کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
4464647 1313839 2016 10 صفحه PDF ندارد دانلود رایگان
عنوان انگلیسی مقاله
Multiscale quantification of urban composition from EO-1/Hyperion data using object-based spectral unmixing
ترجمه فارسی عنوان
تعیین کمی چندمقیاسی از ترکیب شهری از داده های Hyperion EO-1 با استفاده از جداسازی طیفی مبتنی بر شی
کلمات کلیدی
MESMA مبتنی بر شی ؛ EO-1/Hyperion ؛ تعیین کمی چندمقیاسی؛ ترکیب شهری
Object-based MESMA; EO-1/Hyperion; Multiscale quantification; Urban composition
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات کامپیوتر در علوم زمین
چکیده انگلیسی


• Quantified the urban composition at multiple scales.
• Applied the object-based MESMA to unmix EO-1/Hyperion imagery.
• Compared the object-based mixture analysis with the pixel-based one.

Quantification of the urban composition is important in urban planning and management. Previous research has primarily focused on unmixing medium-spatial resolution multispectral imagery using spectral mixture analysis (SMA) in order to estimate the abundance of urban components. For this study an object-based multiple endmember spectral mixture analysis (MESMA) approach was applied to unmix the 30-m Earth Observing-1 (EO-1)/Hyperion hyperspectral imagery. The abundance of two physical urban components (vegetation and impervious surface) was estimated and mapped at multiple scales and two defined geographic zones. The estimation results were validated by a reference dataset generated from fine spatial resolution aerial photography. The object-based MESMA approach was compared with its corresponding pixel-based one, and EO-1/Hyperion hyperspectral data was compared with the simulated EO-1/Advanced Land Imager (ALI) multispectral data in the unmixing modeling. The pros and cons of the object-based MESMA were evaluated. The result illustrates that the object-based MESMA is promising for unmixing the medium-spatial resolution hyperspectral imagery to quantify the urban composition, and it is an attractive alternative to the traditional pixel-based mixture analysis for various applications.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Applied Earth Observation and Geoinformation - Volume 47, May 2016, Pages 153–162
نویسندگان
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