A Clustering Method for Static Earth Observation Capabilities of Optical Satellite Sensors

Abstract

The invention discloses a clustering method of static earth observation capacity of an optical satellite sensor. The clustering method comprises the steps of obtaining a principal component score of the static earth observation capacity of the optical satellite sensor based on a performance index parameter value of the optical satellite sensor; and clustering the optical satellite sensor by using a self-organizing neural network self organized mapping (SOM) model based on the principal component score. Compared with the prior art, the clustering method of the static earth observation capacity of the optical satellite sensor has the advantages of being quantified, objective and flexible. Support is provided for static observation capacity of an earth observation optical satellite sensor through quantified measurement of a performance parameter, and an accurate method is supplied to sensor classifying according to observation capacity.

Description

technical field [0001] The invention relates to a sensor observation ability evaluation and sensor classification method, in particular to a static earth observation ability clustering method of an optical satellite sensor, which belongs to the field of geospatial information. Background technique [0002] Earth observation sensors are devices used to observe the Earth from space, with remote sensing techniques for observing the surface and atmosphere. These remote sensing techniques are based on the observation of the spectrum and spatial intensity distribution of electromagnetic waves reflected or radiated by the earth and atmosphere. Different earth observation sensors have different observation capabilities when meeting earth observation requests, and only sensors with appropriate capabilities can be used for specific observation tasks. On the one hand, among the existing artificial satellites, there are few earth observation satellites and not many earth observation se...

AI Technical Summary

Problems solved by technology

Human participation highlights the influence of subjectivity and weakens the objectivity of evaluation methods

Insufficient objectivity of the evaluation method will cause a certain deviation between the evaluation results and the objective facts, which will lead to the inability to achieve optimal sensor selection decisions based on the evaluation results, thus failing to maximize the use of earth observation sensor resources

[0009] (2) All performance indicators of the sensor are treated equally, and the importance differences of different indicators cannot be reflected

The existing performance evaluation and classification methods generally treat different indicators equally when classifying. The classification results cannot reflect the importance differences of different ability indicators. Optimal, so that it cannot maximize the use of earth observation sensor resources

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