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Most pattern recognition applications within the Geoscience field involve the clustering and classification of remote sensed multispectral data, which basically aims to allocate the right class of ground category to a reflectance or radiance signal. Generally, the complexity of this problem is related to the incorporation of spatial characteristics that are complementary to the nonlinearities of land surface heterogeneity, remote sensing effects and multispectral features. The present chapter describes recent developments in the performance of a kernel method applied to the representation and classification of agricultural land use systems described by multispectral responses. In particular, we focus on the practical applicability of learning machine methods to the task of inducting a relationship between the spectral response of farms land cover to their informational typology from a representative set of instances. Such methodologies are not traditionally used in agricultural studies. Nevertheless, the list of references reviewed here show that its applications have emerged very fast and are leading to simple and theoretically robust classification models. This chapter will cover the following phases: a)learning from instances in agriculture; b)feature extraction of both multispectral and attributive data and; c) kernel supervised classification. The first provides the conceptual foundations and a historical perspective of the field. The second belongs to the unsupervised learning field, which mainly involves the appropriate description of input data in a lower dimensional space. The last is a method based on statistical learning theory, which has been successfully applied to supervised classification problems and to generate models described by implicit functions.

Using Kernel Methods in a Learning Machine Approach for Multispectral Data Classification. An Application in Agriculture