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Methodology development for the extraction of cartographic features from digital images of the surfaces of the planets Earth, Mars and Mercury


Remote Sensing Images have long been used to support mapping. These images are used from Digital Image Processing techniques (DIP). In this context, this project aims to continue the proposition of integrated methodologies for the extraction and / or detection and treatment of cartographic features using Digital Image Processing techniques including Mathematical Morphology of planetary surfaces. The result of the extraction and / or detection will be subjected to quality control in order to verify the accuracy and precision obtained with the application of the proposed method and to demonstrate its potential use in the field of cartography. Digital images of the surfaces of the planets Mars, Mercury and Earth have been used. In the case of the Earth's surface, methods will be developed and / or implemented using input processed images using Principal Components. The results will be compared with those obtained in previous projects that have used the single band in the experiments in the case of the extraction targets on the Earth's surface. Concerning the Martian surface the focus will be on the extraction and / or detecting slope streaks and regarding Mercury's surface a methodology for the extraction and / or automated detection of impact craters will be developed. The continuity of the researches through this new project will enable the validation of the methodology already proposed in previous projects, and the development of new methodologies for the extraction of impact craters on Mercury and slope streaks on Mars. The results to be obtained will provide the publication of articles in arbitrated scientific journals worldwide.KEYWORDS: Mathematical Morphology, Remote Sensing, Features extraction, Digital Images, Earth, Mars, Mercury. (AU)

Articles published in Pesquisa FAPESP Magazine about the research grant:
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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
NEGRI, ROGERIO GALANTE; DA SILVA, ERIVALDO ANTONIO; CASACA, WALLACE. Inducing Contextual Classifications With Kernel Functions Into Support Vector Machines. IEEE Geoscience and Remote Sensing Letters, v. 15, n. 6, p. 962-966, JUN 2018. Web of Science Citations: 2.

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