Paper Info
Title
Material Classification of Hyperspectral Images Using Unsupervised Fuzzy Clustering Methods
Abstract
This paper presents a novel approach in classifying materials in Hyperspectral images. In particular, unlike other similar approaches in which every pixel in the image is mapped to one of the reference spectra, the proposed methods use the data itself to create clusters of pixels with the same material. This is done by using unsupervised fuzzy clustering methods. Here, two fuzzy clustering approaches have been addressed: Fuzzy C-Means clustering (FCM) and Fuzzy Relational Clustering (FRC). The proposed methods can also solve the problem of identifying the objects for which the radiance of light makes it barely hard to identify them as a single object e.g., a pitched roof. The proposed methods have been applied on the CASI image and the results show that they can successfully classify the materials in the image.
Year
DOI
Venue
2007
10.1109/SITIS.2007.113
SITIS
Keywords
Field
DocType
fuzzy clustering approach,novel approach,unsupervised fuzzy clustering method,casi image,material classification,fuzzy c-means clustering,pitched roof,unsupervised fuzzy clustering methods,hyperspectral image,fuzzy relational clustering,classifying material,fuzzy clustering,fuzzy set theory,image classification,hyperspectral imaging,object recognition,hyperspectral sensors,materials,clustering algorithms,pixel
Canopy clustering algorithm,Fuzzy clustering,Computer vision,Correlation clustering,Pattern recognition,Computer science,Fuzzy set,Artificial intelligence,FLAME clustering,Conceptual clustering,Cluster analysis,Contextual image classification
Conference
Citations 
PageRank 
References 
3
0.40
6
Authors
4
Name
Order
Citations
PageRank
Soudeh Kasiri-Bidhendi171.17
Abbas Sarraf Shirazi2232.66
Narges Fotoohi330.40
Mohammad Mehdi Ebadzadeh437227.36