Journal of Remote Sensing & GIS

Department of Data Science, JIS Institute of advanced studies and Research, Kolkata, India

Publications

• Review Article   
  Machine Learning-based Estimation of the Number of Endmembers for Unmixing Hyperspectral Image
  Author(s): Bipasha Chakrabarti*
  
  Spectral unmixing involves understanding the ground scene by inferring the endmember reflectance pattern, and computing their respective fractional abundance. Unmixing methods can be categorized as blind or semi-blind approach, based on the availability of spectral library data. Many existing methods for unmixing and endmember determination assume prior knowledge of the number of endmembers in the image scene. However, in reality, the number of endmembers is mostly unknown, besides, considering a huge sized spectral library as the endmember set leads to specific predicaments. Therefore, proper estimation of the number of consistent materials or endmembers is a vital task. The unmixing methods tend to consider mixed pixels as endmembers in case of overestimation of endmember number. On the other hand, some actual endmembers are unidentified due to underestimation. The eigenva.. View more»
  

  DOI: 10.35248/2469-4134.23.12.327

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