The purpose of this paper is to provide a multifacetted approach
to interactive analysis and modeling of time series, signals, and
dynamical data sets. The models use statistical regression analysis and are built in an incremental way from a set of simple functional
units. The analysis is supported by data visualization and on-line
adaptive tutorials accessed on the World Wide Web. This work extends our results obtained for the Landsat-5 and Landsat-7 calibration data.
The another objective is to educate the user about available
mathematical models and also to allow the user to build those
models interactively through applets prepared by the authors on a Web
server. These models may be constructed for the user's own data set.
The approach is illustrated using the calibration data sets for
the Landsat sensors, we also discuss agent communication framework of regression models and calibration data. However, the same integrated approach can be used for other data domains. This type of approach is consistent with other recent activities regarding semantic web.
In this paper, we describe an ontology based multi-agent approach to data fusion from heterogeneous data sources. We assume that the data can come from various sensors or databases. In our approach, each data source is handled by one agent. The agent is able to deliver the data and the description of the data. The data description is provided in the form of specialized ontology. This description is the basis for fusion and integration of data from different sources or agents. A specialized agent fuses the data by evaluating these descriptions under the given query. Since data source agents themselves maintain the description of the data, the whole system of data sources is extensible. This means that a new data source agent may enter the system of data sources at any time. After proper registration with the fusion agent, a new agent can contribute to the overall process. The fusion agent is requested to provide data from different sources with type and location specification. The possible process outcomes are fused data, transformation rules or a failure message. After finding fusion rules, the agent is able to provide data continuously. In our examples of ontologies, we describe primarily numerical data sources and their relations, however symbolic data fusion is also considered.
12 The Landsat-7 ETM+ incorporates three on-board calibration devices: (1) an internal calibrator, (2) a partial aperture solar calibrator and (3) a full aperture solar calibrator. The internal calibrator consists of two tungsten lamps and optical and mechanical components to transfer the light to the primary and cooled focal planes. In the present contribution we analyze the ETM+ internal calibrator signal in several spectral bands and model them functionally using several instrument-state quantities available in the Landsat-7 telemetry. Models were tested and compared in terms of their complexity, explanatory power and fit to the data. The intent of such models is to provide a way to correct for the internal calibrator instabilities and allow for its more precise usage in calibrating ETM+ data on-orbit. Some of the computer models we developed can be accessed interactively on the World Wide Web.
Estimate of the organic carbon content in soil is critical for global change modeling activities. Therefore, the predictive model for estimating soil carbon would provide an important tool for the scientific community. We used remotely sensed TM imaginary data together with the soil profiles and moss layer carbon data for the Northern Study Area (NSA) of the BOREAS project. Different classification and functional models of the carbon dependency on remotely sensed data were developed. The complexity of the models was scrutinized. Based on these techniques, we have developed a set of analysis tools. These tools and an Internet based access to some of these tools will be presented.
The purpose of this study is to improve mathematical modeling of calibration curves produced by the Landsat calibrators. We explain one band and one lamp modeling and then one calibration band and multiple lamps averaging. The algorithm has three parts at the present, namely one dimensional modeling that includes a change-point removal and two or more signals averaging. A demo of the algorithm and the data is available from the Internet using any Web browser.
The responsivities of the Landsat-5 Thematic Mapper (TM) reflective bands are characterized over the lifetime of the instrument using its internal calibration system. This system illuminates only the focal planes and aft optics of the TM so that it does not capture changes in the telescope. The observed changes are quantified and categorized as to whether they are likely to be true instrument responsivity changes or changes in the internal calibrator system itself. Changes observed that are likely to be true instrument changes are: (1) 7 percent, 5 percent, 8 percent and 7 percent exponential-like decreases in responsivity with decay half lives of 250, 180, 60 and 110 days in bands 1 to 4, respectively, during the initial on-orbit period and (2) an oscillation in response of about 5 percent peak-to-peak in bands 5 and 7. The first effect is believed to originate in the TM spectral bandpass filters and the second effect is believed to be due to an icing build up in the cold focal plane window. Two rapid apparent responsivity changes, one a decrease and one an increase, which are peculiar to particular internal calibrator lamps are believed to be due to changes in the lamp assemblies themselves as is a gradual increase in all detector's responsivities with time. An annual oscillation of up to 2 percent peak-to-peak in all bands is likely the product of both a temperature sensitivity of the IC and the TM primary focal plane.
The purpose of this study is to propose and study algorithms for off-line trend monitoring and change-point detection for calibration coefficient data streams. This type of algorithm is suitable for monitoring different sensors e.g. AVHRR, ETM+ and MODIS. Based on these algorithms we can produce flags for he instruments indicating normal and abnormal behavior. These algorithms can also help to discover trends and features in the calibration data. Some of these algorithms will be used by the Landsat-7 ETM+ Image Assessment System and the EOS AM1 MODIS calibration system for modeling the gain behavior of the instruments over time. Mathematically we used quadratic splines and statistical concepts for building incremental models.