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

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.