In this article, we present another way to acquire centimetric
data using a quad-rotor UAV and the specific tools developed
to perform near real time cartography. After setting
the context of the UAVs, we will present the design and the
realization of our quad-rotor Vertical Take Off and Landing
solution and it's capacity for Very High Resolution imagery.
Then we will describe the tools we developed to improve its
ability for operational mapping : how to perform flight in
immersion with a customizable HUD that takes the video
broadcast from the UAV and adds vector information in
real-time on virtual reality goggles, how to combine satellite
and vector data with path optimization algorithm to design
relevant flight plans and update them in real time to ensure
data completeness, how to generate relevant geolocalization
meta-data to be able to navigate in the produced image
database few minutes after the landing and finally, how to
employ home-made open-source mosaicer to take advantage
of the three simultaneous on-board digital camera.
The aim of this article is to estimate the revisiting time of an observated site. The revisiting time is the time elapsed between two successive possibilities of taking an image the same site by the same satellite. First, we suppose that the satellite has a cyclical and heliosynchronical orbit and that the imaging captor observes perpendicularly to the motion of the satellite. Then, we talk about how we can use the results of this algorithm to estimate the revisiting time without a computer and under the constraint of obtaining an image without a great geometrical deformation. To do this, we use the curves calculated by our algorithm and which have in x-axis the latitude of the observated site and in y-axis the view angle of the image. These results could be a great help for scientits who need to simply estimate the operationnal potential of a particular satellite in the field of short revisiting time applications.
Due to restricted visibility time of remote sensing polar platforms from earth reception station, on ly a limited number of images can be transmitted. On the case of optical images, an in- board cloud cover detection module will allow to transmit only useful images. In order to derive such a module, we propose a method to detect cloudy areas from subsampled images. For a pixel ground surface of about 110 by 100 m<SUP>2</SUP>, cloudy areas appear as the highest radiometric value homogeneous areas. The algorithm presented in this paper is based on the k-means Method. Its main originality is to improve classical results by introducing isotropic spatial information. Input data are the sorted components of a vector composed of radiometric values for each pixel and its neighbors. Then a classical k-means method with constraints on the cloudy class gravity center is used on these vectors. We tested the method on a set of 206 subsampled SPOT XS and 138 SPOT P images and their manmade interpretation masks. To evaluate the quality of our results, we used the probability of false alarm (PFA) depending on the number of pixels which have been wrongly declared cloudy. We obtained rather good PFA and PND, and compared these values with result obtained with other methods.