Unlike conventional unsupervised classification methods, such as K-means and ISODATA, which are based on partitional clustering techniques, the methodology proposed in this work attempts to take advantage of the properties of Kohonen's self-organizing map (SOM) together with agglomerative hierarchical clustering methods to perform the automatic classification of remotely sensed images. The key point of the proposed method is to execute the cluster analysis process by means of a set of SOM prototypes, instead of working directly with the original patterns of the image. This strategy significantly reduces the complexity of the data analysis, making it possible to use techniques that have not normally been considered viable in the processing of remotely sensed images, such as hierarchical clustering methods and cluster validation indices. Through the use of the SOM, the proposed method maps the original patterns of the image to a two-dimensional neural grid, attempting to preserve the probability distribution and topology of the input space. Afterwards, an agglomerative hierarchical clustering method with restricted connectivity is applied to the trained neural grid, generating a simplified dendrogram for the image data. Utilizing SOM statistic properties, the method employs modified versions of cluster validation indices to automatically determine the ideal number of clusters for the image. The experimental results show examples of the application of the proposed methodology and compare its performance to the K-means algorithm.

Determining the structure of data without prior knowledge of the number of clusters or any information about their composition is a problem of interest in many fields, such as image analysis, astrophysics, biology, etc. Partitioning a set of n patterns in a ^-dimensional feature space must be done such that those in a given cluster are more similar to each other than the rest. As there are approximately ^-possible ways of partitioning the patterns among K clusters, finding the best solution is very hard when n is large. The search space is increased when we have no a priori number of partitions. Although the self-organizing feature map (SOM) can be used to visualize clusters, the automation of knowledge discovery by SOM is a difficult task. This paper proposes region-based image processing methods to post-processing the U-matrix obtained after the unsupervised learning performed by SOM. Mathematical morphology is applied to identify regions of neurons that are similar. The number of regions and their labels are automatically found and they are related to the number of clusters in a multivariate data set. New data can be classified by labeling it according to the best match neuron. Simulations using data sets drawn from finite mixtures of p-variate normal densities are presented as well as related advantages and drawbacks of the method.

Self-organizing map has been applied to a variety of tasks including data visualization and clustering. Once the point density of the neurons approximates the density of data, it is possible to miner clustering information from the data set after its unsupervised learning by using the neuron's relations. This paper presents a new algorithm for dynamical generation of a hierarchical structure of selforganizing maps with applications to data analysis. Di' erently from other tree-structured SOM approaches, which nodes are neurons, in this case the tree nodes are actually maps. From top to down, maps are automatically segmented by using the U-matrix information, which presents relations between neighboring neurons. The automatic map partitioning algorithm is based on mathematical morphology segmentation and it is applied to each map in each level of the hierarchy. Clusters of neurons are automatically identified and labeled and generate new sub-maps. Data are partitioned accordingly the label of its best match unit in each level of the tree. The algorithm may be seen as a recursive partition clustering method with multiple prototypes cluster representation, which enables the discoveries of clusters in a variety of geometrical shapes.

A fertirrigação tem sido prática muito empregada em cultivos mais tecnificados, principalmente na fase de produção, mas pode ser utilizada na etapa de produção de mudas, desde que seja bem planejada quanto à concentração de nutrientes na solução nutritiva. Este trabalho foi desenvolvido com o objetivo de avaliar a produção de mudas de cinco cultivares de pimentão utilizando substrato de fibra de coco e fertirrigadas com diferentes soluções nutritivas. Utilizou-se o delineamento inteiramente casualizado, em esquema fatorial 5 × 5, com quatro repetições. Os tratamentos resultaram da combinação de cinco cultivares de pimentão (Amarelo SF 134, Cascadura Ikeda, Yolo Wonder, Rubi Gigante e All Big) com cinco concentrações de solução nutritiva (25, 50, 75, 100 e 125%), utilizando o sistema floating. As mudas foram avaliadas quanto aos parâmetros de desenvolvimento: altura de mudas, número de folhas, comprimento da raiz principal, diâmetro do caule e massa seca total. Todas as variáveis foram afetadas pelas soluções nutritivas, no entanto as respostas foram diferentes de acordo com a cultivar avaliada. As cultivares Amarelo SF 134, Cascadura Ikeda, Yolo Wonder e Rubi Gigante apresentaram mudas mais vigorosas que a cultivar All Big. Mudas de pimentão de melhor qualidade para as cultivares estudadas são obtidas com fertirrigação utilizando solução nutritiva na concentração variando de 70 a 80% da solução recomendada para a produção de mudas de pimentão.

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