Calibration, validation and the NERC Airborne Remote Sensing Facility

The application of airborne and satellite remote sensing to terrestrial applications has been dominated by empirically-based, semi-quantitative approaches, in contrast to those developed in the marine and atmospheric sciences which have often developed from rigorous physically-based models. Furthermore, the traceability of EO data and the methodological basis of many applications has often been taken for granted, with the result that the repeatability of analyses and the reliability of many terrestrial EO products can be questioned. ‘NCAVEO’ is a recently established network of Earth Observation experts and data users committed to exchanging knowledge and understanding in the area of remote sensing data calibration and validation. It aims to provide a UK-based forum to collate available knowledge and expertise associated with the calibration and validation of EO-based products from both UK and overseas providers, in different discipline areas including land, ocean and atmosphere. This paper will introduce NCAVEO and highlight some of the contributions it hopes to make to airborne remote sensing in the UK

Analysis using Adaptive Tree Structured Clustering Method for Medical Data of Patients with Coronary Heart Disease

It is known that the classification of medical data is difficult problem because the medical data has ambiguous information or missing data. As a result, the classification method that can handle ambiguous information or missing data is necessity. In this paper we proposed an adaptive tree structure clustering method in order to clarify clustering result of selforganizing feature maps. For the evaluating effectiveness of proposed clustering method for the data set with ambiguous information, we applied an adaptive tree structured clustering method for classification of coronary heart disease database. Through the computer simulation we showed that the proposed clustering method was effective for the ambiguous data set

Applying Cluster Ensemble to Adaptive Tree Structured Clustering

Adaptive tree structured clustering (ATSC) is our proposed divisive hierarchical clustering method that recursively divides a data set into 2 subsets using self-organizing feature map (SOM). In each partition, the data set is quantized by SOM and the quantized data is divided using agglomerative hierarchical clustering. ATSC can divide data sets regardless of data size in feasible time. On the other hand clustering result stability of ATSC is equally unstable as other divisive hierarchical clustering and partitioned clustering methods. In this paper, we apply cluster ensemble for each data partition of ATSC in order to improve stability. Cluster ensemble is a framework for improving partitioned clustering stability. As a result of applying cluster ensemble, ATSC yields unique clustering results that could not be yielded by previous hierarchical clustering methods. This is because a different class distances function is used in each division in ATSC

Toward a Science-Based Management Approach to Stealth Threats: A Case Study Using The Novel Coronavirus

Homeland Security Affairs Journal Special COVID-19 IssueThe article record may be found at https://www.hsaj.org/articles16529The modest early stage impact of slow-moving threats makes it easy to underestimate their impact. These threats grow and evolve unnoticed until reaching dramatic impacts in both scope and scale. Since slow-moving threats can grow to catastrophic magnitudes that threaten our very survival, they are more aptly identified as ‘stealth…CONTINUE READINGSponsored the U. S. Department of Homeland Security’s National Preparedness Directorate, FEMA, CHDS is part of the Naval Postgraduate School (NPS)

Optimum Object Analysis Of Islands Activities On South China Sea By DNB On VIIRS

A data processing and analyzing system was designed and made operational with free software to process the big data over the South China Sea (SCS), which are provided by the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (Suomi-NPP) to monitor the light distributions in the night. The VIIRS data are processed from the raw data (level-0) to geophysical data (level-3) using the International Polar Orbiter Processing Package (IPOPP), which are freely available from NASA. The Day Night Band (DNB) of VIIRS is extracted from the level-3 data and is geospatially projected for our region of interest (ROI) on the SCS. For those ingested data, an optimum object analysis in geographic domain was proposed to estimate the reclamation activities on coral reefs in the SCS using GDAL. A pixel base analysis of DNB data is possible to estimate the island activities independently among dredgers, support vessels, or buildings on coral reefs, but not appropriate to analyze the activities for as an integrated system or for the changes in the ROI. Although there is a difficulty to determine the scale of objects on the analysis of DNB data for the reclamation activities, the optimum object scale was empirically determined for different size of coral reefs and was applied for this study. The optimum object analysis determines the reclamation activities with including lights not only from buildings but also dredgers and supporting vessels around coral reefs

Scientific questions for the exploration of the terrestrial planets and Jupiter - Advanced planetary missions technology program Progress report

Scientific questions and experimental design for planetary exploration of Jupiter, Mars, Mercury, and Venu