Improved interpretation of satellite altimeter data using genetic algorithms

Genetic algorithms (GA) are optimization techniques that are based on the mechanics of evolution and natural selection. They take advantage of the power of cumulative selection, in which successive incremental improvements in a solution structure become the basis for continued development. A GA is an iterative procedure that maintains a 'population' of 'organisms' (candidate solutions). Through successive 'generations' (iterations) the population as a whole improves in simulation of Darwin's 'survival of the fittest'. GA's have been shown to be successful where noise significantly reduces the ability of other search techniques to work effectively. Satellite altimetry provides useful information about oceanographic phenomena. It provides rapid global coverage of the oceans and is not as severely hampered by cloud cover as infrared imagery. Despite these and other benefits, several factors lead to significant difficulty in interpretation. The GA approach to the improved interpretation of satellite data involves the representation of the ocean surface model as a string of parameters or coefficients from the model. The GA searches in parallel, a population of such representations (organisms) to obtain the individual that is best suited to 'survive', that is, the fittest as measured with respect to some 'fitness' function. The fittest organism is the one that best represents the ocean surface model with respect to the altimeter data

SOFT project: a new forecasting system based on satellite data

En: Conference Remote Sensing of the Ocean and Sea Ice 2001, 20-09-2001, Toulouse, France. Eds. Charles R. Bostater, Jr., Rosalia Santoleri.-- 13 pages, 12 figures, 1 table.-- Published Online: 7 April 2003.-- Pre-print archive: http://wwwimedea.uib.es/oceanography/projects/soft/The aim of the SOFT project is to develop a new ocean forecasting system by using a combination of satellite data, evolutionary programming and numerical ocean models. To achieve this objective two steps are proposed: (1) to obtain an accurate ocean forecasting system using genetic algorithms based on satellite data; and (2) to integrate the above new system into existing deterministic numerical models. Evolutionary programming will be employed to build “intelligent” systems that, learning from the past ocean variability (provided by satellite data) and considering the present ocean state, will be able to infer near future ocean conditions. Validation of the forecast skill will be carried out by comparing the forecasts fields with satellite and in situ observations. Validation with satellite observations will provide the expected errors in the forecasting system. Validation with in situ data will indicate the capabilities of the satellite based forecast information to improve the performance of the numerical ocean models. This later validation will be accomplished considering in situ measurements in a specific oceanographic area at two different periods of time. The first set of observations will be employed to feed the hybrid systems while the second set will be used to validate the hybrid and traditional numerical model results.This work has been carried out as part of the SOFT project funded by the E. C. under contract: EVK3-CT-2000-00028. Ananda Pascual holds a doctoral fellowship from Universitat de les Illes Balears. We thank Vicente Fernandez for his fruitful comments on the interpretation of EOFs patterns related to the Mediterranean circulation.Peer reviewe

Automated Classification of Airborne Laser Scanning Point Clouds

Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly "see" more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protection and forestry to threat mitigation. In order to process these large quantities of data, novel methods are required. In this contribution, we develop models to automatically classify ground cover and soil types. Using the logic of machine learning, we critically review the advantages of supervised and unsupervised methods. Focusing on decision trees, we improve accuracy by including beam vector components and using a genetic algorithm. We find that our approach delivers consistently high quality classifications, surpassing classical methods

Agricultural Research Service research highlights in remote sensing for calendar year 1981

Selected examples of research accomplishments related to remote sensing are compiled. A brief statement is given to highlight the significant results of each research project. A list of 1981 publication and location contacts is given also. The projects cover emission and reflectance analysis, identification of crop and soil parameters, and the utilization of remote sensing data