Land-Cover Classification Using Self-Organizing Maps Clustered with Spectral and Spatial Information

Digital classification methods of remotely sensed images have acquired a growing importance in the automatic recognition of the land cover patterns. The enormous quantity of images that are being generated from an increasing number of highly sophisticated sensor systems require the development of innovative classification methodologies, which allow an automatic and efficient detection of the great volume of data available in the images and at the same time makes the mapping process of terrestrial surfaces less subjective and with greater potential for reuse in subsequent situations. Particularly, unsupervised classification methods have traditionally been considered as an important approach for the interpretation of remotely sensed images. This approach of classification plays an especially significant role when very little a priori information about image data is available, and for that reason continues to be a popular choice for analysts without ample field knowledge or for those wanting to avoid introduced bias in classification analysis (Duda & Canty, 2002; Kelly et al., 2004). Unsupervised classification is frequently performed through clustering methods. These methods examine the unknown pixels in an image and incorporate them into a set of classes defined through the natural clusters of the gray levels of the pixels. Cluster analysis provides a practical method for organizing a large set of data so that the retrieval of information may be made more efficiently. However, although there is a large quantity of different clustering methods in the pattern recognition area (Xu & Wunsch II, 2005), only a limited quantity of them can be used in remote sensing applications. As pointed out in Tran et. al. (2005), several problems are encountered when clustering remotely sensed images, but above all the image size and the feature dimension problems are those that often make a method inappropriate due to computation time and computer memory. The most common clustering method applied to remotely sensed data is partitional. The majority of software or computational systems meant for the digital processing of remotely