Surya S. Durbha

Semantics-enabled framework for knowledge discovery from Earth observation data archives

Earth observation data have increased significantly over the last decades with satellites collecting and transmitting to Earth receiving stations in excess of 3 TB of data a day. This data acquisition rate is a major challenge to the existing data exploitation and dissemination approaches. The lack of content- and semantic-based interactive information searching and retrieval capabilities from the image archives is an impediment to the use of the data. In this paper, we describe a framework we have developed [Intelligent Interactive Image Knowledge Retrieval (I/sup 3/KR)] that is built around a concept-based model using domain-dependant ontologies. In this framework, the basic concepts of the domain are identified first and generalized later, depending upon the level of reasoning required for executing a particular query. We employ an unsupervised segmentation algorithm to extract homogeneous regions and calculate primitive descriptors for each region based on color, texture, and shape. We initially perform an unsupervised classification by means of a kernel principal components analysis method, which extracts components of features that are nonlinearly related to the input variables, followed by a support vector machine classification to generate models for the object classes. The assignment of concepts in the ontology to the objects is achieved automatically by the integration of a description logics-based inference mechanism, which processes the interrelationships between the properties held in the specific concepts of the domain ontology. The framework is exercised in a coastal zone domain.

A Machine Learning Based Spatio-Temporal Data Mining Approach for Detection of Harmful Algal Blooms in the Gulf of Mexico

Harmful algal blooms (HABs) pose an enormous threat to the U.S. marine habitation and economy in the coastal waters. Federal and state coastal administrators have been devising a state-of-the-art monitoring and forecasting system for these HAB events. The efficacy of a monitoring and forecasting system relies on the performance of HAB detection. We propose a machine learning based spatio-temporal data mining approach for the detection of HAB events in the region of the Gulf of Mexico. In this study, a spatio-temporal cubical neighborhood around the training sample is introduced to retrieve relevant spectral information of both HAB and non-HAB classes. The feature relevance is studied through mutual information criterion to understand the important features in classifying HABs from non-HABs. Kernel based support vector machine is used as a classifier in the detection of HABs. This approach gives a significant performance improvement by reducing the false alarm rate. Further, with the achieved classification accuracy, the seasonal variations and sequential occurrence of algal blooms are predicted from spatio-temporal datasets. New variability visualization is introduced to illustrate the dynamic behavior of HABs across space and time.

Standards-Based Middleware and Tools for Coastal Sensor Web Applications

Coastal buoys and stations provide frequent, high quality marine observations for oceanographic study, weather service, atmospheric, and public safety. There is a great need for sharing the generated data sets to enable timely decision-making across various agencies/institutions. However, this requires tremendous efforts and coordination among different sensor network agencies to come to a shared understanding for disseminating the information in a uniform and standardized way. To address these needs and achieve interoperability, syntactic standardization provides to a certain extent the data description models. In addition, semantic enrichment of the information sources is also required to understand the context of the data. In this paper we adopt standardized data models based on the Open Geospatial Consoritum (OGC) sensor web enablement framework for coastal sensor networks, which facilitates improved information retrieval on a variety of spatio-temporal scales. The semantic enrichment is achieved in terms of conceptualization of selected sensor types and other terminology involved in the coastal domain in the form of Ontologies. A coastal semantic mapping (COSEM-Map) tool developed as a part of this work facilitates the harmonization of different representations. This ontological model is amenable to querying using SPARQL, which is a standardized RDF query language. Further, sensor web services discovery aspects have been addressed by augmenting the Universal Description, discovery and Integration (UDDI) registry with Web Ontology Language for Services (OWL-S) and using a semantic matching algorithm for services discovery. Both web-based and mobile-enabled clients were developed to process syntactic and semantic queries.

Feature Identification via a Combined ICA–Wavelet Method for Image Information Mining

Image transformation is required for color-texture image segmentation. Various techniques are available for the transformation along the spatial and spectral axes. For instance, the HSV-wavelet technique is shown to be very effective for image information mining in remote-sensing applications. However, the HSV transformation approach uses only three spectral bands at a time. In this letter, a new feature set, obtained by combining independent component analysis and wavelet transformation for image information mining in geospatial data, is presented. Experimental results show the effectiveness of the presented method for image information mining in Earth observation data archives.

A framework for semantic reconciliation of disparate earth observation thematic data

There is a growing demand for digital databases of topographic and thematic information for a multitude of applications in environmental management, and also in data integration and efficient updating of other spatially oriented data. These thematic data sets are highly heterogeneous in syntax, structure and semantics as they are produced and provided by a variety of agencies having different definitions, standards and applications of the data. In this paper, we focus on the semantic heterogeneity in thematic information sources, as it has been widely recognized that the semantic conflicts are responsible for the most serious data heterogeneity problems hindering the efficient interoperability between heterogeneous information sources. In particular, we focus on the semantic heterogeneities present in the land cover classification schemes corresponding to the global land cover characterization data. We propose a framework (semantics enabled thematic data Integration (SETI)) that describes in depth the methodology involved in the reconciliation of such semantic conflicts by adopting the emerging semantic web technologies. Ontologies were developed for the classification schemes and a shared-ontology approach for integrating the application level ontologies as described. We employ description logics (DL)-based reasoning on the terminological knowledge base developed for the land cover characterization which enables querying and retrieval that goes beyond keyword-based searches.

PMU placement considerations — A roadmap for optimal PMU placement

Recent investments in the Synchrophasor technology have energized the industry and a significant number of Phasor Measurement Units (PMUs) are being deployed. By some estimates, just in North America, the number of PMU installations is expected to grow five-fold — from approximately 200 today to over a 1000. The first step in PMU deployment is a clear roadmap of the process for selecting the location of the additional PMU devices and establishing guidelines to assist with this decision-making process. Many of the existing optimal PMU placement approaches are mainly focused on a particular application (such as improving State Estimation). This paper proposes a more comprehensive, holistic set of criteria for optimizing PMU placement based on sound practical solutions by experienced industry practitioners. The methodology offers the flexibility for considering multiple, diverse factors that can influence the PMU siting decision-making process, including incorporating several practical implementation aspects (e.g. communications infrastructure, prohibitive deployment cost, etc). Application needs, reliability requirements, and infrastructure challenges that drive the overall solution for optimal PMU location selection are formulated and described.

Wrapper-Based Feature Subset Selection for Rapid Image Information Mining

In a disaster, there is a need for rapid image-information retrieval in real or near real time from vast amounts of data coming from multiple remote-sensing sensors. In general, image information mining (IIM) approaches produce enormous amounts of features that are computationally expensive and inefficient to process before the actual information discovery takes place. Also, it is complicated because the combination of the features has little relevance to the hypothesis space. Hence, selecting a relevant subset of features is necessary to overcome these problems and to provide an efficient representation of the target class. In this letter, we propose feature selection and feature transformations based on a wrapper-based genetic algorithm approach. A support vector machine classification is applied for generating predictive models for those land-cover classes that are important in a coastal disaster event. The proposed system, rapid IIM, is a region-based approach where, in lieu of the prevalent pixel-based methods, it localizes interesting zones and enables rapid querying. Results from this study indicate that selecting relevant feature subsets increases the rate of correctly identifying a semantic class and also enables this process with less number of features.

Knowledge mining in Earth observation data archives: a domain ontology perspective

The earth observation data has increased significantly over the last decades; NASA has 18 Earth observation satellites on orbit earning 80 sensors, as of April 2003. About 3 terabytes of data are collected daily and transmitted to Earth receiving stations. The data exploitation and dissemination methods have not kept pace with the huge data acquisition rate. The products distributed by the agencies are often not in a readily usable form by the nonscience community, and need further processing at the user level. The lack of content and semantic based interactive information searching and retrieval capabilities from the archives is another important issue to be addressed in this context. We propose a framework based on a concept-based model using domain-dependant ontologies where the basic concepts of the domain are identified first and generalized later depending upon the level of reasoning required for executing a particular query. We employ an unsupervised segmentation algorithm to extract homogeneous regions and calculate primitive descriptors for each region based on color, texture and shape. The primitive descriptors are described quantitatively by middle level object ontology. The learning phase is applied at this stage. It associates the middle level descriptors to the concepts in the higher-level ontology by means of a nonlinear support vector machine (SVM) method. These associations are grouped into models specific to a semantic class and used for querying. Also interactive querying is provided by means of a region based relevance feedback method. A methodology to execute complex queries by the integration of an inference engine is discussed. We also intend to extend the system to carry out data exploratory tasks in a peer-to-peer environment.

An Information Semantics Approach for Knowledge Management and Interoperability for the Global Earth Observation System of Systems

The global earth observation system of systems (GEOSS) is built on current international cooperation efforts among existing distributed earth observing and processing systems. The goal is to formulate an end-to-end process that enables the collection and distribution of accurate, reliable earth observation (EO) data, information, products, and services to both suppliers and consumers worldwide. EOs are obtained from a multitude of sources and require tremendous efforts and coordination among different governments and user groups to come to a shared understanding on a set of concepts involved in a domain. Semantic metadata play a crucial role in resolving the differences in meaning, interpretation, and usage of the same or related data. Also, the knowledge about the geopolitical background of the originating datasets could be encoded in the metadata that would address the diversity on a global scale. In distributed environments like GEOSS, modularization is inevitable. In this paper, we describe the need for an information semantics-based approach for knowledge management and interoperability between heterogeneous GEOSS systems. Further, considering the magnitude of concepts involved in GEOSS, we explore the possibility of using modular ontologies for formulating smaller interconnected ontologies.

A Systematic Approach to Wavelet-Decomposition-Level Selection for Image Information Mining From Geospatial Data Archives

Recently, wavelet-based methods have been efficiently used for segmentation and primitive feature extraction to expedite the image-retrieval process of semantic-enabled frameworks for image information mining from geospatial data archives. However, the use of wavelets may introduce aliasing effects due to subband decimation at a certain decomposition level. This paper addresses the issue of selecting a suitable wavelet decomposition level, and a systematic selection process is developed. To validate the applicability of this method, a synthetic image is generated to qualitatively and quantitatively assess the performance. In addition, results for a Landsat-7 Enhanced Thematic Mapper Plus imagery archive are illustrated, and the F-measure is used to assess the feasibility of this method for the retrieval of different classes