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Multi-objective semi-supervised clustering for automatic pixel classification from remote sensing imagery

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Abstract

Classifying the pixels of satellite images into homogeneous regions is a very challenging task as different regions have different types of land covers. Some land covers contain more regions, while some contain relatively smaller regions (e.g., bridges, roads). In satellite image segmentation, no prior information is available about the number of clusters. Here, in this paper, we have solved this problem using the concepts of semi-supervised clustering which utilizes the property of unsupervised and supervised classification. Three cluster validity indices are utilized, which are simultaneously optimized using AMOSA, a modern multiobjective optimization technique based on the concepts of simulated annealing. The first two cluster validity indices, symmetry distance based Sym-index, and Euclidean distance based I-index, are based on unsupervised properties. The last one is a supervised information based cluster validity index, Minkowski index. For supervised information, initially fuzzy C-mean clustering technique is used. Thereafter, based on the highest membership values of the data points to their respective clusters, randomly 10 % data points with their class labels are chosen. The effectiveness of this proposed semi-supervised clustering technique is demonstrated on three satellite image data sets of different cities of India. Results are also compared with existing clustering techniques.

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Abbreviations

SOO:

Single objective optimization

MOO:

Multiobjective optimization

AMOSA:

Archived multiobjective simulated annealing based technique

SA:

Simulated annealing

FCM:

Fuzzy C-means

MOGA:

Multiobjective genetic algorithm

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Authors and Affiliations

  1. Computer Science Engineering, Indian Institute of Technology, Patna, India

    Abhay Kumar Alok, Sriparna Saha & Asif Ekbal

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  1. Abhay Kumar Alok
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  2. Sriparna Saha
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  3. Asif Ekbal
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Corresponding author

Correspondence to Abhay Kumar Alok.

Additional information

Communicated by Y.-S. Ong.

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Alok, A.K., Saha, S. & Ekbal, A. Multi-objective semi-supervised clustering for automatic pixel classification from remote sensing imagery. Soft Comput 20, 4733–4751 (2016). https://doi.org/10.1007/s00500-015-1701-x

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  • DOI: https://doi.org/10.1007/s00500-015-1701-x

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