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

Ryuei Nishii, Shinto Eguchi

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)


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. In a paradigm of supervised learning, AdaBoost was proposed as a machine learning technique in Ref. [10] and has been widely and rapidly improved for use in pattern recognition. AdaBoost linearly combines several weak classifiers into a strong classifier. The coefficients of the classifiers are tuned by minimizing an empirical exponential risk. The classification method exhibits high performance in various fields [11,12]. In addition, fusion techniques have been discussed [13-15]. In the present chapter, we consider contextual classification methods based on statistics and machine learning. We review AdaBoost with binary class labels as well as multi-class labels. The procedures for deriving coefficients for classifiers are discussed, and robustness for loss functions is emphasized here. Next, contextual image classification methods including Switzer’s smoothing method [1], MRF-based methods [16], and spatial boosting [2,17] are introduced. Relationships among them are also pointed out. Spatial parallel boost by meta-learning for multi-source and multi-temporal data classification is proposed. The remainder of the chapter is organized as follows. In Section 4.2, AdaBoost is briefly reviewed. A simple example with binary class labels is provided to illustrate AdaBoost. Then, we proceed to the case with multi-class labels. Section 4.3 gives general boosting methods to obtain the robustness property of the classifier. Then, contextual classifiers including Switzer’s method, an MRF-based method, and spatial boosting are discussed. Relationships among them are shown in Section 4.5. The exact error rate and the properties of the MRF-based classifier are given. Section 4.6 proposes spatial parallel boost applicable to classification of multi-source and multi-temporal data sets. The methods treated here are applied to a synthetic data set and two benchmark data sets, and the performances are examined in Section 4.7. Section 4.8 concludes the chapter and mentions future problems.

Original languageEnglish
Title of host publicationImage Processing for Remote Sensing
PublisherCRC Press
Number of pages28
ISBN (Electronic)9781420066654
ISBN (Print)1420066641, 9781420066647
Publication statusPublished - Jan 1 2007

All Science Journal Classification (ASJC) codes

  • General Engineering
  • General Earth and Planetary Sciences


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