Paul M. Tag

Segmentation of Satellite Imagery Using Hierarchical Thresholding and Neural Networks

Abstract A significant task in the automated interpretation of cloud features on satellite imagery is the segmentation of the image into separate cloud features to be identified. A new technique, hierarchical threshold segmentation (HTS), is presented. In HTS, region boundaries are defined over a range of gray-shade thresholds. The hierarchy of the spatial relationships between collocated regions from different thresholds is represented in tree form. This tree is pruned, using a neural network, such that the regions of appropriate sizes and shapes are isolated. These various regions from the pruned tree are then collected to form the final segmentation of the entire image. In segmentation testing using Geostationary Operational Environmental Satellite data, HTS selected 94% of 101 dependent sample pruning points correctly, and 93% of 105 independent sample pruning points. Using Advanced Very High Resolution Radiometer data, HTS correctly selected 90% of both the 235-case dependent sample and the 253-case ...

An Automated Method to Estimate Tropical Cyclone Intensity Using SSM/I Imagery

Abstract An automated method to estimate tropical cyclone intensity using Special Sensor Microwave Imager (SSM/I) data is developed and tested. SSM/I images (512 km × 512 km) centered on a given tropical cyclone (TC), with a known best-track intensity, are collected for 142 different TCs (1988–98) from the North Pacific, Atlantic, and Indian Oceans. Over 100 characteristic features are computed from the 85-GHz (H-pol) imagery data and the derived rain-rate imagery data associated with each TC. Of the 1040 sample images, 942 are selected as training samples. These training samples are examined in a feature-selection algorithm to select an optimal subset of the characteristic features that could accurately estimate TC intensity on unknown samples in a K-nearest-neighbor (K-NN) algorithm. Using the 15 selected features as the representative vector and the best-track intensity as the ground truth, the 98 testing samples (taken from four TCs) are presented to the K-NN algorithm. A root-mean-square error (rmse)...

Toward Automated Interpretation of Satellite Imagery for Navy Shipboard Applications

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.

A meteorological fuzzy expert system incorporating subjective user input

Abstract. We present a fuzzy expert system, MEDEX, for forecasting gale-force winds in the Mediterranean basin. The most successful local wind forecasting in this region is achieved by an expert human forecaster with access to numerical weather prediction products. That forecasters knowledge is expressed as a set of ‘rules-of-thumb’. Fuzzy set methodologies have proved well suited for encoding the forecasters knowledge, and for accommodating the uncertainty inherent in the specification of rules, as well as in subjective and objective input. MEDEX uses fuzzy set theory in two ways: as a fuzzy rule base in the expert system, and for fuzzy pattern matching to select dominant wind circulation patterns as one input to the expert system. The system was developed, tuned, and verified over a two-year period, during which the weather conditions from 539 days were individually analyzed. Evaluations of MEDEX performance for both the onset and cessation of winter and summer winds are presented, and demonstrate that MEDEX has forecasting skill competitive with the US Navys regional forecasting center in Rota, Spain.

A fuzzy expert system to assist in the prediction of hazardous wind conditions within the Mediterranean basin

An expert system (MEDEX) for predicting the gale-force onset, continuation, and cessation of seven major wind types within the Mediterranean basin has been designed, developed, and tested. The six wind types consist of the bora (flowing through both the Adriatic and Aegean Seas), etesian, levante, mistral, sirocco and westerly (poniente and vendaval). Except for the sirocco, these winds result from synoptic situations that lead to topographical channelling. MEDEX is rule-based and incorporates fuzzy logic to handle both objective and subjective inputs, the latter being a unique application of fuzzy logic. MEDEX has approximately 330 fuzzy rules, covering the seven winds in both winter and summer seasons. While MEDEX has been designed as a nowcasting tool (0–12 h), it can be applied to any future time for which forecasting charts (consisting of surface pressure and 500 mb height fields) are available. Inputs consist of objective pressure gradients in addition to subjective interpretations of various synoptic features. These inputs, as well as the corresponding rules, were developed, tuned, and verified over a two-year period during which the weather conditions from 539 days were individually analysed. Ground truth verification was produced primarily from over-water Special Sensor Microwave/Imager (SSM/I) wind speed measurements but also included observations as available, model predictions, and official Navy wind warnings. Evaluations of MEDEX performance for both onset and cessation of winter and summer winds are presented. In addition, comparisons with forecast statistics for the Navys regional weather centre in Rota, Spain show that MEDEX has comparable forecasting skill. Copyright

Machine Learning of Maritime Fog Forecast Rules

Abstract In recent years, the field of artificial intelligence has contributed significantly to the science of meteorology, most notably in the now familiar form of expert systems. Expert systems have focused on rules or heuristics by establishing, in computer code, the reasoning process of a weather forecaster predicting, for example, thunderstorms or fog. In addition to the years of effort that goes into developing such a knowledge base is the time-consuming task of extracting such knowledge and experience from experts. In this paper, the induction of rules directly from meteorological data is explored-a process called machine learning. A commercial machine learning program called C4.5, is applied to a meteorological problem, forecasting maritime fog, for which a reliable expert system has been previously developed. Two detasets are used: 1) weather ship observations originally used for testing and evaluating the expert system, and 2) buoy measurements taken off the coast of California. For both dataset...

Application of hypermedia and expert system technology to Navy environmental satellite image analysis

The Naval Research Laboratory Marine Meteorology Division, over a period of more than 15 years, has developed a series of satellite imagery training documents called the Navy Tactical Applications ...