960 resultados para Automatic Gridding of microarray images
Resumo:
Context. On 12 November 2014, the European mission Rosetta delivered the Philae lander on the nucleus of comet 67P /Churyumov-Gerasimenko (67P). After the first touchdown, the lander bounced three times before finally landing at a site named Abydos. Aims. We provide a morphologically detailed analysis of the Abydos landing site to support Philae's measurements and to give context for the interpretation of the images coming from the Comet Infrared and Visible Analyser (CIVA) camera system onboard the lander. Methods. We used images acquired by the OSIRIS Narrow Angle Camera (NAC) on 6 December 2014 to perform the analysis of the Abydos landing site, which provided the geomorphological map, the gravitational slope map, the size-frequency distribution of the boulders. We also computed the albedo and spectral reddening maps. Results. The morphological analysis of the region could suggest that Philae is located on a primordial terrain. The Abydos site is surrounded by two layered and fractured outcrops and presents a 0.02 km(2) talus deposit rich in boulders. The boulder size frequency distribution gives a cumulative power-law index of 4.0 + 0.3/0.4, which is correlated with gravitational events triggered by sublimation and /or thermal fracturing causing regressive erosion. The average value of the albedo is 5.8% at lambda(1) = 480.7 nm and 7.4% at lambda(2) = 649.2 nm, which is similar to the global albedos derived by OSIRIS and CIVA, respectively.
Resumo:
This work is part of the project CAMEVA for the development of an expert system aimed at the automatic identification of ores [1, 2]. It relies on the measure of their reflectance values, R, on digital images. Software for calibration, acquisition and analysis of the multispectral data was designed by AITEMIN [3]; the research was also assessed by H.J. Bernhardt and E. Pirard [1].
Resumo:
The application of thematic maps obtained through the classification of remote images needs the obtained products with an optimal accuracy. The registered images from the airplanes display a very satisfactory spatial resolution, but the classical methods of thematic classification not always give better results than when the registered data from satellite are used. In order to improve these results of classification, in this work, the LIDAR sensor data from first return (Light Detection And Ranging) registered simultaneously with the spectral sensor data from airborne are jointly used. The final results of the thematic classification of the scene object of study have been obtained, quantified and discussed with and without LIDAR data, after applying different methods: Maximum Likehood Classification, Support Vector Machine with four different functions kernel and Isodata clustering algorithm (ML, SVM-L, SVM-P, SVM-RBF, SVM-S, Isodata). The best results are obtained for SVM with Sigmoide kernel. These allow the correlation with others different physical parameters with great interest like Manning hydraulic coefficient, for their incorporation in a GIS and their application in hydraulic modeling.