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Spaceborne Earth-Observing Optical Sensor Static Capability Index for Clustering.

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Title: Spaceborne Earth-Observing Optical Sensor Static Capability Index for Clustering.
Authors: Chen, Nengcheng, Xing, Chenjie, Zhang, Xiang, Zhang, Liangpei, Gong, Jianya
Source: IEEE Transactions on Geoscience & Remote Sensing. Oct2015, Vol. 53 Issue 10, p5504-5518. 15p.
Subjects: Earth Observing System (Program), Detectors, Multiple criteria decision making, Algorithms, Cluster theory (Nuclear physics)
Abstract: Different Earth-observing (EO) sensors have various capabilities for diverse observing tasks. Sensor planning services make the choice of web-ready sensors for specific observing tasks with regard to observing requests and sensor capabilities. Sensor capabilities rely on various parameters; thus, choosing EO sensors for specific observing tasks relying directly on these parameters is a multicriteria decision process. A sensor's capability can be drawn from these parameters with the help of an algorithm. Furthermore, if divided into different clusters based on capabilities, applicable sensors can be more easily chosen for a category of observing tasks. In this paper, a spaceborne EO optical sensor static capability index (SSCI) mechanism is drawn from an evaluation-and-clustering algorithm, which is composed of a self-organizing neural map in combination with weighted principal component analysis. The scheme of SSCI relies on no expert analysis system and thus is more flexible and efficient. EO scenarios of disaster reactions are among the application of this algorithm. In particular, scenarios of flooding disaster forecasting, relief aiding, and postdisaster loss assessment within the framework of International Charter on Space and Major Disasters have been utilized for experiments. They have shown that the SSCI assessing algorithm is feasible and stable, and the EO optical sensor clustering algorithm based on SSCI can offer reasonable clustering accuracies of EO optical sensors. In our experiments, the EO optical sensor SSCI computation and clustering algorithm had a time consumption within 2 s and 2 min, respectively, and memory consumption within 200 MB on a normal personal computer. [ABSTRACT FROM PUBLISHER]
Copyright of IEEE Transactions on Geoscience & Remote Sensing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Spaceborne Earth-Observing Optical Sensor Static Capability Index for Clustering.
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Nengcheng%22">Chen, Nengcheng</searchLink><br /><searchLink fieldCode="AR" term="%22Xing%2C+Chenjie%22">Xing, Chenjie</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xiang%22">Zhang, Xiang</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Liangpei%22">Zhang, Liangpei</searchLink><br /><searchLink fieldCode="AR" term="%22Gong%2C+Jianya%22">Gong, Jianya</searchLink>
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  Data: Different Earth-observing (EO) sensors have various capabilities for diverse observing tasks. Sensor planning services make the choice of web-ready sensors for specific observing tasks with regard to observing requests and sensor capabilities. Sensor capabilities rely on various parameters; thus, choosing EO sensors for specific observing tasks relying directly on these parameters is a multicriteria decision process. A sensor's capability can be drawn from these parameters with the help of an algorithm. Furthermore, if divided into different clusters based on capabilities, applicable sensors can be more easily chosen for a category of observing tasks. In this paper, a spaceborne EO optical sensor static capability index (SSCI) mechanism is drawn from an evaluation-and-clustering algorithm, which is composed of a self-organizing neural map in combination with weighted principal component analysis. The scheme of SSCI relies on no expert analysis system and thus is more flexible and efficient. EO scenarios of disaster reactions are among the application of this algorithm. In particular, scenarios of flooding disaster forecasting, relief aiding, and postdisaster loss assessment within the framework of International Charter on Space and Major Disasters have been utilized for experiments. They have shown that the SSCI assessing algorithm is feasible and stable, and the EO optical sensor clustering algorithm based on SSCI can offer reasonable clustering accuracies of EO optical sensors. In our experiments, the EO optical sensor SSCI computation and clustering algorithm had a time consumption within 2 s and 2 min, respectively, and memory consumption within 200 MB on a normal personal computer. [ABSTRACT FROM PUBLISHER]
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  Data: <i>Copyright of IEEE Transactions on Geoscience & Remote Sensing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/TGRS.2015.2424298
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        Text: English
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      – SubjectFull: Detectors
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      – SubjectFull: Multiple criteria decision making
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      – SubjectFull: Algorithms
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      – SubjectFull: Cluster theory (Nuclear physics)
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              Text: Oct2015
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