Senthilnath, J and Omkar, SN and Mani, V and Karnwal, Nitin and Shreyas, PB (2013) Crop stage classification of hyperspectral data using unsupervised techniques. In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6 (3). pp. 861-866.
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The presence of a large number of spectral bands in the hyperspectral images increases the capability to distinguish between various physical structures. However, they suffer from the high dimensionality of the data. Hence, the processing of hyperspectral images is applied in two stages: dimensionality reduction and unsupervised classification techniques. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The selected dimensions are classified using Niche Hierarchical Artificial Immune System (NHAIS). The NHAIS combines the splitting method to search for the optimal cluster centers using niching procedure and the merging method is used to group the data points based on majority voting. Results are presented for two hyperspectral images namely EO-1 Hyperion image and Indian pines image. A performance comparison of this proposed hierarchical clustering algorithm with the earlier three unsupervised algorithms is presented. From the results obtained, we deduce that the NHAIS is efficient.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to IEEE-Inst Electrical Electronics Engineers Inc.|
|Keywords:||Hyperspectral Images; Niche Hierarchical Artificial Immune System; Principal Component Analysis|
|Department/Centre:||Division of Mechanical Sciences > Aerospace Engineering (Formerly, Aeronautical Engineering)|
|Date Deposited:||11 Jul 2013 06:37|
|Last Modified:||11 Jul 2013 06:37|
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