Identification of primary aberrations on a lateral shearing interferogram of optical components using neural network
A method is proposed for the identification of primary aberrations with a single image of a lateral shearing interferogram of optical components, obtained from a monochromatic laser source, using a neural network. The neural network is used to illustrate the features of the sampled interferograms and identify the primary aberrations of the other interferograms by means of its excellence in interpolation and extrapolation. Analysis of the pattern is performed to find the proper features to teach the neural network in less time. Simulations are performed to verify the suggested method.
This paper analyzes the use of CBERS (China-Brazil Earth Resources Satellite) high resolution images to map urban limits of a small city in the Amazon Region of Brazil. The use of Sino-Brazilian satellite images to study small cities of the Brazilian Amazon Region is also discussed. A HCR image was used to classify the image of the study area into four classes: urban, forest, clouds, and shadows, by visual interpretation. This map was compared with another map produced with unsupervised classification. A confusion matrix was produced to evaluate the accuracy of unsupervised classification. Comparison between the reference map (visual interpretation) and the unsupervised classification map verified that 82.8% of the areas were classified correctly. This ability to recognize and delimitate small cities in Amazonia demonstrates the quality of the CBERS – HCR image.