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Wheat Monitoring by Using Kernel Based Possibilistic c-Means Classifier: Bi-sensor Temporal Multi-spectral Data

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Abstract

The existence of mixed pixels in the satellite images has always been an area of concern. Adding to the challenge is an occurrence of non-linearity between the classes, which is generally overlooked. The study makes an attempt to solve the two frequently occurring problems by kernel based fuzzy approach. This research work deals with Possibilistic c-Means (PCM) classifier with local, global, spectral angle and hyper tangent kernels for wheat crop (Triticum aestivum) identification in Haridwar, Uttarakhand, India. The multi-temporal vegetation index data of Formosat-2 have been used which covers the whole phenology of wheat crop. The additional sensor Landsat-8 OLI imagery has been filled the crucial gap of Formosat-2 temporal datasets. Nine types of proposed kernels based PCM classifier have been applied on three temporal datasets (four, five and six date combinations) to classify two classes early sown and late sown wheat crop. These test results have been concluded that at optimized weighted constant KMOD and polynomial kernel was found effective to separate wheat crop. The five and six date combination were sufficient to discriminate early sown and late sown wheat crop.

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Acknowledgements

The authors would like to thank Sponsors the National Space Organization, National Applied Research Laboratories and jointly supported by the Chinese Taipei Society of Photogrammetry and Remote Sensing and the Center for Space and Remote Sensing Research, National Central University of Taiwan for providing Formosat-2 temporal images of Haridwar district, Uttrakhand, India. The first author is thankful to IIRS ISRO for allowing ten month M. Tech project on study of specific crop identification by using kernel based fuzzy classifier.

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Authors and Affiliations

  1. National Institute of Technology, Warangal, Telangana, India

    Rohit Nandan Tripathi

  2. H.N.B. Garhwal University, Shrinagar, Uttarakhand, India

    Ranjeet Kumar

  3. Indian Institute of Remote Sensing, (IIRS/ISRO), Dehradun, Uttarakhand, India

    Anil Kumar & A. Senthil Kumar

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  1. Rohit Nandan Tripathi
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  2. Ranjeet Kumar
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  3. Anil Kumar
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  4. A. Senthil Kumar
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Correspondence to Anil Kumar.

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Tripathi, R.N., Kumar, R., Kumar, A. et al. Wheat Monitoring by Using Kernel Based Possibilistic c-Means Classifier: Bi-sensor Temporal Multi-spectral Data. J Indian Soc Remote Sens 45, 1005–1014 (2017). https://doi.org/10.1007/s12524-016-0651-9

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