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

Durbha, Surya S.; King, Roger L.

doi:10.1109/igarss.2004.1368973

Cited by 18 publications

(14 citation statements)

References 18 publications

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“…For example, nearly 3 terabytes of data are being sent to Earth by NASA's satellites every day [1]. Advances in satellite technology and computing power have enabled the study of multi-modal, multi-spectral, multi-resolution and multi-temporal data sets for applications such as urban land use monitoring and management, GIS and mapping, environmental change, site suitability, agricultural and ecological studies.…”

Section: Introductionmentioning

confidence: 99%

Spatial Techniques for Image Classification

Aksoy¹

2006

Signal and Image Processing for Remote Sensing

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The constant increase in the amount and resolution of remotely sensed imagery necessitates development of intelligent systems for automatic processing and classification. We describe a Bayesian framework that uses spatial information for classification of high-resolution images. First, spectral and textural features are extracted for each pixel. Then, these features are quantized and are used to train Bayesian classifiers with discrete non-parametric density models. Next, an iterative split-and-merge algorithm is used to convert the pixel level classification maps into contiguous regions. Then, the resulting regions are modeled using the statistical summaries of their spectral, textural and shape properties, and are used with Bayesian classifiers to compute the final classification maps. Experiments with three ground truth data sets show the effectiveness of the proposed approach over traditional techniques that do not make strong use of region-based spatial information.

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Section: Introductionmentioning

confidence: 99%

Spatial Techniques for Image Classification

Aksoy¹

2006

Signal and Image Processing for Remote Sensing

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“…The literature [3] introduced an integrated spatial data mining algorithm using formal ontology. A framework based on a concept-based model using domaindependant ontology was proposed in literature [4]. The paper proposes an ontology-based framework for environmental data management and integration.…”

Section: Introductionmentioning

confidence: 99%

Towards an Integrated GIS Framework for Managing Environmental Data

Gui

Huang

Xie

2010

2010 4th International Conference on Bioinformatics and Biomedical Engineering

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Ontology is deemed as an effective means in the field of information integration. However, environmental data, which involves various kinds of data such as spatio-temporal data about air, land, water as well as non nature data about social and economic issues, is quite distinct to general data. Currently how to use ontology for environmental data integration was seldom discussed. This paper presents the hierarchical structure of environmental ontology and gives a method of modeling environmental data using ontology. Then an ontology-based GIS framework for integrating and managing environmental data is proposed.

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“…Similar to application ontology, Protégé can also be used to develop domain ontology (Semenova & Kureychik, 2015). The most language used in domain ontology development is the OWL (Durbha & King, 2004;Ochoa et al, 2013;She & Wang, 2009;Soo-Yeon et al, 2004;Su et al, 2005). However, defining ontologies for serving multiple users and application is hard to create and manage (Mena et al, 2000).…”

Section: Domain Ontologiesmentioning

confidence: 99%

Towards knowledge modeling and manipulation technologies: A survey

Bimba

Al-Hunaiyyan

Mahmud

et al. 2016

International Journal of Information Management

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A system which represents knowledge is normally referred to as a knowledge based system (KBS). This article focuses on surveying publications related to knowledge base modelling and manipulation technologies, between the years 2000-2015. A total of 185 articles excluding the subject descriptive articles which are mentioned in the introductory parts, were evaluated in this survey. The main aim of this study is to identify different knowledge base modelling and manipulation techniques based on 4 categories; 1) linguistic knowledge base; 2) expert knowledge base; 3) ontology and 4) cognitive knowledge base. This led to the proposition of 8 research questions, which focused on the different categories of knowledge base modelling technologies, their underlying theories, knowledge representation technique, knowledge acquisition technique, challenges, applications, development tools and development languages. A part of the findings from this survey is the high dependence of linguistic knowledge base, expert knowledge base and ontology on volatile expert knowledge. A promising technique for knowledge-based business management and other knowledge related applications is also discussed.

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

Cited by 18 publications

References 18 publications

Spatial Techniques for Image Classification

Spatial Techniques for Image Classification

Towards an Integrated GIS Framework for Managing Environmental Data

Towards knowledge modeling and manipulation technologies: A survey

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