Performance analysis of maximum likelihood and artificial neural network classifiers for training sets with mixed pixels
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Tarih
2008
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Bellwether Publ Ltd.
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
This study evaluates the performance of an artificial neural network, specifically a multilayer perceptron, and a maximum likelihood algorithm to classify multitemporal Landsat ETM+ remote sensor data. The study area in Turkey is a mountainous region that contains many small scattered fields, usually 5-10 pixels in size. The classifiers were employed to identify eight land cover/use features covering the bulk of the study area using the same training and test datasets in order to avoid any difference resulting from sampling variations. Results show that the neural network approach performed better in extracting land cover information from multi-spectral and multitemporal images with training data sets including a large amount of mixed and atypical pixels. The maximum likelihood classifier was found to be ineffective, particularly in classifying spectrally similar categories and classes having subclasses.
Açıklama
Anahtar Kelimeler
Kaynak
Giscience & Remote Sensing
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
45
Sayı
3
