کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4459009 1621276 2012 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Combining object-based texture measures with a neural network for vegetation mapping in the Everglades from hyperspectral imagery
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات کامپیوتر در علوم زمین
پیش نمایش صفحه اول مقاله
Combining object-based texture measures with a neural network for vegetation mapping in the Everglades from hyperspectral imagery
چکیده انگلیسی

An informative and accurate vegetation map for the Greater Everglades of South Florida is in an urgent need to assist with the Comprehensive Everglades Restoration Plan (CERP), a $10.5-billion mission to restore the south Florida ecosystem in 30 + years. In this study, we examined the capability of fine spatial resolution hyperspectral imagery collected from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) for vegetation mapping in the Everglades. In order to obtain an efficient and accurate procedure for vegetation discrimination, we developed a neural network classifier first and then combined the object-based texture measures with the classifier to examine the contribution of the spatial information for vegetation mapping. The neural network is capable of modeling the characteristics of multiple spectral and spatial signatures within a class by an internally unsupervised engine and characterizing spectral and spatial differences between classes by an externally supervised system. The designed procedure was tested in a portion of the Everglades. An object-based vegetation map was generated with an overall classification accuracy averaged 94% and a kappa value averaged 0.94 in discriminating 15 classes. The results are significantly better than those obtained from conventional classifiers such as maximum likelihood and spectral angle mapper. The study illustrates that combining object-based texture measures in the neural network classifier can significantly improve the classification. It is concluded that fine spatial resolution hyperspectral data is an effective solution to accurate vegetation mapping in the Everglades which has a rich plant community with a high degree of spatial and spectral heterogeneity.


► Developed a neural network classifier for hyperspectral data analysis.
► Designed a procedure for complex wetland vegetation mapping.
► Combined four remote sensing fields in vegetation mapping.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Remote Sensing of Environment - Volume 124, September 2012, Pages 310–320
نویسندگان
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