Wooil M. Moon

Integration Of Geophysical And Geological Data Using Evidential Belief Function

Several methods are available for integrating geophysical, geological, and remote sensing data sets and also for integrating them with additional information such as newly observed geophysical and geological data. Several published reports discuss successful applica- tion of different types of spatial information integration techniques in- cluding the geographical information system (GIs). There have also been theoretical developments including Bayesian approach in updat- ing old data sets with newly acquired information. However, weak- nesses and problems still exist. Many geological and geophysical data sets often have only partial coverage and in almost all cases have very different spatial resolution. These cause serious difficulties in certain cases. In this research the partial belief function approach is examined as a means to integrate one set of airborne and/or ground geophysical data with other available geological and geophysical data sets, and to update the existing information successively with newly observed data over target areas. In theory, the Dempster-Shafer method appears to be the most suitable method, but in practice several difficulties arise that must be overcome. One of the major difficulties is the dependency of the partial belief function on exploration targets, which can only be defined, at present, in a case-by-case approach.

Uncertainty Management in Integration of Exploration Data Using the Belief Function

The evidential belief function (EBF) provides an adequate theoretical basis for managing uncertainties in exploration data integration. The EBF can be used to represent uncertainties in the reasoning process and provides the capability of distinguishing between lack of information and negative information. This capability is desirable when combining diverse data sets, which often vary in spatial resolution and spatial extent. The uncertainties associated with data and propositions can be represented naturally and consistently using belief functions. Hence, using the EBF approach can provide a realistic quantitative picture of the target proposition.

Application of TerraSAR-X Data for Emergent Oil-Spill Monitoring

Synthetic aperture radar (SAR) signals can propagate through hazardous weather and atmospheric conditions with heavy cloud cover, volcanic dust, snow, or rain. The all-weather capabilities of SARs have attracted significant interest in remote sensing communities, since serious environmental disasters such as oil spills have been highly ¿elusive¿ to optical sensors, making visible spectrum data vulnerable to rapidly changing atmospheric conditions. In this paper, we discuss the technical functionalities of TerraSAR-X from the emergency response perspective, describing its technical abilities in terms of a damping ratio, radiometric accuracy, and noise level with reference to the actual Hebei Spirit oil-spill incident that occurred on the west coast of the Korean peninsula in December 2007. The damping ratios estimated from the TerraSAR-X data as a function of Bragg wavenumber for various wind speeds indicate that TerraSAR-X data can be effectively used to identify oil-spill areas with acceptable accuracy. We also received ERS-2, ENVISAT, RADARSAT-1, and ALOS PALSAR data for this oil-spill event, not simultaneously but with varying time delays. The processing results for the multitemporal data sets obtained from the X- and C-band SAR systems are useful since they can be used to determine the near-real-time migration of spilt oil. The results of the current study indicate that there are distinct advantages of using X-band TerraSAR-X data for oil-spill detection compared to the data obtained at other available frequencies.

Unsupervised Classification of Scattering Mechanisms in Polarimetric SAR Data Using Fuzzy Logic in Entropy and Alpha Plane

The eigenvalue-eigenvector-based approach for understanding the scattering mechanisms of polarimetric synthetic aperture radar (POLSAR) data leads to noisy classification results due to arbitrarily fixed zone boundaries in the H/alpha macr plane. In this paper, a new classification scheme that can address the inherent vagueness of class boundaries in the H/alpha macr plane was tested in order to improve the unsupervised classification of the microwave scattering mechanism by introducing concepts related to fuzzy sets. A 2-D fuzzy membership function was developed for the fuzzification of the 2-D H/alpha macr plane. The proposed fuzzy H/alpha macr classifier is composed of three steps: fuzzification of the H/alpha macr plane, iterative refinement of membership degrees using the c-means algorithm, and defuzzification for the final decision process. The performance of this new approach for the L-band NASA/Jet Propulsion Laboratorys Airborne SAR data obtained during the PACRIM-II experiment was shown to be consistently improved. This new classification technique can be applied to POLSAR data without any a priori information. The fuzzification of the zone boundaries can be further applied to the interpretation of the POLSAR data, e.g., multifrequency classification, retrieval of bio- and geophysical parameters, etc. In order to propose another implementation of the fuzzy boundary representation, we exploited the combination of the H/alpha macr state space and anisotropy information.

An object-oriented knowledge representation structure for exploration data integration

Knowledge representation structure and reasoning processes are very important issues in the knowledge-based approach of integrating multiple spatial data sets for resource exploration. An object-oriented knowledge representation structure and corresponding reasoning processes are formulated and tested in this research on the knowledge-based approach of integrating spatial exploration data. The map-based prototype expert system developed in this study has self-contained knowledge representation structure and inference mechanisms. It is important to distinguish between lack of information and information providing negative evidence for a map-based system because the spatial distribution of data sets are uneven in most cases. Error and uncertainty estimation is also an important component of any production expert system. The uncertainty propagation mechanisms developed here work well for this type of integrated exploration problem. Evidential bellef function theory provides a natural theoretical basis for representing and integrating spatially uneven geophysical and geological information. The prototype system is tested using real mineral exploration data sets from the Snow Lake area, northern Manitoba, Canada. The test results outline the favorable exploration areas successfully and show the effectiveness of the knowledge representation structure and inference mechanisms for the knowledge-based approach.

Fully Polarimetric SAR Image Classification via Sparse Representation and Polarimetric Features

Feature extraction and image classification using polarimetric synthetic aperture radar (PolSAR) images are currently of great interest in SAR applications. Generally, PolSAR image classification is a high-dimensional nonlinear mapping problem. Sparse representation-based techniques have shown great potential for pattern recognition problems. Therefore, on the basis of the sparse characteristics of the features for PolSAR image classification, a supervised PolSAR image classification method based on sparse representation is proposed in this paper. First, the effective features are extracted to describe the distinction of each class. Then, the feature vectors of the training samples construct an over-complete dictionary and obtain the corresponding sparse coefficients; meanwhile, the residual error of the pending pixel with respect to each atom is evaluated and considered as the criteria for classification, and the ultimate class results can be obtained according to the atoms with the least residual error. In addition, a Simplified Matching Pursuit (SMP) algorithm is proposed to solve the optimization problem of sparse representation of PolSAR images. The verification tests are implemented using Danish EMISAR L-band fully polarimetric SAR data of Foulum area, Denmark. The preliminary experimental results confirm that the proposed method outputs an excellent result and moreover the classification process is simpler and less time consuming.

Estimation of Surface Roughness Parameter in Intertidal Mudflat Using Airborne Polarimetric SAR Data

The coastal zones of the Korean peninsula are well known for their large tide ranges and vast expanse of intertidal flats. In this paper, methods of extracting the roughness of the scattering surface of intertidal mudflats from polarimetric synthetic aperture radar (SAR) data have been investigated. The L-band NASA/Jet Propulsion Laboratories airborne SAR data, which were acquired in the intertidal zone during PACRIM-II Korea campaign, were used to estimate the roughness of intertidal mudflats. Surface roughness can be utilized as a useful parameter to monitor the fishery activities in intertidal flats as well as the changes in textural characteristics of surface sediments. In order to retrieve roughness parameters, such as the rms height and the correlation length, of intertidal mudflats, three types of roughness inversion algorithms, based on the Integral Equation Method (IEM), semiempirical, and extended-Bragg models, have been investigated and developed. The inversion algorithms based on the IEM and semiempirical models can be applied to the dual-polarized SAR, while the extended-Bragg model-based inversion approach is also applicable to the fully polarimetric SAR observations. Results indicate the fully polarimetric approach is more pertinent to monitor geophysical parameters from space than the dual polarimetric approach, even if it is possible to reduce the number of unknown surface variables in the specific case of inversion problems.

On Knowledge-based Approach Of Integrating Remote Sensing, Geophysical And Geological Information

An object-oriented and map-based prototype expert system is developed for integrating geophysical, geological, and remote sensing data for base metal exploration and tested using real exploration data from Farley Lake, Manitoba, Canada. Evidential belief function theory is utilized to manage the uncertainties in the system. The object-oriented knowledge representation structure and uncertainty propagation mechanisms used work well for this integrated exploration problem. In addition to other advantages of knowledge-based approach, the problem of dependent information can be dealt with in a knowledge-based system of this type by explicitly introducing important uncertainties and by organizing the relation network properly.

Dependence of Waterline Mapping on Radar Frequency Used for SAR Images in Intertidal Areas

Waterline detection in the intertidal areas was investigated through synthetic aperture radar (SAR) images and field measurements. Two valuable facts were found in this letter: 1) A discrepancy of waterlines between L- and P-band airborne SAR images was discovered and investigated through precise global positioning system measurements and the theory of the SAR imaging mechanism. In the intertidal areas having low slopes, the Bragg waves resonant with the radar signal can reside in different depths depending on the radar frequency, with the result that the boundary between water and land can be mapped differently in the respective SAR images. 2) Intertidal areas covered with a water film present low radar backscatter in SAR images, which can cause mislocation of waterlines

Very High-Resolution Remote Sensing: Challenges and Opportunities [Point of View]

Advanced information processing and architectures will be needed to bridge the gap between the potential offered by the new generations of sensors and the needs of the end-users to actually face tomorrows challenges in many applications with a very high societal impact. As remote sensing researchers and engineers, this is our passion, our charge, and our responsibility.