A.R.L. Tatnall

Harnessing High-Altitude Solar Power

As an intermediate solution between Glasers satellite solar power (SSP) and ground-based photovoltaic (PV) panels, this paper examines the collection of solar energy using a high-altitude aerostatic platform. A procedure to calculate the irradiance in the medium/high troposphere, based on experimental data, is described. The results show that here a PV system could collect about four to six times the energy collected by a typical U.K.-based ground installation, and between one-third and half of the total energy the same system would collect if supported by a geostationary satellite (SSP). The concept of the aerostat for solar power generation is then briefly described together with the equations that link its main engineering parameters/variables. A preliminary sizing of a facility stationed at 6 km altitude and its costing, based on realistic values of the input engineering parameters, is then presented.

A neural network-based method for tracking features from satellitesensor images

A new approach for feature tracking on sequential satellite sensor images using neural networks has been developed. The method defines the correspondence problem between features as the minimization of a cost function using a Hopfield neural network. It has been tested on Meteosat radiometer images by tracking a cloud with rotational movement and compared to the maximum cross-correlation method. The Hopfield net was found to be more accurate and faster.

Determination of spatial and temporal characteristics as an aid to neural network cloud classification

Previous studies of cloud classification from meteorological satellite imagery have shown that artificial neural networks (ANNs) perform as well as, or better than, statistical pattern recognition when multispectral features, supplemented with selected textural features, are used. These features, however, represent only a subset of the full range of features available in this imagery. Spatial characteristics based on the shape of clouds, and temporal characteristics, derived from image sequences, can be more direct pointers to cloud type. In this paper the methods for the determination of such parameters are described, some results are presented, and the effectiveness of the methods are discussed.

Incorporating uncertainty in land cover classification from remote sensing imagery

Abstract This article reports an investigation of the sources of uncertainty arising from land cover characteristics that affect the performance of area estimation models.

A comparison of Multi-layer Perceptron and multilinear regression algorithms for the inversion of synthetic ocean colour spectra

Artificial radiance sets were used as inputs to Multi-layer Perceptron and multilinear regression algorithms to study their retrieval capabilities for optically active constituents in sea water. The radiative transfer model Hydrolight was used to produce 18,000 artificial reflectance spectra representing various case 1 and case 2 water conditions. The remote sensing reflectances were generated at the Medium Resolution Imaging Spectrometer (MERIS) wavebands 412, 442, 490, 510, 560, 620, 665 and 682 nm from randomly generated triplet combinations of chlorophyll a, non-chlorophyllous particles and CDOM (Coloured Dissolved Organic Matter) concentrations. These reflectances were contaminated with different noise terms, before they were used to assess the performance of multilayer perceptron and multilinear regression algorithms. The potential of both algorithms for retrieving optically active constituents was demonstrated with the neural network showing more accurate results for case 2 scenarios.

Future robotic exploration using honeybee search strategy: Example search for caves on Mars

Autonomous control has an increasing role in Earth and Space based applications. High level autonomy can greatly improve planetary exploration and is, in many cases, essential. It has been suggested during the Mars cave exploration programme, that an effective way to explore a larger surface area would be the use of many, small and fully autonomous robots. However, there are many challenges to overcome if such a swarm exploration programme is to be implemented. This paper summarises these challenges and focuses on one of the most crucial one: strategy. Many effective group exploration behaviours can be observed in nature, most of which are optimised to work with agents that have limited capabilities as individuals. For this paper a computer program has been written to simulate the way bees search for new hives and investigate whenever it is an optimal method to search for cave entrances on Mars. It has been found that this method, using simple autonomous robots which can be constructed using available technologies, could greatly improve the speed and range of a planetary exploration mission. The simulation results show that 50 swarm robots can cover an area of over 300 meters square completely in 5 sols while they are searching for cave entrances and returning results to the Lander which is a major performance improvement on any previous mission. Furthermore areas of interests found by the explorers are sorted in order of importance automatically and without the need of computational analysis, hence larger quantities of data were collected from the more important areas. Therefore the system – just like a hive of bees – can make a complex decision easily and quickly to find the place which matches the required criteria best. Using a high performance search strategy such as the one described in this paper is crucial if we plan to search for important resources or even life on Mars and other bodies in the solar system.

Neural network training: Using untransformed or log‐transformed training data for the inversion of ocean colour spectra?

A bio‐optical model coupled with the radiative transfer model Hydrolight was used to create 18,000 synthetic ocean colour spectra corresponding to open ocean and coastal waters. The bio‐optical model took into account the optical properties of the three oceanic constituents, chlorophyll‐a, suspended non‐chlorophyllous particles and coloured dissolved organic matter (CDOM) as well as of normal seawater. The resulting spectra were input into multilayer perceptron neural network algorithms with the aim of computing the original concentrations of chlorophyll‐a, non‐chlorophyllous particles and CDOM initially input into the bio‐optical model. The process of training the neural networks is essential for the accuracy of the inversion the neural net performs on the coupled bio‐optical and radiative transfer models. The objective of this paper is to investigate the performance difference of a neural network trained with untransformed as opposed to logarithmically transformed data.

Prioritizing ocean colour channels by neural network input reflectance perturbation

The radiative transfer model Hydrolight was used to produce 18 000 artificial reflectance spectra representing case 1 and case 2 water conditions. Remote sensing reflectances were generated at the MERIS wavebands 412, 442, 490, 510, 560, 620, 665 and 682 nm from randomly generated triplet combinations of chlorophyll a, non‐chlorophyllous particles and coloured dissolved organic matter concentrations. These spectra were used to train multilayer perceptron neural network algorithms to perform the inversion from input reflectances to these three optically active substances. A method is proposed that establishes the neural network output error sensitivity towards changes in the individual input reflectance channels. From the output error produced for each reflectance change, a hypothesis about the importance of each band can be made. Results suggest a strong weight associated to the 620 nm band for the estimation of all three substances.

The Use of the Hopfield Neural Network to Measure Sea-Surface Velocities From Satellite Images

The knowledge of ocean surface circulation is of major importance for many applications, including the understanding of global climate, resources exploitation, and containment of chemical spills. In this letter, sea-surface feature tracking based on the Hopfield neural network (NN) is described. The method is based on the minimization of an energy function that represents the feature tracking problem. A Hopfield NN is used to merge cross-correlation information with prior knowledge of sea-surface flows and image contextual information. It has been tested on real satellite images. A set of five Advanced Very High Resolution Radiometer thermal images of the coastal zone of California, along with a data set of coincident surface drifters positions, was used to test the method. Results of the new analysis are compared with in situ data and previous results using other techniques. The method can be used on various kinds of images for tracking and also find other applications in image registration and pattern recognition.

Dimensional stability testing on a space optical bench structure

High-performance space-based optical systems typically require structures that exhibit high levels of dimensional stability over their lifetimes. To better understand the mechanisms for dimensional instability, a novel series of tests were carried out at Rutherford Appleton Laboratory on a breadboard high-stability optical bench structure. Goals of the testing were to assess the relative stability of a pair of reference surfaces and to determine the contributions of various structural elements and joints to dimensional instability. The breadboard was subject to an intensive environmental test campaign that included thermal cycling under vacuum and random vibration testing. Metrology was performed throughout the campaign to assess the dimensional stability response to the various environmental loads. The metrology requirement was challenging, with measurements of micron-level displacement and arcsecond- level tilt over 1-meter distances being necessary in situ during environmental testing. This issue was resolved using a combination of techniques: a contacting coordinate measurement machine, laser interferometry, and optical autocollimation. The greatest levels of instability were produced by random vibration testing, though evidence of a bedding-in process implies that vibratory conditioning could be used to improve stability.