DocumentCode
692804
Title
Spectral derivative features for supervised classification of remote sensing data: An experimental evaluation
Author
Jiangfeng Bao ; Mingmin Chi ; Benediktsson, Jon Atli
Author_Institution
Sch. of Comput. Sci., Fudan Univ., Shanghai, China
fYear
2012
fDate
4-7 June 2012
Firstpage
1
Lastpage
4
Abstract
Derivatives of spectral reflectance signatures can capture salient features of different land-cover classes. This information is widely used for supervised classification of remote sensing data. In the paper, we study the issue when the derivatives of spectral reflectance work for supervised classification of remote sensing data and give an empirical conclusion in terms of a large amount of experimental evaluations.
Keywords
geophysical image processing; image classification; remote sensing; land-cover classes; remote sensing data; spectral derivative features; spectral reflectance signatures; spectral reflectance work; supervised classification; Abstracts; Educational institutions; Hyperspectral imaging; Logic gates; Support vector machines; Spectral derivatives; hyperspectral data; remote sensing;
fLanguage
English
Publisher
ieee
Conference_Titel
Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2012 4th Workshop on
Conference_Location
Shanghai
Print_ISBN
978-1-4799-3405-8
Type
conf
DOI
10.1109/WHISPERS.2012.6874247
Filename
6874247
Link To Document