Этот файл из на Викискладе и может использоваться в других проектах.
Информация с его страницы описания приведена ниже.
Краткое описание
Spectral LiDAR analysis and terrain classification in a semi-urban environment
()
Автор
McIver, Charles A.
Название
Spectral LiDAR analysis and terrain classification in a semi-urban environment
Издательство
Monterey, California: Naval Postgraduate School
Описание
Remote-sensing analysis is conducted for the Naval Postgraduate School campus, containing buildings, impervious surfaces (asphalt and concrete), natural ground, and vegetation. Data is from the Optech Titan, providing three-wavelength laser data (532, 1064, and 1550 nm) at 10–15 points/m2. Analysis techniques for laser-scanner (LiDAR) data traditionally use only x, y, z coordinate information. The traditional approach is used to initialize the classification process into broad-spatial classes (unclassified, ground, vegetation, and buildings). Spectral analysis contributes a unique approach to the classification process. Tools and techniques designed for multispectral imagery are adapted to the LiDAR analysis herein. ENVI's N-Dimensional Visualizer is employed to develop training sets for supervised classification techniques, primarily Maximum Likelihood. Unsupervised classification for the combined spatial/spectral data is accomplished using a K-means classifier for comparison. The campus is classified into 10 and 16 classes, compared to the four from traditional methods. Addition of the spectral component improves the discrimination among impervious surfaces, other ground elements, and building materials. Maximum Likelihood demonstrates 75% overall classification accuracy, with grass (99.9%), turf (95%), asphalt shingles (94%), light-building concrete (89%), sand (88%), shrubs (85%), asphalt (84%), trees (80%), and clay-tile shingles (77%). Post-process filtering by number of returns increases overall accuracy to 82%.
Subjects: remote sensing; space systems operations; LiDAR; satellite laser altimetry; Optech Titan; multi-wavelength LiDAR; spectral LiDAR; terrain and building classification
Язык
английский
Дата публикации
март 2017
Текущее местонахождение
IA Collections: navalpostgraduateschoollibrary; fedlink
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
FEDLINK - United States Federal Collection spectrallidarnal1094553017 (User talk:Fæ/IA books#Fork8) (batch 1990-2020 #35008)
Использование файла
Нет страниц, использующих этот файл.
Метаданные
Файл содержит дополнительные данные, обычно добавляемые цифровыми камерами или сканерами. Если файл после создания редактировался, то некоторые параметры могут не соответствовать текущему изображению.
Краткое название
Spectral LiDAR analysis and terrain classification in a semi-urban environment