Recurrent neural network-based remote sensing monitoring method and device for ground surface abnormal phenomenons

A cyclic neural network and abnormal phenomenon technology, which is applied in the field of remote sensing monitoring of surface anomalies based on cyclic neural network, can solve the problem that the robustness and applicability are not strong, it is difficult to find the change law and essential characteristics of the mathematical model, and the abnormal monitoring accuracy is not high. ideals etc.

Active Publication Date: 2020-04-10
南京国诚土地整治研究院有限公司
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Problems solved by technology

[0004] However, both of the above two schemes have disadvantages: on the one hand, the scheme based on time series decomposition assumes that the remote sensing time series conforms to strict periodicity, and can use a linear function to approximate the trend item, which is inconsistent with the real law of surface change; on the other hand, On the one hand, due to the complexity and variety of time-domain change patterns of different la

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  • Recurrent neural network-based remote sensing monitoring method and device for ground surface abnormal phenomenons
  • Recurrent neural network-based remote sensing monitoring method and device for ground surface abnormal phenomenons
  • Recurrent neural network-based remote sensing monitoring method and device for ground surface abnormal phenomenons

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[0049] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.

[0050] The schematic flow chart of the method for predicting surface anomalies based on the cyclic neural network provided in the embodiment of the present invention includes specific steps, which will be described in detail below in conjunction with the specific steps.

[0051] Step S101, obtaining the observation value of the observation location.

[0052] In the specific implementation, the observation location is the target location that needs to be observed and predicted; the observation value is usually expressed as the historical remote sensing image data of the observation location. Images can be acquired by remote sensing satellites according to the same revisit cycle, or by other image acquisition methods according to a certain time rule.

[0053] Step S102, forming a historical sequence of the observed values ​​of the observed loca...

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Abstract

The invention discloses a recurrent neural network-based remote sensing monitoring method and a device for ground surface abnormal phenomenons. The method comprises the steps: inputting a training front-segment sequence into an encoder constructed based on a bidirectional recurrent neural network, and obtaining a front-segment hidden state sequence for representing the global information of the training front-segment sequence; sequentially inputting the numerical value of the last moment into a decoder based on a single-layer recurrent neural network, so as to sequentially obtain predicted values corresponding to the moments in the training back-end sequence; adopting an encoder-decoder model to predict an observation location at a future moment. By the adoption of the above scheme, an overall change rule of a historical sequence can be mined and observation values at multiple moments in the future can be predicted; near-real-time anomaly monitoring and real-time monitoring and supervising are achieved, meanwhile, required data preprocessing and manually set experience parameters are few, the requirements for experience and professional backgrounds of implementers are not high, themethod is suitable for various geographic areas and land cover types, and the method is high in feasibility, robustness and prediction result accuracy.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a remote sensing monitoring method and device for surface anomalies based on a cyclic neural network. Background technique [0002] Remote sensing time series data records the continuous change process of the earth's surface, and its change pattern over time deeply reflects a variety of natural geographical processes and vegetation phenological rhythms, and has complex nonlinear characteristics of periodicity, trend and randomness. By analyzing the historical remote sensing time series data accumulated over a long period of time in the same area, and mining its internal structure and time-varying laws, it is possible to perform near-real-time monitoring and early warning of various abnormal phenomena caused by human activities or natural disasters. [0003] Existing near-real-time monitoring and early warning of anomalies, the adopted remote sensing time series anomaly detection sc...

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Application Information

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IPC IPC(8): G06N3/04G06N3/08G06K9/00G06K9/62G01N21/17G01N21/55
CPCG06N3/08G01N21/17G01N21/55G01N2021/1793G06N3/045
Inventor 袁媛陈一祥李文梅姜杰
Owner 南京国诚土地整治研究院有限公司
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