Supervised image classification of multi-spectral images based on statistical machine learning

Ryuei Nishii, Shinto Eguchi

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

Abstract

Image classification for geostatistical data is one of the most important issues in the remote-sensing community. Statistical approaches have been discussed extensively in the literature. In particular, Markov random fields (MRFs) are used for modeling distributions of land-cover classes, and contextual classifiers based on MRFs exhibit efficient performances. In addition, various classification methods were proposed. See Ref. [3] for an excellent review paper on classification. See also Refs. [1,4-7] for a general discussion on classification methods, and Refs. [8,9] for backgrounds on spatial statistics.

Original languageEnglish
Title of host publicationSignal and Image Processing for Remote Sensing
PublisherCRC Press
Pages345-371
Number of pages27
ISBN (Electronic)9781420003130
ISBN (Print)0849350913, 9780849350917
Publication statusPublished - Jan 1 2006

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Earth and Planetary Sciences(all)

Fingerprint Dive into the research topics of 'Supervised image classification of multi-spectral images based on statistical machine learning'. Together they form a unique fingerprint.

  • Cite this

    Nishii, R., & Eguchi, S. (2006). Supervised image classification of multi-spectral images based on statistical machine learning. In Signal and Image Processing for Remote Sensing (pp. 345-371). CRC Press.