Spectral unmixing of remotely sensed imagery using maximum entropy

Samir R. Chettri, Nathan S. Netanyahu

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations


This paper addresses the importance of a maximum entropy formulation for the extraction of content from a single picture element in a remotely sensed image. Most conventional classifiers assume a winner take all procedure in assigning classes to a pixel whereas in general it is the case that there exists more than one class within the picture element. There have been attempts to perform spectral unmixing using variants of least-squares techniques, but these suffer from conceptual and numerical problems which include the possibility that negative fractions of ground cover classes may be returned by the procedure. In contrast, a maximum entropy (MAXENT) based approach for sub-pixel content extraction possesses the useful information theoretic property of not assuming more information than is given, while automatically guaranteeing positive fractions. In this paper we apply MAXENT to obtain the fractions of ground cover classes present in a pixel and show its clear numerical superiority over conventional methods. The optimality of this method stems from the combinatorial properties of the information theoretic entropy.

Original languageEnglish
Pages (from-to)55-62
Number of pages8
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 1997
Externally publishedYes
Event25th AIPR Workshop: Emerging Applications of Computer Vision - Washington, DC, United States
Duration: 16 Oct 199616 Oct 1996


  • Information theory
  • Maximum entropy
  • Remote sensing
  • Spectral unmixing
  • Statistical thermodynamics


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