Paper Info
Title
Clustering Multispectral Images Using Spatial-Spectral Information
Abstract
Clustering is an important topic in image analysis and has many applications. Owing to the limitations of the feature space in multispectral images and spectral overlap of the clusters, it is required to use some additional information such as the spatial context in image clustering. To increase the accuracy of image clustering, a new Hierarchical Iterative Clustering Algorithm using Spatial and Spectral information (HICLASS) is introduced. This algorithm separates pixels into uncertain and certain categories based on decision distances in the feature space. The algorithm labels the certain pixels using the -means clustering, and the uncertain ones with the help of information in both spatial and spectral domains of the image. The proposed algorithm is tested using simulated and real data. The benchmark results indicate better performance of HICLASS when compared with the -means, local embeddings, and some proximity-based algorithms. The overall accuracy of the -means has increased between 12.5% and 20.4% for different data. The HICLASS method increases the accuracy and generates more homogeneous regions, which are required for object-based applications.
Year
DOI
Venue
2015
10.1109/LGRS.2015.2411558
Geoscience and Remote Sensing Letters, IEEE  
Keywords
Field
DocType
$k$-means,clustering,hierarchical algorithm,multispectral,remote sensing,accuracy,labeling,clustering algorithms,algorithm design and analysis,image processing,k means,image segmentation
CURE data clustering algorithm,Remote sensing,Image processing,Multispectral pattern recognition,Artificial intelligence,FLAME clustering,Cluster analysis,Computer vision,Canopy clustering algorithm,Pattern recognition,Correlation clustering,Multispectral image,Mathematics
Journal
Volume
Issue
ISSN
PP
99
1545-598X
Citations 
PageRank 
References 
2
0.36
11
Authors
3
Name
Order
Citations
PageRank
Sayyed Bagher Fatemi120.69
Mohammad Reza Mobasheri2272.64
Ali A. Abkar320.69