A new criterion for classifying multispectral remote sensing images or textured images by using spectral and spatial information is proposed. The images are modeled with a hierarchical Markov Random Field (MRF) model that consists of the observed intensity process and the hidden class label process. The class labels are estimated according to the maximum a posteriori (MAP) criterion, but some reasonable approximations are used to reduce the computational load. A stepwise classification algorithm is derived and is confirmed by simulation and experimental results.

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We propose a spatial contextual classification system for remote sensing images. In the system the observed multispectral images are modeled with a multivariate Gaussian Markov Random Field (GMRF) model and the hidden classified image is modeled with another type of MRF model. The classification is carried out from the viewpoint of Maximum a Posteriori (MAP) estimation. One of the well-known problems of MAP estimation is its high computational complexity involved. One way to avoid this problem is a pixelwise classification that is successfully implemented on a computer with a clique-type block matrix notation of a multivariate GMRF local conditional density function (LCDF). The proposed system is applied to real remote sensing data. INTRODUCTIONAs a result of recent development in remote sensing technologies, quite high-dimensional multispectral image data are now available. To analyze these data, it is important to classify each pixel into an appropriate class, however the conventional pixelwise spectral-base classification methods sometimes bring about misclassifications because of not considering spatial correlations. Thus spatial contextual classification methods using both spatial and spectral information is desirable to ameliorate classification accuracy. One of the easiest way to implement a spatial contextual classification is to operate a smoothing spatial filter as a preprocessing or postprocessing, however it is difficult to find any physical justification of using such a filter. Accordingly we developed a classification system using stochastic models like the Markov Random Field (MRF) that enable us to utilize both spatial and spectral information simultaneously (see also [SI).Over the last decade MRF models have been utilized in the fields of image restoration, classification and segmentation. Some researchers discussed the mathematical properties of MRF models and many concrete energy functions were introduced. The Gaussian MRF (GMRF) model is one of a variety of MRF models and various properties of multivariate GMRF model were minutely studied. This paper is organized as follows: A two-component MRF system is proposed in Section 2. Then the parameter estimation methods are developed in Section 3. In Section 4, we describe the image classification criterion and the experimental results are given in Section 5.