An Observation Capability Semantic-Associated Approach to the Selection of Remote Sensing Satellite Sensors: A Case Study of Flood Observations in the Jinsha River Basin

Hu, Chuli; Li, Jie; Lin, Xin; Chen, Nengcheng; Yang, Chao

doi:10.3390/s18051649

Cited by 9 publications

(4 citation statements)

References 28 publications

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“…Table 11 illustrates the qualitative comparative results between the OCEM and seven other methods, including the DOCI, sensor static capability index (SSCI) [25], sensor observation capability association ontology (SOCA Ontology) [58], sensor observation capability object field (SOCO-Field) [15], star sensor observation capability evaluation model (SSOCEM) [32], Earth observation potential evaluation model (EOPEM) [33], and sensor band selection method (SBSM) [11]. An overview of these methods is included in the final column of the table.…”

Section: Superiority Of Ocemmentioning

confidence: 99%

Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection

Duan,

Zhang,

Liu

et al. 2023

Applied Sciences

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Satellite sensors are one of the most important means of collecting real-time geospatial information. Due to their characteristics such as large spatial coverage and strong capability for dynamic monitoring, they are widely used in the observation of real-time flood situation information for flood situational awareness and response. Selecting the optimum sensor is vital when multiple sensors exist. Presently, sensor selection predominantly hinges on human experience and various quantitative and qualitative evaluation methods. Yet, these methods lack optimization considering the flood’s spatiotemporal characteristics, such as different flood phases and geographical environmental factors. Consequently, they may inaccurately evaluate and select the inappropriate sensor. To address this issue, an innovative observation capability evaluation model (OCEM) is proposed to quantitatively pre-evaluate the performance of flood-water-observation-oriented satellite sensors. The OCEM selects and formulates various flood-water-observation-related capability factors and supports dynamic weight assignment considering the spatiotemporal characteristics of the flood event. An experiment involving three consecutive flood phase observation tasks was conducted. The results demonstrated the flexibility and effectiveness of the OCEM in pre-evaluating the observation capability of various satellite sensors across those tasks, accounting for the spatiotemporal characteristics of different flood phases. Additionally, qualitative and quantitative comparisons with related methods further affirmed the superiority of the OCEM. In general, the OCEM has provided a “measuring table” to optimize the selection and planning of sensors in flood management departments for acquiring real-time flood information.

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Section: Superiority Of Ocemmentioning

confidence: 99%

Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection

Duan,

Zhang,

Liu

et al. 2023

Applied Sciences

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“…In recent years, along with the rapid development of sensor technology, aerospace platform technology, and data communication technology, the global observation capability of satellite, aircraft, and land integration has been greatly enhanced [ 6 , 7 , 8 ]. Remote sensing data usually contain two major characteristics of spectral and spatial structures, which can effectively describe the shape distribution and morphological structure information of spatial objects on the Earth’s surface [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Approaches for Meteorological Disaster Monitoring: Recent Achievements and New Challenges

2022

IJERPH

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Meteorological disaster monitoring is an important research direction in remote sensing technology in the field of meteorology, which can serve many meteorological disaster management tasks. The key issues in the remote sensing monitoring of meteorological disasters are monitoring task arrangement and organization, meteorological disaster information extraction, and multi-temporal disaster information change detection. To accurately represent the monitoring tasks, it is necessary to determine the timescale, perform sensor planning, and construct a representation model to monitor information. On this basis, the meteorological disaster information is extracted by remote sensing data-processing approaches. Furthermore, the multi-temporal meteorological disaster information is compared to detect the evolution of meteorological disasters. Due to the highly dynamic nature of meteorological disasters, the process characteristics of meteorological disasters monitoring have attracted more attention. Although many remote sensing approaches were successfully used for meteorological disaster monitoring, there are still gaps in process monitoring. In future, research on sensor planning, information representation models, multi-source data fusion, etc., will provide an important basis and direction to promote meteorological disaster process monitoring. The process monitoring strategy will further promote the discovery of correlations and impact mechanisms in the evolution of meteorological disasters.

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“…GEOBIA 2014, 5th GEOBIA Conference, Thessaloníki, Greece, 21-24 May, 2014. Ontologies have then been used for semantic annotation of satellite images using domain ontologies (Amiri, Farah, and Farah 2017), to validate remote sensing workflows (Liu et al (2017), to structure and describe processing chains in remote sensing (Nys et al 2018) or to improve the discovery and selection of satellite sensors depending on expected applications (Hu et al 2018). A major application consists in using ontologies to interpret remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Ontologies to interpret remote sensing images: why do we need them?

Arvor

Belgiu

Falomir

et al. 2019

GIScience & Remote Sensing

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The development of new sensors and easier access to remote sensing data are significantly transforming both the theory and practice of remote sensing. Although data-driven approaches based on innovative algorithms and enhanced computing capacities are gaining importance to process big Earth Observation data, the development of knowledge-driven approaches is still considered by the remote sensing community to be one of the most important directions of their research. In this context, the future of remote sensing science should be supported by knowledge representation techniques such as ontologies. However, ontology-based remote sensing applications still have difficulty capturing the attention of remote sensing experts. This is mainly because of the gap between remote sensing experts' expectations of ontologies and their real possible contribution to remote sensing. This paper provides insights to help reduce this gap. To this end, the conceptual limitations of the knowledge-driven approaches currently used in remote sensing science are clarified first. Then, the different modes of definition of geographic concepts, their duality, vagueness and ambiguity, and the sensory and semantic gaps are discussed in order to explain why ontologies can help address these limitations. In particular, this paper focuses on the capacity of ontologies to represent both symbolic and numeric knowledge, to reason based on cognitive semantics and to share knowledge on the interpretation of remote sensing images. Finally, a few recommendations are provided for remote sensing experts to comprehend the advantages of ontologies in interpreting satellite images.

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An Observation Capability Semantic-Associated Approach to the Selection of Remote Sensing Satellite Sensors: A Case Study of Flood Observations in the Jinsha River Basin

Cited by 9 publications

References 28 publications

Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection

Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection

Remote Sensing Approaches for Meteorological Disaster Monitoring: Recent Achievements and New Challenges

Ontologies to interpret remote sensing images: why do we need them?

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