Hierarchical Clustering Algorithm for Land Cover Mapping Using Satellite Images

This paper presents hierarchical clustering algorithms for land cover mapping problem using multi-spectral satellite images. In unsupervised techniques, the automatic generation of number of clusters and its centers for a huge database is not exploited to their full potential. Hence, a hierarchical clustering algorithm that uses splitting and merging techniques is proposed. Initially, the splitting method is used to search for the best possible number of clusters and its centers using Mean Shift Clustering (MSC), Niche Particle Swarm Optimization (NPSO) and Glowworm Swarm Optimization (GSO). Using these clusters and its centers, the merging method is used to group the data points based on a parametric method (k-means algorithm). A performance comparison of the proposed hierarchical clustering algorithms (MSC, NPSO and GSO) is presented using two typical multi-spectral satellite images - Landsat 7 thematic mapper and QuickBird. From the results obtained, we conclude that the proposed GSO based hierarchical clustering algorithm is more accurate and robust.

[1]  David A. Landgrebe,et al.  Hyperspectral Image Data Analysis as a High Dimensional Signal Processing Problem , 2002 .

[2]  Ujjwal Maulik,et al.  Genetic clustering for automatic evolution of clusters and application to image classification , 2002, Pattern Recognit..

[3]  Jon Atli Benediktsson,et al.  A Marker-Based Approach for the Automated Selection of a Single Segmentation From a Hierarchical Set of Image Segmentations , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  Yu Hen Hu,et al.  Detection, classification, and tracking of targets , 2002, IEEE Signal Process. Mag..

[5]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Frans van den Bergh,et al.  A NICHING PARTICLE SWARM OPTIMIZER , 2002 .

[7]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[8]  Tao Jiang,et al.  The Regularized EM Algorithm , 2005, AAAI.

[9]  V. Mani,et al.  Multi-spectral satellite image classification using Glowworm Swarm Optimization , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[10]  David A. Landgrebe,et al.  Hyperspectral image data analysis , 2002, IEEE Signal Process. Mag..

[11]  Debasish Ghose,et al.  Detection of multiple source locations using a glowworm metaphor with applications to collective robotics , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[12]  Debasish Ghose,et al.  Theoretical foundations for rendezvous of glowworm-inspired agent swarms at multiple locations , 2008, Robotics Auton. Syst..

[13]  Larry D. Hostetler,et al.  The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.

[14]  Jon Atli Benediktsson,et al.  Segmentation and classification of hyperspectral images using watershed transformation , 2010, Pattern Recognit..

[15]  M. Rajeshwari,et al.  Automatic road extraction using high resolution satellite image based on texture progressive analysis and normalized cut method , 2009 .

[16]  S.N. Omkar,et al.  Urban Satellite Image Classification using Biologically Inspired Techniques , 2007, 2007 IEEE International Symposium on Industrial Electronics.

[17]  V. Mani,et al.  Clustering using firefly algorithm: Performance study , 2011, Swarm Evol. Comput..

[18]  Yuliya Tarabalka,et al.  Spectral-spatial classification of hyperspectral images using hierarchical optimization , 2011, 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[19]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[20]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[21]  Chris H. Q. Ding,et al.  Cluster merging and splitting in hierarchical clustering algorithms , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[22]  Jon Atli Benediktsson,et al.  Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[23]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .