A framework of ontology-based knowledge information processing for change detection in remote sensing data

Hashimoto, S.; Tadono, Takeo; Onosato, Masahiko; Hori, Masahiro; Moriyama, Takashi

doi:10.1109/igarss.2011.6050090

Cited by 12 publications

(6 citation statements)

References 5 publications

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“…Hashimoto et al [49] have presented a framework based on ontologies and heuristics for automatic change interpretation. The proposed framework considers remote sensing data analysis as a knowledge information processing, which derives new information about targets with inference from the observed data and a priori knowledge for remote sensing images.…”

Section: Ontology-based Change Detectionmentioning

confidence: 99%

Semantic Remote Sensing Scenes Interpretation and Change Interpretation

Ghazouani¹,

Farah²,

Solaiman³

2018

Ontology in Information Science

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A fundamental objective of remote sensing imagery is to spread out the knowledge about our environment and to facilitate the interpretation of different phenomena affecting the Earth's surface. The main goal of this chapter is to understand and interpret possible changes in order to define subsequently strategies and adequate decision-making for a better soil management and protection. Consequently, the semantic interpretation of remote sensing data, which consists of extracting useful information from image date for attaching semantics to the observed phenomenon, allows easy understanding and interpretation of such occurring changes. However, performing change interpretation task is not only based on the perceptual information derived from data but also based on additional knowledge sources such as a prior and contextual. This knowledge needs to be encoded in an appropriate way for being used as a guide in the interpretation process. On the other hand, interpretation may take place at several levels of complexity from the simple recognition of objects on the analyzed scene to the inference of site conditions and to change interpretation. For each level, information elements such as data, information and knowledge need to be represented and characterized. This chapter highlights the importance of ontologies exploiting for encoding the domain knowledge and for using it as a guide in the semantic scene interpretation task.

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Section: Ontology-based Change Detectionmentioning

confidence: 99%

Semantic Remote Sensing Scenes Interpretation and Change Interpretation

Ghazouani¹,

Farah²,

Solaiman³

2018

Ontology in Information Science

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“…Ontology allows the representation of concepts, instances, relationships, and axioms and permits the inference of implicit knowledge [7]. Many studies prove that ontologies are feasible in supporting the semantic representation of remote sensing images [8]- [11]. However, most of the studies have some limitations, e.g., the feature value stored for different classes in the ontology are given by expert's prior knowledge and experiences about the study area.…”

Section: Introductionmentioning

confidence: 98%

Extraction of land cover from ZY-3 imagery using ontology-based method

Luo

Kuai

et al. 2015

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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In this paper, an ontology-based framework of China Geographic National Conditions Monitoring (CGNCM) land cover extraction is presented. Using statistical analysis method, spectral, texture, spatial features of each land cover class are extracted from the referenced ZY-3 image acquired in summer, 2012. Stored in the form of OWL, the features of one class can be considered as the prototype of this class. With the help of prototype, automation extraction of land cover information is validated on ZY-3 images of summer in subsequent year. The results highlight the capacity of the ontology-based method to automatically identify land cover using the ZY-3 image in CGNCM mission.

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“…Ontology allows the representation of concepts, instances, relationships, and axioms and permits the inference of implicit knowledge (Fonseca, 2002). Many studies prove that ontologies are feasible in supporting the semantic representation of remote sensing images (Forestier, 2002) (Durand, 2007) (Jensen, 2009 (Hashimoto, 2011) (Samuel, 2012). However, most of the studies have some limitations, e.g., the feature value stored for different classes in the ontology are given by expert's prior knowledge and experiences about the study area.…”

Section: Introductionmentioning

confidence: 99%

Land Cover Extraction of Coastal Area From Gf-1 WFV Imagery Using Ontological Method

Luo

Kuai

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. As a knowledge organization and representation method, ontology that can store land cover spectral, texture, shape attributes and relationships derived from image analysis. With the knowledge organized in ontology, the efficiency of automatic or semi-automatic land cover information extraction for the large coastal area is supposed to be improved. Together with the help of GF-1 Wide Field of View (WFV) data, which covers almost 200 km width area, the more frequent monitoring and change detection for coastal area of Guangxi province are available. This study makes attempt to monitor the land cover of Guangxi coastal area using GF-1 WFV data with ontological method. The land cover ontology for this area is established first via image feature analysis. Using this ontology, automatic image extraction from GF-1 WFV data of subsequent monitoring time is realized. The results of this study reveal that, using ontology, land cover extraction can be completed in acceptable accuracy but with higher efficiency.

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A framework of ontology-based knowledge information processing for change detection in remote sensing data

Cited by 12 publications

References 5 publications

Semantic Remote Sensing Scenes Interpretation and Change Interpretation

Semantic Remote Sensing Scenes Interpretation and Change Interpretation

Extraction of land cover from ZY-3 imagery using ontology-based method

Land Cover Extraction of Coastal Area From Gf-1 WFV Imagery Using Ontological Method

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