scholarly journals The COAST Interferometer

1994 ◽  
Vol 158 ◽  
pp. 163-166
Author(s):  
G. C. Cox

The Cambridge Optical Aperture Synthesis Telescope (COAST) [1] will be the first instrument of its kind to exploit the techniques of aperture synthesis and closure phase to produce very high resolution (one milliarcsecond) optical images. The instrument will consist of four identical independent mobile 40 cm telescopes, and an optical building incorporating the path compensators and the fringe and acquisition and auto-guider detector systems. The present status is; there are three operational telescopes on site with two fully functional path compensator trolleys, an acquisition and auto-guider system capable of controlling up to four telescopes, correlator and a fringe detector system.

2014 ◽  
Author(s):  
Yady T. Solano Correa ◽  
Francesca Bovolo ◽  
Lorenzo Bruzzone

1989 ◽  
Vol 8 (1) ◽  
pp. 78-80 ◽  
Author(s):  
T. R. Bedding ◽  
J. G. Robertson

AbstractWe propose to construct an optical interferometer to produce high resolution images by aperture synthesis. The interferometer, known as the Masked Aperture Pupil-Plane Interference Telescope (MAPPIT), will be mounted at the coudé focus of the Anglo-Australian Telescope. It will use a non-redundant aperture mask, together with closure phase methods developed for radio VLBI, to overcome the wavefront distortions which are introduced by atmospheric turbulence. By using the techniques of pupil-plane interferometry and wavelength dispersion, it is hoped that MAPPIT will have more sensitivity than many other interferometric imaging projects.


Author(s):  
Y. Tanguy ◽  
J. Michel ◽  
G. Salgues

Abstract. This paper presents a method to perform automatic vector-to-image registration. The proposed method performs well on different kinds of optical satellite images from Very High Resolution (VHR, sub-meter resolution) to images in the 10/20 m resolution range. It allows to automatically register vector dataset such as urban maps (by using building layers). In contrast with existing methods, our method needs few prior-knowledge on the features to match and can therefore adapt to different landscapes.This paper demonstrates the method robustness in several use-cases and presents the implementation which will soon be available as open-source software.


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