This paper describes the use of the Robocode platform for algorithms and programming languages teaching in high school. The experience was developed with the help of undergraduate students that had already concluded courses on computer programming and acted as tutors and co-advisors of the younger students. Robocode environment was used as a support tool, using a playful learning strategy, providing an early contact of the high-school students with Java programming language. Besides programming learning, results include higher motivation in high school students to learn computer programming and the development of leadership abilities in undergraduate students, that came from tutoring and co-advising.Resumo. Esse artigo descreve a experiência do uso da ferramenta Robocode no ensino de algoritmos e programação de computadores para alunos do Ensino Médio. O estudo envolveu também alunos de graduação que já haviam cursado disciplinas de programação de computadores e que atuaram como tutores e coorientadores dos alunos. O ambiente Robocode foi utilizado como ferramenta de apoio ao ensino de algoritmos e linguagens de programação, com base em uma estratégia lúdica de ensino, sugerindo um contato mais precoce com a linguagem de programação Java, ainda no Ensino Médio, a fim de estimular o raciocínio lógico-matemático dos envolvidos de forma rápida e divertida. Além do aprendizado de programação de computadores pelos alunos, os resultados observados incluem o aumento do interesse pela programação nos alunos do Ensino Médio e o desenvolvimento da capacidade de liderança dos alunos de graduação, proveniente da experiência em coorientar os alunos aprendizes.

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This work presents a case study in land cover classification using ms-NN, an extension of k-NN classification algorithm. The case study focuses on an area in the Brazilian Amazon region, with data obtained from LANDSAT5 satellite Thematic Mapper (TM) sensor and Advanced Land Observing System satellite (ALOS) Phase Array L-Band Synthetic Aperture Radar (PALSAR), using Fine Beam Dual. The results obtained with ms-NN are compared with k-NN and Support Vector Machine algorithms, considering the use of a single training set, a Monte Carlo procedure for testing and an extensive number of parameterizations for the classification methods. Considering only the best results for each classifier, ms-NN obtained better results than the other methods.Index Termsland cover classification, multiple space nearest neighbor, classification algorithm, SAR and optical data classification.