The CAFADIS camera is a new sensor patented by Universidad de La Laguna (Canary Islands, Spain): international patent PCT/ES2007/000046 (WIPO publication number WO/2007/082975). It can measure the wavefront phase and the distance to the light source at the same time in a real time process. This could be really useful when using Adaptive Optics with Laser Guide Stars, in order to know the LGS height variations during the observation, or even the 3D LGS profile at Na layer.
The CAFADIS camera has been designed using specialized hardware: Graphical Processing Units (GPUs) and Field Programmable Gates Arrays (FPGAs). These two kinds of electronic hardware present an architecture capable of handling the sensor output stream in a massively parallel approach. Previous papers have shown their ability for AO in ELTs.
CAFADIS is composed, essentially, by a microlenses array at the telescope image space, sampling the image instead of the telescope pupil. Conceptually, when only 2x2 microlenses are presented it is very similar to the pyramid sensor. But in fact, this optical design can be used to measure distances in the object space using a variety of techniques.
Our paper shows a simulation of an observation using Na-LGS and Raylegh-LGS at the same time, where both of the LGS heights are accurately measured. The employed techniques are presented and future applications are introduced.
Change detection is an important part of many remote sensing applications. This paper addresses the problem of unsupervised pixel classification into 'Change' and 'No Change' classes based on Hidden Markov Random Field (HMRF) models. HMRF models have long been recognized as a method to enforce spatially coherent class assignment. The optimal classification under these models is usually obtained under the Maximum a Posteriori (MAP) criterion. However, the MAP classification in HMRF models leads in general to problems with exponential complexity, so approximate techniques are needed. In this paper we show that the simple structure of the change detection problem makes that MAP classification can be exactly and efficiently calculated using graph cut techniques. Another problem related to HMRF modelling (and to any change detection technique) is the determination of the parameters or thresholds for classification. This learning problem is solved in our HMRF model by the Expectation Maximization (EM) algorithm. Experimental results obtained on four sets of multispectral remote sensing images confirm the validity of the proposed approach.