Analysis Of Multi-sensor Data Research Articles

Multisource data analysis for geoscience applications

A program has been written in MATLAB for the integration of several time series from multiple sources. The program implements a statistical procedure that has been designed in two steps: the first step estimates a common-mode harmonic signal using a least-squares model while the second step validates the solution through a series of statistical tests. A modest contribution focuses on the special problem of detecting common-mode harmonic seasonal variations in the baseline solution vectors derived by co-located space geodetic techniques. This does not, however, confine the procedure from being extended to other measurements or similar systems. For example, other potential application areas include multi-proxy data analysis for paleoclimate research, tidal analysis for oceanography studies, multisensor data analysis for on-line structural monitoring and other geotechnical engineering studies.

Multisource data analysis for geoscience applications*1

A program has been written in MATLAB for the integration of several time series from multiple sources. The program implements a statistical procedure that has been designed in two steps: the first step estimates a common-mode harmonic signal using a least-squares model while the second step validates the solution through a series of statistical tests. A modest contribution focuses on the special problem of detecting common-mode harmonic seasonal variations in the baseline solution vectors derived by co-located space geodetic techniques. This does not, however, confine the procedure from being extended to other measurements or similar systems. For example, other potential application areas include multi-proxy data analysis for paleoclimate research, tidal analysis for oceanography studies, multisensor data analysis for on-line structural monitoring and other geotechnical engineering studies.

A multisensor approach to dust storm monitoring over the nile delta

This work analyzes several remote sensing instrument capabilities in monitoring dust storms. Multisensor data analysis is carried out to study the behavior of dust particles at different wavelengths. A technique based on a combination of optical and microwave sensing of dust storms, using the Moderate Resolution Imaging Spectrometer (MODIS) and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) respectively, is found to be particularly useful.

Satellite multi-sensor data analysis of urban surface temperatures and landcover

Multiple satellite sensors are used to analyze physical processes that determine energy fluxes and their interaction at the urban surface. The study is based on summertime microclimate analyses of the Los Angeles and Paris metropolises. The method consists of deriving some parameters governing the surface heat fluxes, constructing statistics of thermal infrared images, and using a GIS to combine them with a landcover classification from SPOT-HRV multispectral images, and with data from intensive in-situ experiments. The average images reveal spatial and temporal variations of land surface temperature (LST), and distinct microclimatic patterns. The combined interpretation of the statistics images and of the landcover classification shows: (i) the effect of surface physical properties, especially in downtown business and industrial districts that display heat-islands larger than 7 °C; (ii) the temperating influence of water; (iii) the negative correlation between afternoon land surface temperature and normalized vegetation index, which confirms the cooling effect of urban parks; (iv) the correlation between variations of surface temperature and ozone concentration at diurnal and longer time scales.

Fault diagnosis of multivariate systems using pattern recognition and multisensor data analysis technique

A pattern recognition-based methodology is proposed for fault diagnosis of multivariate and dynamic systems. Noisy input patterns, belonging to a class of event(s), are first scaled to unit variance and zero mean. This step, termed as a harmonizing step, reduces magnitude difference amongst patterns belonging to a class of event(s). Existence of low frequency segments, such as ramp-type trends, in a pattern hampers the efficacy of the harmonizing step. In this work, a digital band-pass filter is designed to eliminate the ramp-type segments and decrease noise intensity. Then, the Principal Component Analysis (PCA) technique is applied in order to describe the information space by a set of uncorrelated and fictitious data sources. A wavelet-based methodology is employed for each new sensor to extract pattern features. A binary decision tree is used to classify the extracted features. The outputs of each decision tree are: (1) the a posteriori probabilities that an unlabeled input pattern belongs to different classes of events; and (2) the probability confidence limits that input pattern may be classified to any of known classes. As the last step, any of two consensus theory-based techniques or evidence theory are utilized to combine the outputs of the decision trees and find the best classes of events describing system behavior. The performance of the proposed technique is examined by diagnosis of simulated faulty behavior for the Tennessee Eastman Process.