Method and device for remote sensing monitoring of surface anomalies based on cyclic neural network

Abstract

The invention discloses a method and device for remote sensing monitoring of surface anomalies based on a cyclic neural network. The method includes: inputting the pre-training sequence into an encoder constructed on the basis of a bidirectional cyclic neural network to obtain the global information used to characterize the pre-training sequence The hidden state sequence of the previous segment; the value of the previous moment is input to the decoder based on the single-layer recurrent neural network in turn, so that the predicted value corresponding to the moment in the sequence of the latter segment of the training is sequentially obtained; the encoder-decoder model is used to predict the future moment Observation locations for prediction. By adopting the above scheme, it is possible to mine the overall change rule of the historical sequence and predict the observed values ​​at multiple times in the future, so as to realize near-real-time anomaly monitoring and real-time monitoring and supervision. At the same time, less data preprocessing and manual setting of empirical parameters are required. The experience and professional background of the implementer are not high, and it is applicable to various geographical regions and land cover types. The method has high feasibility, robustness and accuracy of prediction results.

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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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 land cover types in the prediction-based scheme, it is difficult to find a suitable mathematical model to reflect the internal change law and essential characteristics of the time series, resulting in the gap between the model prediction value and the real value. Large error, the accuracy of anomaly monitoring is not ideal

Therefore, the above two types of methods have their own defects, and their robustness and applicability are not strong.

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