The U.S. Navy has plans to develop an automated system to analyze satellite imagery aboard its ships at sea. Lack of time for training, in combination with frequent personnel rotations, precludes the building of extensive imagery interpretation expertise by shipboard personnel. A preliminary design starts from pixel data from which clouds are classified. An image segmentation is performed to assemble and isolate cloud groups, which are then identified (e.g., as a cold front) using neural networks. A combination of neural networks and expert systems is subsequently used to transform key information about the identified cloud patterns as inputs to an expert system that provides sensible weather information, the ultimate objective of the imagery analysis.

Toward Automated Interpretation of Satellite Imagery for Navy Shipboard Applications

Cloud detection in satellite images with classical and deep neural network approach: A review

This study compares four autonomous navigation architectures for mobile robots, in order to determinate which has a greater efficiency in tasks solving. These architectures are: reactive, neuronal network, fuzzy logic and neuro-fuzzy hybrid architecture. The contrast within architectures was assessed collecting data of performance metrics based on safety, smoothness of curve (energy consumption), speed and distance. The comparison was done using the robotic platform, Pioneer 3-DX within a simulated environment of MobileSim, the official software of MobileRobot®. The collected data were statistically analyzed, generating, therefore a formal base of comparison. The outcomes of this procedure indicate that neuro-fuzzy architecture presents higher levels of security and energy efficiency in contrast with other architectures. Thus, using synergic architectures allows achieving a greater efficiency.

/pdf/estudio-comparativo-basado-en-metricas-para-diferentes-56fpwt7hhz.pdf

Estudio comparativo basado en métricas para diferentes arquitecturas de navegación reactiva Comparative study based on metrics for various reactive navigation architectures

espanolEl presente estudio compara cuatro arquitecturas de navegacion autonoma para robots moviles, con el fin de determinar cual presenta una mayor eficiencia en la realizacion de tareas. Las arquitecturas comparadas son: reactivo puro, redes neuronales, logica difusa y una arquitectura hibrida neuro-difusa. La comparacion se realizo recopilando datos de las metricas de desempeno basadas en seguridad, suavidad de curva (consumo energetico), velocidad y distancia, en la plataforma robotica Pioneer 3-DX en un entorno simulado con MobileSim, el software oficial de MobileRobot®. Los datos obtenidos fueron analizados estadisticamente, generando una base formal de comparacion. Los resultados muestran que la arquitectura neuro-difusa presenta mayores niveles de seguridad y ahorro energetico que las otras arquitecturas, demostrando asi, que la utilizacion de arquitecturas en forma sinergica logra mayor eficiencia. EnglishThis study comparesfour autonomous navigation architecturesfor mobile robots, in order to determinate which has a greater efficiency in tasks solving. These architectures are: reactive, neuronal network,fuzzy logic and neuro-fuzzy hybrid architecture. The contrast within architectures was assessed collecting data ofperformance metrics based on safety, smoothness of curve (energy consumption), speed and distance. The comparison was done using the robotic platform, Pioneer 3-DX within a simulated environment of MobileSim, the official software of MobileRobot®. The collected data were statistically analyzed, generating, therefore a formal base of comparison. The outcomes of this procedure indicate that neuro-fuzzy architecture presents higher levels of security and energy efficiency in contrast with other architectures. Thus, using synergic architectures allows achieving a greater efficiency.

/pdf/estudio-comparativo-basado-en-metricas-para-diferentes-pefo11advc.pdf

Estudio comparativo basado en métricas para diferentes arquitecturas de navegación reactiva

: The Hierarchical Stepwise Optimization algorithm of Beaulieu and Goldberg (1989) is applied to the segmentation of satellite images into meaningful, large scale cloud features. GOES-W visible imagery is used. Several different forms of the cost function are explored in an attempt to improve the segmentation of Nov. 15, 1983 case. A new cost function is shown to result in a superior segmentation. Both HSWO versions are tested on six additional cases. A modification to the HSWO approach is suggested for further research.

Use of Hierarchical Stepwise Optimization for the Segmentation of Cloud Features

: Six artificial neural network models are explored as potential methodologies for the automated interpretation of satellite cloud images. The Multi-layer Perceptron Network is chosen to be the most applicable to the image interpretation problem. A complete, mathematical description of the methodology is presented. The neural network's classification capability is demonstrated using simple geometric patterns and alphabetic characters. A more complex test using GOES infrared imagery shows that the neural network can distinguish 53 of 54 large-scale cloud patterns. An architecture for a complete, automated cloud feature recognition system is proposed.

Neural Network Methodologies and Their Potential Application to Cloud Pattern Recognition.

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Neural Network Methodologies and Their Potential Application to Cloud Pattern Recognition.