Total views : 231

Framework for Hyperspectral Image Segmentation using Unsupervised Algorithm

Affiliations

  • Department of Information Technology, Gandhi Institute of Technology and Management, Beach Road,Gandhi Nagar, Rushikonda, Visakhapatnam – 530045, Andhra Pradesh, India
  • Department of Computer Science and Systems Engineering, Andhra University, Waltair Junction,Visakhapatnam – 530003, Andhra Pradesh, India

Abstract


Hyperspectral imaging system contains stack of images collected from the sensor with different wavelengths representing the same scene on the earth. This paper presents a framework for hyperspectral image segmentation using a clustering algorithm. The framework consists of four stages in segmenting a hyperspectral data set. In the first stage, filtering is done to remove noise in image bands. Second stage consists of dimensionality reduction algorithms, in which the bands that convey less information or redundant data will be removed. This deletion will decrease the storage requirement, computational load etc in processing the hyperspectral data. In the third stage, the informative bands which are selected in the second stage are merged into a single image using hierarchical fusion technique. The main goal of image fusion is to combine all the information from the selected image bands to form a single image. This single image is segmented using Fuzzy C-means (FCM) algorithm. The qualitative and quantitative analysis shows that this framework will segment the data set more accurately by combining all the features in the image bands.

Keywords

Dimensionality Reduction, Empirical Mode Decomposition, FCM, Hyperspectral Imaging.

Full Text:

 |  (PDF views: 213)

References


  • Romero A et.al. Unsupervised deep feature extraction for remote sensing image classification. Institute of Electrical and Electronics Engineers (IEEE) Transactions on Geoscience and Remote Sensing. 2016; 54(3):1349–62.
  • Saichandana B et.al. Hyperspectral image enhancement using evolutionary algorithm. Indian Journal of Applied Research (IJAR). 2016 Mar; 3(4):934–8.
  • Harikiran J et al. Spot edge detection in microarray images using bi-dimensional empirical mode decomposition.Proceedings of C3IT Elsevier Procedia Technology. Science Direct. 2012; 4:19–25.
  • Chaudri S, Kotwal K. Hyperspectral image fusion. Springer book; 2013.
  • Harikiran J et al. Fast clustering algorithms for segmentation of microarray images. International Journal of Scientific and Engineering Research (IJSER). 2014; 5(10):569–74.
  • Harikiran J et al. Denoising based clustering algorithm for segmentation of microaray image. International Journal of Electronics Communication and Computer Engineering (IJECCE). 2013; 3(6):1608–12.
  • Saichandana B et al. Clustering algorithm combined with empirical mode decomposition for classification of remote sensing image. International Journal of Scientific and Engineering Research (IJSER). 2015; 5(9):686–95.
  • Saichandana B et al. Image fusion in hyperspectral image classification using genetic algorithm. Indonesian Journal of Electrical Engineering and Computer Science. 2016; 2(3):703–11.
  • Sweet JN. The Spectral similarity scale and its application to the classification of hyperspectral remote sensing data. Proceedings of Institute of Electrical and Electronics Engineers (IEEE) International Conference on Image Analysis and Processing (ICIAP); 2009.
  • Gharaati E, Nasri M. A New Band Selection Method for Hyperspectral Images based on Constrained Optimization”, proceedings of Institute of Electrical and Electronics Engineers (IEEE) Conference on Information and Knowledge Technology (IKT); 2015.
  • Yin J et al. A new dimensionality reduction algorithm for hyperspectral image using evolutionary strategy. Institute of Electrical and Electronics Engineers (IEEE) Transactions on Industrial Informatics. 2012 Dec; 8(4):935–43.
  • Saichandana B, Harikiran J, Srinivas K, Kumar RK.Application of BEMD and hierarchical image fusion in hyperspectral image classification. International Journal of Computer Science and Information Security (IJCSIS). 2016 May; 14(5):437–45.
  • Harikiran J et al. Fuzzy C-means with bi-dimensional empirical mode decomposition for segmentation of microarray image. International Journal of Computer Science Issues (IJCSI). 2012; 9(5):273–9.
  • Hyperspectral Remote Sensing Scenes [Internet]. 2014 [cited 2014 Apr 7]. Available from: http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_ Sensing_Scenes.
  • Saichandana B et al. Clustering algorithm combined with hill climbing for classification of remote sensing image. Institute of Advanced Engineering and Science (IAES) International Journal of Electrical and Computer Engineering (IJECE). 2014 Dec; 4(6):923–30.
  • Harikiran J et al. Multiple feature fuzzy C-means clustering algorithm for segmentation of microarray image. Institute of Advanced Engineering and Science (IAES) International Journal of Electrical and Computer Engineering (IJECE).2015; 5(5):1045–53.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.