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Complex network modeling of spectral remotely sensed imagery: A case study of massive green algae blooms detection based on MODIS data

Author

Listed:
  • Sui, Yi
  • Shao, Fengjing
  • Wang, Changying
  • Sun, Rencheng
  • Ji, Jun

Abstract

Feature bands selection and targets classification is of great importance in spectral remotely sensed imagery interpretation. In this work, complex network is adopted for modeling spectral remotely sensed imagery. Subnet is constructed for each band based on spatial neighboring characteristic. Feature bands could be obtained by analyzing and comparing topological characteristics between subnets. After finding feature bands, subnets of feature bands are compounded. Targets classification could be measured by degree distribution of the composited network. This approach is evaluated with empirical experiments based on detecting massive green algae blooms with MODIS data. Feature bands found are coincided with spectral mechanism of green algae. By comparing with FAI, RVI, NDVI, EVI and OSABI methods, our approach improves correct classification rates.

Suggested Citation

  • Sui, Yi & Shao, Fengjing & Wang, Changying & Sun, Rencheng & Ji, Jun, 2016. "Complex network modeling of spectral remotely sensed imagery: A case study of massive green algae blooms detection based on MODIS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 464(C), pages 138-148.
    • Handle: RePEc:eee:phsmap:v:464:y:2016:i:c:p:138-148
    DOI: 10.1016/j.physa.2016.07.061
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    References listed on IDEAS

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    1. Fengjing Shao & Yi Sui, 2014. "Reorganizations of complex networks: Compounding and reducing," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 25(05), pages 1-11.
    2. Wu, Zhenxing & Lu, Xi & Deng, Yong, 2015. "Image edge detection based on local dimension: A complex networks approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 440(C), pages 9-18.
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