25,451 research outputs found
Supervised learning on graphs of spatio-temporal similarity in satellite image sequences
High resolution satellite image sequences are multidimensional signals
composed of spatio-temporal patterns associated to numerous and various
phenomena. Bayesian methods have been previously proposed in (Heas and Datcu,
2005) to code the information contained in satellite image sequences in a graph
representation using Bayesian methods. Based on such a representation, this
paper further presents a supervised learning methodology of semantics
associated to spatio-temporal patterns occurring in satellite image sequences.
It enables the recognition and the probabilistic retrieval of similar events.
Indeed, graphs are attached to statistical models for spatio-temporal
processes, which at their turn describe physical changes in the observed scene.
Therefore, we adjust a parametric model evaluating similarity types between
graph patterns in order to represent user-specific semantics attached to
spatio-temporal phenomena. The learning step is performed by the incremental
definition of similarity types via user-provided spatio-temporal pattern
examples attached to positive or/and negative semantics. From these examples,
probabilities are inferred using a Bayesian network and a Dirichlet model. This
enables to links user interest to a specific similarity model between graph
patterns. According to the current state of learning, semantic posterior
probabilities are updated for all possible graph patterns so that similar
spatio-temporal phenomena can be recognized and retrieved from the image
sequence. Few experiments performed on a multi-spectral SPOT image sequence
illustrate the proposed spatio-temporal recognition method
Terrestrial applications: An intelligent Earth-sensing information system
For Abstract see A82-2214
Monitoring land use changes using geo-information : possibilities, methods and adapted techniques
Monitoring land use with geographical databases is widely used in decision-making. This report presents the possibilities, methods and adapted techniques using geo-information in monitoring land use changes. The municipality of Soest was chosen as study area and three national land use databases, viz. Top10Vector, CBS land use statistics and LGN, were used. The restrictions of geo-information for monitoring land use changes are indicated. New methods and adapted techniques improve the monitoring result considerably. Providers of geo-information, however, should coordinate on update frequencies, semantic content and spatial resolution to allow better possibilities of monitoring land use by combining data sets
Applications of ISES for vegetation and land use
Remote sensing relative to applications involving vegetation cover and land use is reviewed to consider the potential benefits to the Earth Observing System (Eos) of a proposed Information Sciences Experiment System (ISES). The ISES concept has been proposed as an onboard experiment and computational resource to support advanced experiments and demonstrations in the information and earth sciences. Embedded in the concept is potential for relieving the data glut problem, enhancing capabilities to meet real-time needs of data users and in-situ researchers, and introducing emerging technology to Eos as the technology matures. These potential benefits are examined in the context of state-of-the-art research activities in image/data processing and management
Data compression for satellite images
An efficient data compression system is presented for satellite pictures and two grey level pictures derived from satellite pictures. The compression techniques take advantages of the correlation between adjacent picture elements. Several source coding methods are investigated. Double delta coding is presented and shown to be the most efficient. Both predictive differential quantizing technique and double delta coding can be significantly improved by applying a background skipping technique. An extension code is constructed. This code requires very little storage space and operates efficiently. Simulation results are presented for various coding schemes and source codes
Pre-processing for approximate Bayesian computation in image analysis
Most of the existing algorithms for approximate Bayesian computation (ABC)
assume that it is feasible to simulate pseudo-data from the model at each
iteration. However, the computational cost of these simulations can be
prohibitive for high dimensional data. An important example is the Potts model,
which is commonly used in image analysis. Images encountered in real world
applications can have millions of pixels, therefore scalability is a major
concern. We apply ABC with a synthetic likelihood to the hidden Potts model
with additive Gaussian noise. Using a pre-processing step, we fit a binding
function to model the relationship between the model parameters and the
synthetic likelihood parameters. Our numerical experiments demonstrate that the
precomputed binding function dramatically improves the scalability of ABC,
reducing the average runtime required for model fitting from 71 hours to only 7
minutes. We also illustrate the method by estimating the smoothing parameter
for remotely sensed satellite imagery. Without precomputation, Bayesian
inference is impractical for datasets of that scale.Comment: 5th IMS-ISBA joint meeting (MCMSki IV
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