is caused by irregularly spaced LIDAR measurements. To reconstruct 3D building models, the raw 2D topology of each building is first extracted and then further adjusted. Since the adjusting operations for simple building models do not work well on 2D topology, 2D snake algorithm is proposed to adjust 2D topology. The 2D snake algorithm consists of newly defined energy functions for topology adjusting and a linear algorithm to find the minimal energy value of 2D snake problems. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. The results demonstrated that the proposed framework achieves a very good performance.
590 $a School code: 1023.
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Remote Sensing. $3 1018559
690 $a 0544
690 $a 0799
690 $a 0984
710 2 $a Florida International University. $3 718935
773 0 $t Dissertation Abstracts International $g 68-08B.
790 $a 1023
790 1 0 $a Chen, Shu-Ching, $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3279239