A novel flowchart for small-size objects identification in satellite images of insufficient resolution within the graphic reference images database using neural network technology based on compromise contradiction, i.e. simultaneously the resolution enhancement of the object segment of input image and the resolution reduction of the reference image to joint resolution through the simulation of the imaging system has been proposed. This is necessary due to a significant discrepancy between the resolutions of the input image and the graphic reference images used for identification. The required level of resolution enhancement for satellite images, as a rule, is unattainable, and a significant coarsening of reference images is undesirable because of identification errors. Therefore, a certain intermediate spatial resolution is used for identification, which, on the one hand, can be obtained, and on the other the loss of information contained in the reference image is still acceptable. The intermediate resolution is determined by simulating the process of image acquisition with satellite imaging system. To facilitate such simulation, it is advisable to perform it in the frequency domain, where the advanced Fourier analysis is available and, as a rule, all the necessary transfer properties of the links of image formation chain are known. Three main functional elements are engaged for identification: an artificial neural network for the resolution enhancement of input images, a module of frequency-domain simulating of the graphical reference satellite imaging and an artificial neural network for comparing the enhanced object segment with the reference model images. The feasibility of the described approach is demonstrated by the example of successful identification of the sea vessel image in the SPOT-7 satellite image. Currently, the works are under way to compare the performance of a neural network platforms variety for small-size objects identification in satellite images aa well as to assess achievable accuracy.
The paper provides a model for selecting satellite systems for optical observation of the Earth by the probability of recognizing objects. The model is based on improved rules for selecting satellites by the spatial resolution of onboard imaging system. The model advantage over the known ones is the satellite systems selection not only due to the spatial resolution of image, but also taking into account the predictable contrast of objects and the required recognition level. The proposed model ensures a more correct pre-selection of satellite systems, in doing so reducing the cost of satellite imaging by preventing extra-requirements for spatial resolution of onboard imaging system. Also, the article proposes a method of forming a database of radiometric contrasts of typical objects and backgrounds for their consideration in the model for choosing satellite systems. This method does not depend on the specific types of on-board equipment, as well as on the availability of archival images at the time of planning satellite imagery.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.