CN117523418A

CN117523418A - Multisource remote sensing image analysis method and system

Info

Publication number: CN117523418A
Application number: CN202410021334.0A
Authority: CN
Inventors: 张月珍; 孙海笑; 张牧军
Original assignee: Shandong Provincial Institute of Land Surveying and Mapping
Current assignee: Shandong Provincial Institute of Land Surveying and Mapping
Priority date: 2024-01-08
Filing date: 2024-01-08
Publication date: 2024-02-06
Anticipated expiration: 2044-01-08
Also published as: CN117523418B

Abstract

The invention relates to the technical field of image processing, in particular to a multisource remote sensing image analysis method and a multisource remote sensing image analysis system, which comprise the following steps: based on a probability map model of multi-band satellite remote sensing data, a structured learning Bayesian network and a conditional random field algorithm are adopted to analyze the interrelationship and conditional dependence among the multi-source remote sensing data, and a dependence relation map is generated. In the invention, complex dependency relationship among multi-source remote sensing data can be modeled and quantized more accurately by applying a structured learning Bayesian network and a conditional random field algorithm, and secondly, the invention is more comprehensive and effective in predicting and managing volatility and uncertainty in the multi-source data, and in addition, the invention improves the efficiency and effect of processing different types of data sources, and simultaneously, the invention can capture dynamic change and complex behavior patterns among the multi-source data more deeply, and finally, the image quality is effectively improved by applying a remote sensing image enhancement technology based on spectrum deconvolution.

Description

Multisource remote sensing image analysis method and system

Technical Field

The invention relates to the technical field of image processing, in particular to a multisource remote sensing image analysis method and system.

Background

The field of image processing technology is a field focusing on acquiring, analyzing, processing and understanding image data from various sources, in which development of technology and algorithms aims to promote understanding of image content so as to achieve more accurate information extraction and more efficient data utilization, and the image processing technology is widely applied to multiple fields of satellite remote sensing, medical imaging, video monitoring, automatic driving vehicles and the like, wherein multiple aspects of image enhancement, recovery, classification, pattern recognition, image understanding and the like are involved.

Among other things, the objective of the multi-source remote sensing image analysis method is to effectively integrate and analyze images from different remote sensing data sources to improve the accuracy and comprehensiveness of the analysis, and the method aims to overcome the limitations of single data sources, and by combining multiple sources of data to provide a more comprehensive and accurate analysis result, for example, combining image data from different satellites, different sensors, and even different times, environmental changes, urban development, natural disasters, etc. can be more comprehensively monitored and understood.

Although the existing multi-source remote sensing image analysis technology has achieved remarkable results in terms of integrating different data sources and improving accuracy and comprehensiveness of analysis, in the face of more complex and dynamic data environments, the following defects still exist, firstly, the existing technology lacks sufficient precision and depth in terms of processing highly complex and interdependent data relationships, although the technologies such as image registration and image fusion are adopted, but the precise modeling and quantitative analysis of complex probability relationships and condition dependencies among multi-source data are still insufficient, in addition, the traditional method is not comprehensive in terms of processing data volatility, particularly in terms of predicting and managing volatility and uncertainty in the multi-source data, stability and reliability after data integration are easily affected, in addition, the traditional technology uses feature extraction, machine learning and data mining technologies, but the application in terms of multi-task learning and migration learning is not wide enough, which limits the improvement of processing efficiency and effect of the existing technology in terms of different types of data sources (such as optical images and radar images), meanwhile, the existing method is not mature in terms of capturing dynamic changes and complex behavior patterns among the multi-source data, particularly in terms of improving the quality of the dynamic behaviors, and improving the characteristics of the dynamic models, in terms of the dynamic models, enhancing the characteristics of the image analysis, and the dynamic models, and the system is still being applied in terms of improving the characteristics, and improving the performance of the dynamic models, and improving the characteristics, and the characteristics of the analysis, and the aspects of the existing remote sensing image analysis, and the aspects are more difficult in terms of the aspects, and the analysis.

Disclosure of Invention

The invention aims to solve the defects in the prior art and provides a multisource remote sensing image analysis method and a multisource remote sensing image analysis system.

In order to achieve the above purpose, the present invention adopts the following technical scheme: a multisource remote sensing image analysis method comprises the following steps:

s1: based on a probability map model of multi-band satellite remote sensing data, adopting a structured learning Bayesian network and a conditional random field algorithm to analyze the interrelation and conditional dependence among the multi-source remote sensing data and generate a dependence relation map;

s2: based on the dependency graph, analyzing by adopting a generalized autoregressive conditional heteroscedastic model, and quantitatively predicting the fluctuation of a plurality of data sources to generate a fluctuation analysis report;

s3: based on the fluctuation analysis report, adopting a multi-task learning model and a field self-adaptive transfer learning strategy, and processing multi-class image data by a design model to generate a characteristic transfer model;

s4: based on the characteristic migration model, analyzing the dynamic behavior of the multi-source image data by adopting a nonlinear dynamics analysis method, and generating a dynamic behavior analysis report;

s5: based on the dynamic behavior analysis report, estimating the frequency domain characteristics of the image by adopting a frequency spectrum analysis and deconvolution processing technology, and performing frequency spectrum optimization on the image data to generate optimized image data;

S6: based on the optimized image data, adopting a geometric transformation algorithm and a convolutional neural network to synchronously learn, and carrying out image alignment to generate aligned and synchronized image data;

s7: based on the aligned and synchronized image data, integrating the dependency graph, the volatility analysis report, the characteristic migration model, the dynamic behavior analysis report and the optimized image data by adopting a multi-view data fusion technology to perform data integration processing to generate a final image data set.

As a further scheme of the invention, the dependency graph comprises data nodes, associated edges and condition dependency indexes, the fluctuation analysis report comprises a fluctuation index, a prediction trend and a key fluctuation factor, the characteristic migration model comprises a characteristic extraction rule, an adaptive parameter and a cross-domain adjustment factor, the dynamic behavior analysis report comprises a time sequence mode, a behavior trend and a dynamic change index, the optimized image data comprises a frequency domain characteristic result and image quality improvement details, the aligned and synchronized image data comprises a spatial alignment result and a synchronous learning parameter, and the final image data set comprises a comprehensive fusion result and multi-view analysis information.

As a further scheme of the invention, based on a probability map model of multi-band satellite remote sensing data, a structured learning Bayesian network and a conditional random field algorithm are adopted, and the steps of analyzing the interrelation and conditional dependence among the multi-source remote sensing data and generating a dependence relation map are specifically as follows:

s101: based on a probability map model of multiband satellite remote sensing data, evaluating the correlation among multiple wavebands by adopting a covariance analysis method, and generating a correlation analysis report;

s102: based on the correlation analysis report, adopting a graph theory algorithm to perform preliminary network structure construction, and generating an initial network structure diagram;

s103: based on the initial network structure diagram, adopting a Bayesian network parameter estimation method to refine probability relations in a network and generating an optimized probability network diagram;

s104: describing the conditional dependence relationship among the multi-source data by adopting a conditional random field algorithm based on the optimized probability network diagram, and generating a dependence relationship diagram;

the covariance analysis method comprises a Pearson correlation coefficient and a Spearman rank correlation coefficient, the graph theory algorithm comprises a shortest path algorithm and a network flow algorithm, the Bayesian network parameter estimation method comprises maximum likelihood estimation and Bayesian estimation, and the conditional random field algorithm is specifically a linear chain conditional random field and a graph structure conditional random field.

As a further scheme of the present invention, based on the dependency graph, a generalized autoregressive conditional heteroscedastic model is adopted to analyze, and the fluctuation of a plurality of data sources is quantitatively predicted, and the step of generating a fluctuation analysis report specifically includes:

s201: based on the dependency graph, a time sequence analysis method is adopted to explore the time sequence characteristics of the data, and a time sequence fluctuation report is generated;

s202: analyzing data fluctuation by adopting an autoregressive model based on the time sequence fluctuation report, and generating an autoregressive model analysis report;

s203: based on the autoregressive model analysis report, adopting a generalized autoregressive conditional heteroscedastic model to analyze the fluctuation trend, and generating a fluctuation trend analysis report;

s204: based on the fluctuation trend analysis report, combining the analysis report content of the autoregressive model, and adopting a statistical analysis technology to synthesize a multi-fluctuation analysis result so as to generate a fluctuation analysis report;

the time sequence analysis method comprises an autoregressive moving average model and a Kalman filter, the autoregressive model is specifically a moving average model and an autoregressive moving average model, the generalized autoregressive conditional heteroscedastic model comprises a basic GARCH model, an exponential GARCH model and a threshold GARCH model, and the statistical analysis technology comprises hypothesis test and confidence interval analysis.

As a further scheme of the invention, based on the fluctuation analysis report, a multi-task learning model and a field self-adaptive transfer learning strategy are adopted, the design model processes multiple types of image data, and the step of generating the characteristic transfer model specifically comprises the following steps:

s301: based on the fluctuation analysis report, carrying out preliminary multi-source data fusion by adopting an algorithm combination technology to generate a preliminary multi-task processing frame;

s302: based on the preliminary multi-task processing frame, adopting an optimization algorithm to adjust the processing capacity of the frame, and generating an optimized multi-task processing frame;

s303: based on the optimized multi-task processing frame, optimizing the processing efficiency by adopting a field self-adaptive transfer learning strategy, and generating a field adaptive transfer learning model;

s304: based on the field adaptive transfer learning model, combining the fluctuation analysis report, adopting a deep learning network adjustment technology to carry out final model design, and generating a characteristic transfer model;

the algorithm combination technology comprises a stacking model and a model fusion technology, the optimization algorithm is specifically a genetic algorithm and a simulated annealing algorithm, the field self-adaptive migration learning strategy comprises migration component analysis and a field countermeasure network, and the deep learning network adjustment technology comprises convolutional neural network adjustment and cyclic neural network optimization.

As a further scheme of the invention, based on the characteristic migration model, a nonlinear dynamics analysis method is adopted to analyze the dynamic behaviors of the multi-source image data, and the step of generating a dynamic behavior analysis report specifically comprises the following steps:

s401: based on the characteristic migration model, acquiring dynamic behavior characteristics of the image data by adopting a nonlinear dynamics analysis method, and generating a dynamic behavior characteristic report;

s402: based on the dynamic behavior characteristic report, adopting chaos theory analysis to analyze the dynamic behavior of the image data and generate a chaos characteristic analysis report;

s403: based on the chaos characteristic analysis report, fractal dimension calculation is adopted to evaluate the fractal attribute of the image data, and a fractal characteristic evaluation report is generated;

s404: based on the fractal characteristic evaluation report, predicting future behaviors of the image data by adopting a dynamic prediction model, and generating a dynamic behavior analysis report;

the nonlinear dynamics analysis method comprises singular attractor recognition and Lyapunov index analysis, the chaos theory analysis comprises singular attractor analysis and bifurcation theory, the fractal dimension calculation is specifically a box dimension and a correlation dimension, and the dynamic prediction model comprises phase space prediction and a dynamic planning algorithm.

As a further scheme of the present invention, based on the dynamic behavior analysis report, a spectrum analysis and deconvolution processing technique is adopted to estimate the frequency domain characteristics of the image, and the spectrum optimization of the image data is performed, and the steps of generating the optimized image data specifically include:

s501: based on the dynamic behavior analysis report, exploring the frequency domain characteristics of the image data by adopting a spectrum analysis technology to generate a spectrum characteristic report;

s502: based on the spectrum characteristic report, adopting harmonic analysis to evaluate frequency components of the image data, and generating a harmonic analysis report;

s503: based on the harmonic analysis report, adopting a signal reconstruction technology to adjust the frequency domain representation of the image and generating signal reconstruction data;

s504: based on the signal reconstruction data, carrying out final spectrum optimization processing by adopting a frequency domain optimization algorithm to generate optimized image data;

the spectrum analysis technology comprises group velocity analysis and spectrum decomposition, the harmonic analysis comprises Fourier transformation and wavelet transformation, the signal reconstruction technology comprises inverse Fourier transformation and inverse wavelet transformation, and the frequency domain optimization algorithm comprises band-pass filter design and adaptive spectrum enhancement technology.

As a further scheme of the present invention, based on the optimized image data, a geometric transformation algorithm and a convolutional neural network are adopted for synchronous learning, and image alignment is performed, and the steps of generating aligned and synchronized image data specifically include:

s601: based on the optimized image data, adopting a space transformation network to adjust the space characteristics of the image and generating space-adjusted image data;

s602: based on the image data subjected to the space adjustment, performing image alignment operation by adopting an image alignment technology based on deep learning, and generating image alignment data;

s603: based on the image alignment data, optimizing network representation by adopting a convolutional neural network-based network optimization technology, and generating network-adjusted data;

s604: based on the data after the network adjustment, adopting an image fusion technology to perform final image processing to generate aligned and synchronized image data;

the spatial transformation network comprises coordinate transformation and shape adjustment, the image alignment technology based on deep learning comprises feature alignment and structure alignment, the network optimization technology based on the convolutional neural network comprises residual error learning and attention mechanism, and the image fusion technology comprises multi-scale fusion and semantic hierarchy fusion.

As a further scheme of the present invention, based on the aligned and synchronized image data, the steps of integrating the dependency graph, the volatility analysis report, the feature migration model, the dynamic behavior analysis report and the optimized image data by using a multi-view data fusion technology, and generating a final image dataset are specifically as follows:

s701: based on the aligned and synchronized image data, adopting a high-dimensional data mapping technology to convert a data form and generate mapped image data;

s702: based on the mapped image data, combining the dependency relationship graph, adopting network analysis and a node clustering algorithm to identify key data connection and generating graph theory analysis data;

s703: based on the graph theory analysis data, combining the fluctuation analysis report and the dynamic behavior analysis report, adopting a Bayesian model fusion and random forest fusion technology, integrating statistical features, and generating statistical feature fusion data;

s704: based on the statistical feature fusion data, combining the optimized image data, adopting manifold learning fusion and deep learning fusion technology to realize final integration and generate a final image data set;

The high-dimensional data mapping technology comprises t-SNE and multidimensional scaling, the network analysis and node clustering algorithm is specifically a community detection algorithm and spectral clustering, the Bayesian model fusion is specifically Bayesian probability fusion and Bayesian parameter estimation, the random forest fusion technology is specifically a feature random subspace method and a decision tree integrated learning method, and the manifold learning fusion and deep learning fusion technology is specifically Isomap and neural network integration.

The system comprises a data fusion module, a fluctuation analysis module, a characteristic migration module, a dynamic behavior analysis module, a frequency domain optimization module, a space alignment module, a deep learning alignment module and a final fusion module;

the data fusion module carries out correlation evaluation among wavebands by adopting a covariance analysis method based on a probability map model of multiband satellite remote sensing data to generate a correlation analysis report;

the fluctuation analysis module explores the fluctuation characteristics of the data by adopting a time sequence analysis method based on the correlation analysis report to generate a time sequence fluctuation report;

The feature migration module adopts a multi-task learning and field self-adaptive migration learning strategy to carry out model design based on a time sequence fluctuation report, and generates a feature migration model;

the dynamic behavior analysis module adopts a nonlinear dynamics analysis method to explore the dynamic behavior of the image data based on the characteristic migration model, and generates a dynamic behavior characteristic report;

the frequency domain optimization module carries out image frequency domain characteristic estimation by adopting a frequency spectrum analysis and deconvolution processing technology based on a dynamic behavior characteristic report to generate signal reconstruction data;

the space alignment module is used for carrying out image space characteristic adjustment by adopting a space transformation network based on the signal reconstruction data to generate space adjusted image data;

the deep learning alignment module performs image alignment operation by adopting an image alignment technology based on deep learning based on the image data after the spatial adjustment to generate image alignment data;

the final fusion module is used for integrating multiple types of image data by adopting a multi-view data fusion technology based on the image alignment data to generate a final image data set.

Compared with the prior art, the invention has the advantages and positive effects that:

According to the invention, by applying the structured learning Bayesian network and the conditional random field algorithm, the complex dependency relationship between the multi-source remote sensing data can be modeled and quantized more accurately, the understanding of the correlation between the data is improved, so that the analysis result is more accurate and deep, and then, a fluctuation analysis model in the financial market, such as a GARCH model, is introduced, so that the dynamic change and complex behavior mode between the multi-source data can be more comprehensively and effectively predicted and managed, the stability and reliability of the integrated data are improved, the prediction capability of data processing is enhanced, in addition, the efficiency and effect of processing different types of the data sources are greatly improved by applying the multi-task learning and migration learning technology, the data of different sources can be integrated and analyzed more quickly and accurately, the analysis result is particularly applied to a feature migration model, the learning process can be accelerated remarkably, the data integration efficiency is improved, and meanwhile, the dynamic change and the complex behavior mode between the multi-source data can be captured more comprehensively and effectively in the aspect of predicting and managing the fluctuation and uncertainty in the multi-source data, the final convolutional analysis is provided for the image, the image is more abundant, the image is more accurate and the image is based on the characteristics of the complex analysis, and the image is more obvious and the quality is improved, and the image is more obvious due to the fact that the image is based on the complex analysis, and the characteristics are better in the aspect of the advanced and the advanced analysis technology.

Drawings

FIG. 1 is a schematic workflow diagram of the present invention;

FIG. 2 is a S1 refinement flowchart of the present invention;

FIG. 3 is a S2 refinement flowchart of the present invention;

FIG. 4 is a S3 refinement flowchart of the present invention;

FIG. 5 is a S4 refinement flowchart of the present invention;

FIG. 6 is a S5 refinement flowchart of the present invention;

FIG. 7 is a S6 refinement flowchart of the present invention;

FIG. 8 is a S7 refinement flowchart of the present invention;

fig. 9 is a system flow diagram of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.

Embodiment one:

referring to fig. 1, the present invention provides a technical solution: a multisource remote sensing image analysis method comprises the following steps:

s2: based on the dependency graph, a generalized autoregressive conditional heteroscedastic model is adopted for analysis, and the fluctuation of a plurality of data sources is quantitatively predicted, so that a fluctuation analysis report is generated;

s4: based on the characteristic migration model, a nonlinear dynamics analysis method is adopted to analyze the dynamic behaviors of the multi-source image data, and a dynamic behavior analysis report is generated;

s5: based on a dynamic behavior analysis report, estimating the frequency domain characteristics of the image by adopting a frequency spectrum analysis and deconvolution processing technology, and performing frequency spectrum optimization on the image data to generate optimized image data;

S7: based on the aligned and synchronized image data, integrating the dependency graph, the volatility analysis report, the characteristic migration model, the dynamic behavior analysis report and the optimized image data, and adopting a multi-view data fusion technology to perform data integration processing to generate a final image data set.

The dependency relation graph comprises data nodes, associated edges and condition dependency indexes, the fluctuation analysis report comprises a fluctuation index, a prediction trend and a key fluctuation factor, the characteristic migration model comprises a characteristic extraction rule, an adaptability parameter and a cross-domain adjustment factor, the dynamic behavior analysis report comprises a time sequence mode, a behavior trend and a dynamic change index, the optimized image data comprises a frequency domain characteristic result and image quality improvement details, the aligned and synchronized image data comprises a spatial alignment result and a synchronous learning parameter, and the final image data set comprises a comprehensive fusion result and multi-view analysis information.

By adopting the structured learning Bayesian network and the conditional random field algorithm, the method can accurately reveal and analyze the conditional probability relation and the dependence path between the multiband satellite remote sensing data, so that the interrelation and the dependence between the data are deeply understood and quantized, and the accuracy and the depth of the data analysis are remarkably improved. In particular, in applications in the fields of environmental monitoring, urban development, natural disasters, etc., the method can provide more comprehensive and accurate analysis results.

By utilizing the generalized autoregressive conditional heteroscedastic model to carry out fluctuation analysis, the method can quantitatively predict the fluctuation of a plurality of data sources. Uncertainty and variation in multi-source data processing can be more effectively identified and managed, and stability and reliability of the integrated data are enhanced. Particularly, the method is critical to coping with natural changes and artificial interference in remote sensing data, and is beneficial to improving the monitoring accuracy of environment or other changes.

The application of the multi-task learning model and the field self-adaptive transfer learning strategy greatly improves the efficiency of processing multi-class image data. Different types of remote sensing data, such as optical images and radar images, can be simultaneously learned and processed, knowledge obtained on one data source can be applied to other data sources through migration learning strategies, so that the learning process is accelerated, and the efficiency and effect of data integration are improved.

The dynamic behavior of the multi-source image data is analyzed by adopting a nonlinear dynamics analysis method, so that the hidden dynamic mode and behavior in the image, such as dynamic change of an ecosystem caused by climate change, can be revealed, and a new visual angle and deeper insight of image analysis are provided. Particularly has great application value in the aspects of understanding and predicting environmental changes and the like.

Image data spectrum optimization by spectrum analysis and deconvolution processing techniques significantly improves image quality, particularly when dealing with image blurring or distortion due to different sensor characteristics. Not only improves the readability and information content of the image, but also provides a higher-quality data basis for subsequent image analysis and decision.

Referring to fig. 2, based on a probability map model of multi-band satellite remote sensing data, a structured learning bayesian network and a conditional random field algorithm are adopted to analyze the interrelation and conditional dependence among the multi-source remote sensing data, and the step of generating a dependency relationship map specifically includes:

s103: based on the initial network structure diagram, adopting a Bayesian network parameter estimation method to refine the probability relation in the network and generating an optimized probability network diagram;

In step S101, correlation among multiple wave bands is evaluated by using a covariance analysis method based on a probability map model of multi-band satellite remote sensing data. This process involves the use of Pearson and Spearman rank correlation coefficients, both of which can effectively measure linear or nonlinear correlations between different remote sensing data bands. And finally, generating a comprehensive correlation analysis report which reveals the degree of correlation between different data bands and provides important basic information for subsequent data processing.

In step S102, a preliminary initial network structure diagram is constructed using a graph theory algorithm based on the obtained correlation analysis report. This step involves applying shortest path algorithms, network flow algorithms, etc., which help reveal structural relationships and flow characteristics between the data. Therefore, the complex relationship among the multi-source remote sensing data can be visualized and understood more clearly, and a foundation is laid for further data processing.

In step S103, based on the generated initial network structure diagram, a bayesian network parameter estimation method is adopted to refine the probability relation in the network. In this step, maximum likelihood estimates and bayesian estimates are used to accurately determine the probability distribution of the various nodes and edges in the network. Not only the accuracy of the network model is enhanced, but also the model can better reflect the dependence and the relevance between actual data.

In step S104, based on the optimized probability network diagram, a conditional random field algorithm is used to further describe the conditional dependency relationship between the multisource data. Here, linear chain member random fields and graph structure conditional random fields are used to accurately model and analyze the conditional dependencies between data. The completion of the step marks the generation of the dependency graph, so that complex inter-dependency relationships among data are more visual and easier to understand, and a solid foundation is provided for subsequent data analysis and processing.

Referring to fig. 3, based on the dependency graph, the generalized autoregressive conditional heteroscedastic model is adopted to analyze, and quantitatively predict the volatility of a plurality of data sources, and the step of generating a volatility analysis report specifically includes:

S201: based on the dependency graph, a time sequence analysis method is adopted to explore the time sequence characteristics of the data and generate a time sequence fluctuation report;

s203: based on the analysis report of the autoregressive model, analyzing the fluctuation trend by adopting a generalized autoregressive conditional heteroscedastic model, and generating an analysis report of the fluctuation trend;

s204: based on the fluctuation trend analysis report, combining the analysis report content of the autoregressive model, adopting a statistical analysis technology to synthesize a multi-fluctuation analysis result, and generating a fluctuation analysis report;

the time sequence analysis method comprises an autoregressive moving average model and a Kalman filter, wherein the autoregressive model is specifically a moving average model and an autoregressive moving average model, the generalized autoregressive condition heteroscedastic model comprises a basic GARCH model, an exponential GARCH model sum and a threshold GARCH model, and the statistical analysis technology comprises hypothesis test and confidence interval analysis.

In step S201, a time-series analysis method is employed to search for time-series characteristics of multi-source remote sensing data based on the dependency graph. The method comprises an autoregressive moving average model and Kalman filtering, and is helpful for identifying and analyzing the change trend of data with time, so as to generate a time sequence fluctuation report. The report details the fluctuation characteristics of the data over time, providing a basis for subsequent fluctuation analysis.

In step S202, based on the time-series volatility report, an autoregressive model is used to perform deeper analysis on the data volatility. Including the application of a running average model and an autoregressive running average model, the inherent rules and patterns of data fluctuations can be further revealed. By analyzing the models, an autoregressive model analysis report is generated, and the fluctuation behavior and possible influencing factors of the data are recorded in detail.

In step S203, based on the autoregressive model analysis report, the generalized autoregressive conditional heteroscedastic model (such as the basic GARCH model, the exponential GARCH model and the threshold GARCH model) is further used to analyze the volatility trend of the data. Thereby enabling more accurate quantification and prediction of the volatility of the data, generating a volatility trend analysis report providing predictions and trend analysis about future volatility.

In step S204, based on the volatility trend analysis report, and in combination with the contents of the autoregressive model analysis report, a statistical analysis technique (including hypothesis testing and confidence interval analysis) is used to synthesize a variety of volatility analysis results. The comprehensive process involves summarizing and interpreting various volatility analysis results to ultimately generate a volatility analysis report. The report provides a comprehensive and deep understanding of the volatility of the multi-source remote sensing data, and provides an important basis for further data processing and decision making.

Referring to fig. 4, based on a volatility analysis report, a multitasking learning model and a domain self-adaptive migration learning strategy are adopted, a design model processes multiple types of image data, and the step of generating a feature migration model specifically includes:

s301: based on the fluctuation analysis report, adopting an algorithm combination technology to perform preliminary multi-source data fusion to generate a preliminary multi-task processing frame;

s303: based on the optimized multitasking frame, optimizing the processing efficiency by adopting a field self-adaptive transfer learning strategy, and generating a field adaptive transfer learning model;

s304: based on the field adaptive migration learning model, combining with a fluctuation analysis report, adopting a deep learning network adjustment technology to carry out final model design, and generating a characteristic migration model;

In step S301, a preliminary multi-source data processing framework is constructed by employing an algorithm combining technique based on the volatility analysis report. This step involves the application of stacked models and model fusion techniques that cooperate to form a preliminary multi-tasking framework capable of processing data from different remote sensing data sources. Therefore, the remote sensing data of different types can be processed simultaneously, thereby laying a foundation for higher-level analysis and processing.

In step S302, an optimization algorithm is employed to adjust and enhance the processing power of the framework based on the preliminarily constructed multitasking framework. Optimization algorithms include genetic algorithms and simulated annealing algorithms that optimize and adjust the multitasking framework to more efficiently process complex and diverse data. By this step an optimized multitasking framework is created that has more powerful data processing capabilities and higher efficiency.

In step S303, a domain adaptive migration learning strategy is applied to further optimize the processing efficiency based on the optimized multitasking framework. Including migration component analysis and domain countermeasure networks, which enable the model to better adapt to data characteristics of different domains, thereby generating a domain adaptive migration learning model. The method not only can process the existing data types, but also can adapt to the new and unseen data types, and greatly enhances the applicability and flexibility of the model.

In step S304, based on the field adaptive transfer learning model, and combining with the fluctuation analysis report, adopting the deep learning network adjustment technology to carry out final model design. The application of the techniques, including convolutional neural network adjustment and cyclic neural network optimization, enables the final feature migration model to more accurately identify and utilize key features in the multi-source remote sensing data. Through the step, a characteristic migration model capable of effectively processing and analyzing the multi-source data is generated, and strong technical support is provided for multi-source remote sensing image analysis.

Referring to fig. 5, based on a feature migration model, a nonlinear dynamics analysis method is adopted to analyze dynamic behaviors of multi-source image data, and the steps of generating a dynamic behavior analysis report specifically include:

s401: based on the feature migration model, a nonlinear dynamics analysis method is adopted to acquire dynamic behavior features of the image data, and a dynamic behavior feature report is generated;

S404: based on the fractal characteristic evaluation report, predicting the future behavior of the image data by adopting a dynamic prediction model, and generating a dynamic behavior analysis report;

the nonlinear dynamics analysis method comprises singular attractor identification and Lyapunov index analysis, the chaos theory analysis comprises singular attractor analysis and bifurcation theory, the fractal dimension calculation is specifically a box dimension and a correlation dimension, and the dynamic prediction model comprises phase space prediction and a dynamic programming algorithm.

In step S401, based on the feature migration model, the multi-source image data is converted and features are extracted, and then nonlinear dynamics analysis methods (such as singular attractor recognition and Lyapunov index analysis) are applied to reveal dynamic behavior features of the data, so as to generate a dynamic behavior feature report:

example code

import numpy as np

from some_dynamics_library import NonlinearDynamicsAnalyzer

def analyze_dynamic_behavior(feature_transfer_model, image_data):

# assume feature_transfer_model is a trained model

# image_data is input image data

transformed_features = feature_transfer_model.transform(image_data)

# use nonlinear kinetic analysis method

dynamics_analyzer = NonlinearDynamicsAnalyzer()

dynamic_features = dynamics_analyzer.analyze(transformed_features)

# generating dynamic behavior characteristics report

dynamic_behavior_report = create_report(dynamic_features)

return dynamic_behavior_report

Example #

image_data=load_image_data () # load data

feature_transfer_model=load_model () # load model

dynamic_behavior_report = analyze_dynamic_behavior(feature_transfer_model, image_data)。

In step S402, according to the dynamic behavior characteristic report, the nonlinear dynamic behavior of the image data is further studied by applying the chaos theory analysis (including the singular attractor analysis and the bifurcation theory), and possible chaos features are identified, so as to generate a chaos characteristic analysis report:

Example code

from chaos_theory import ChaosAnalyzer

def chaos_theory_analysis(dynamic_behavior_report):

chaos_analyzer = ChaosAnalyzer()

chaos_features = chaos_analyzer.analyze(dynamic_behavior_report)

# generating chaotic characteristic analysis report

chaos_report = create_report(chaos_features)

return chaos_report

Example #

chaos_report = chaos_theory_analysis(dynamic_behavior_report)。

In step S403, based on the chaotic characteristic analysis report, a fractal dimension calculation method (such as box dimension and association dimension calculation) is adopted to evaluate the fractal attribute of the image data, so as to facilitate understanding of the complex geometric structure of the image data and generate a fractal characteristic evaluation report:

example code

from fractals import FractalDimensionCalculator

def calculate_fractal_dimensions(chaos_report):

calculator = FractalDimensionCalculator()

fractal_dimensions = calculator.calculate(chaos_report)

# generating fractal property evaluation report

fractal_report = create_report(fractal_dimensions)

return fractal_report

Example #

fractal_report = calculate_fractal_dimensions(chaos_report)。

In step S404, using the fractal property evaluation report as input, a dynamic prediction model (e.g., a phase space prediction or dynamic programming algorithm) is used to predict future behavior patterns of the image data, generating a dynamic behavior analysis report:

example code

from prediction_model import DynamicPredictor

def dynamic_prediction(fractal_report):

predictor = DynamicPredictor()

future_behavior = predictor.predict(fractal_report)

# generating dynamic behavior analysis report

dynamic_prediction_report = create_report(future_behavior)

return dynamic_prediction_report

Example #

dynamic_prediction_report = dynamic_prediction(fractal_report)。

Referring to fig. 6, based on a dynamic behavior analysis report, frequency domain characteristics of an image are estimated by adopting spectrum analysis and deconvolution processing technology, spectrum optimization of image data is performed, and the steps of generating optimized image data are specifically as follows:

s502: based on the spectrum characteristic report, evaluating frequency components of the image data by adopting harmonic analysis to generate a harmonic analysis report;

In step S501, frequency domain features of image data are explored by employing spectroscopic techniques based on dynamic behavior analysis reports. This step covers group velocity analysis and spectral decomposition methods to reveal the distribution and characteristics of the image data in the frequency domain. Finally, a detailed spectrum characteristic report is generated, and deep insight into the frequency domain characteristics of the image data is provided.

In step S502, a harmonic analysis method is applied to evaluate the frequency content of the image data according to the spectral characteristics report. Including fourier transform and wavelet transform techniques, for analyzing frequency components and periodic features in image data. And finally, generating a harmonic analysis report, and recording the performances of the image data on different frequencies in detail.

In step S503, a signal reconstruction technique is employed to adjust the frequency domain representation of the image based on the harmonic analysis report. Here, an inverse fourier transform and an inverse wavelet transform are applied to the original image data to improve the frequency domain characteristics thereof, particularly for correction of noise and distortion. Signal reconstruction data are generated which demonstrate the effect of the image after frequency domain optimization.

In step S504, based on the signal reconstruction data, a frequency domain optimization algorithm is applied to perform final spectrum optimization processing. This includes bandpass filter designs and adaptive spectral enhancement techniques that further optimize the frequency domain characteristics of the image, particularly in terms of noise reduction while retaining important information. Finally, optimized image data are generated, and the data show higher quality and definition.

Referring to fig. 7, based on the optimized image data, the geometric transformation algorithm and the convolutional neural network are adopted to synchronously learn, and the image alignment is performed, so that the steps of generating the aligned and synchronized image data are specifically as follows:

s601: based on the optimized image data, adopting a space transformation network to adjust the space characteristics of the image and generating the image data after space adjustment;

S602: based on the image data after the space adjustment, performing image alignment operation by adopting an image alignment technology based on deep learning to generate image alignment data;

s603: based on the image alignment data, adopting a convolutional neural network-based network optimization technology to optimize network representation, and generating network-adjusted data;

s604: based on the data after network adjustment, adopting an image fusion technology to perform final image processing to generate aligned and synchronized image data;

In step S601, spatial characteristics of an image are adjusted by employing a spatial transformation network based on the optimized image data. Including coordinate transformation and shape adjustment of the image to improve spatial positioning and alignment of the image. Through these spatial transformations spatially adjusted image data is generated, which is more consistent and standardized in spatial characteristics, providing a solid basis for subsequent processing steps.

In step S602, based on the spatially adjusted image data, an image alignment operation is performed using an image alignment technique based on deep learning. Including feature alignment and structure alignment techniques, image features are precisely adjusted and aligned by a deep learning model. The consistency in characteristics and structure of image data from different sources is ensured, and precisely aligned image data is generated.

In step S603, optimization of the network representation is performed using a convolutional neural network-based network optimization technique based on the image alignment data. Techniques including residual learning and attention mechanisms are used to further enhance the performance of the network and the expressive power of the image data. With this fine tuning, network-tuned data is generated that demonstrates improvements in the quality and characterization of the optimized image.

In step S604, final image processing is performed using an image fusion technique based on the network-adjusted data. This process involves multi-scale fusion and semantic hierarchy fusion techniques for effectively combining data of different image sources together to produce a more rich and informative image result. The completion of this step marks the generation of aligned and synchronized image data that is significantly improved and optimized both in visual and information content.

Referring to fig. 8, based on the aligned and synchronized image data, the steps of integrating the data and generating the final image dataset by using a multi-view data fusion technique are specifically as follows:

s701: based on the aligned and synchronized image data, adopting a high-dimensional data mapping technology to convert the data form and generate mapped image data;

s702: based on the mapped image data, combining the dependency graph, adopting network analysis and a node clustering algorithm to identify key data connection and generating graph theory analysis data;

s703: based on graph theory analysis data, combining a fluctuation analysis report and a dynamic behavior analysis report, adopting a Bayesian model fusion and random forest fusion technology, integrating statistical features, and generating statistical feature fusion data;

the high-dimensional data mapping technology comprises t-SNE and multidimensional scaling, a network analysis and node clustering algorithm is specifically a community detection algorithm and spectral clustering, bayesian model fusion is specifically Bayesian probability fusion and Bayesian parameter estimation, a random forest fusion technology is specifically a feature random subspace method and a decision tree integrated learning method, and a manifold learning fusion and deep learning fusion technology is specifically Isomap and neural network integration.

In step S701, the aligned and synchronized image data is form-converted by employing high-dimensional data mapping techniques such as t-SNE and multi-dimensional scaling. Effectively mapping complex high-dimensional data into lower-dimensional space, thereby facilitating subsequent analysis and processing. This step generates mapped image data, providing a more intuitive and easy to handle data representation.

In step S702, based on the mapped image data, and in combination with the dependency graph, a network analysis and a node clustering algorithm (such as a community detection algorithm and spectral clustering) are applied to identify key data connections. This process helps reveal key structures and relationships in the image data, generating graph theory analysis data, providing a basis for deeper data understanding and analysis.

In step S703, based on the graph theory analysis data, in combination with the volatility analysis report and the dynamic behavior analysis report, bayesian model fusion (including bayesian probability fusion and bayesian parameter estimation) and random forest fusion techniques (including feature random subspace method and decision tree ensemble learning method) are applied. The method integrates the statistical characteristics of different data sources and analysis results, and generates statistical characteristic fusion data, thereby improving the accuracy and reliability of data integration.

Finally, in step S704, manifold learning fusion (e.g., isomap) and deep learning fusion techniques (e.g., neural network integration) are employed in combination with the optimized image data based on the statistical feature fusion data. These advanced techniques combine the geometry and depth features of the data to achieve final integration. The completion of this step marks the generation of the final image dataset, which achieves a higher degree of fusion and optimization both visually and informatively.

Referring to fig. 9, a multi-source remote sensing image analysis system is used for executing the multi-source remote sensing image analysis method, and the system comprises a data fusion module, a volatility analysis module, a feature migration module, a dynamic behavior analysis module, a frequency domain optimization module, a space alignment module, a deep learning alignment module and a final fusion module;

The feature migration module adopts a multitask learning and field self-adaptive migration learning strategy to carry out model design based on a time sequence fluctuation report, and generates a feature migration model;

the frequency domain optimization module carries out image frequency domain characteristic estimation by adopting a frequency spectrum analysis and deconvolution processing technology based on the dynamic behavior characteristic report to generate signal reconstruction data;

the deep learning alignment module performs image alignment operation by adopting an image alignment technology based on deep learning based on the image data after the space adjustment to generate image alignment data;

and the final fusion module is used for integrating multiple types of image data by adopting a multi-view data fusion technology based on the image alignment data to generate a final image data set.

The data fusion module carries out comprehensive analysis on multiband satellite remote sensing data through a probability map model, and understanding and utilization efficiency of different data sources are improved. The module enables the system to capture and fuse important information in the multi-source data, providing a richer and more accurate input for subsequent analysis.

The fluctuation analysis module explores the fluctuation of the data through a time sequence analysis method, so that the system can understand and predict the change trend of the environment or the target. Has important value for dynamic environment monitoring and long-term trend analysis.

The feature transfer module utilizes a multi-task learning and transfer learning strategy to enhance the adaptability and efficiency of the model in processing different types of image data. The universality and the flexibility of the system under various data environments are improved.

The dynamic behavior analysis module adopts a nonlinear dynamics method to deeply analyze the dynamic behavior of the image data, and reveals the changes and modes which are difficult to observe in a complex environment. Is particularly important for understanding the complex dynamics of natural and man-made environments.

The frequency domain optimization module optimizes the frequency domain characteristics of the image data through spectrum analysis and deconvolution processing technology, and remarkably improves the image quality. Is important for improving the readability and analysis accuracy of the image.

The spatial alignment module uses a spatial transformation network to carry out spatial feature adjustment and alignment on the images, so that the consistency of the images of different data sources in space is ensured. Lays a foundation for high-quality image fusion.

The deep learning alignment module further optimizes the image alignment process through a deep learning technology, enhances the consistency of image data in characteristics and structures, and provides high-quality input for final fusion and analysis.

The final fusion module combines the multi-view data fusion technology and synthesizes the output of the previous module to generate a comprehensive and information-rich final image data set. The data set not only contains the comprehensive view of the multi-source data, but also optimizes the expression mode of the data, and provides a strong foundation for decision support and further application.

The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.

Claims

1. The multisource remote sensing image analysis method is characterized by comprising the following steps of:

based on a probability map model of multi-band satellite remote sensing data, adopting a structured learning Bayesian network and a conditional random field algorithm to analyze the interrelation and conditional dependence among the multi-source remote sensing data and generate a dependence relation map;

Based on the dependency graph, analyzing by adopting a generalized autoregressive conditional heteroscedastic model, and quantitatively predicting the fluctuation of a plurality of data sources to generate a fluctuation analysis report;

based on the fluctuation analysis report, adopting a multi-task learning model and a field self-adaptive transfer learning strategy, and processing multi-class image data by a design model to generate a characteristic transfer model;

based on the characteristic migration model, analyzing the dynamic behavior of the multi-source image data by adopting a nonlinear dynamics analysis method, and generating a dynamic behavior analysis report;

based on the dynamic behavior analysis report, estimating the frequency domain characteristics of the image by adopting a frequency spectrum analysis and deconvolution processing technology, and performing frequency spectrum optimization on the image data to generate optimized image data;

based on the optimized image data, adopting a geometric transformation algorithm and a convolutional neural network to synchronously learn, and carrying out image alignment to generate aligned and synchronized image data;

based on the aligned and synchronized image data, integrating the dependency graph, the volatility analysis report, the characteristic migration model, the dynamic behavior analysis report and the optimized image data by adopting a multi-view data fusion technology to perform data integration processing to generate a final image data set.

2. The method of claim 1, wherein the steps of: the dependency relation graph comprises data nodes, associated edges and condition dependency indexes, the fluctuation analysis report comprises a fluctuation index, a prediction trend and a key fluctuation factor, the feature migration model comprises a feature extraction rule, an adaptability parameter and a cross-domain adjustment factor, the dynamic behavior analysis report comprises a time sequence mode, a behavior trend and a dynamic change index, the optimized image data comprises a frequency domain characteristic result and image quality improvement details, the aligned and synchronized image data comprises a spatial alignment result and a synchronous learning parameter, and the final image data set comprises a comprehensive fusion result and multi-view analysis information.

3. The method of claim 1, wherein the steps of: based on a probability map model of multi-band satellite remote sensing data, adopting a structured learning Bayesian network and a conditional random field algorithm to analyze the interrelation and conditional dependence among the multi-source remote sensing data, and generating a dependence relation map specifically comprises the following steps:

based on a probability map model of multiband satellite remote sensing data, evaluating the correlation among multiple wavebands by adopting a covariance analysis method, and generating a correlation analysis report;

Based on the correlation analysis report, adopting a graph theory algorithm to perform preliminary network structure construction, and generating an initial network structure diagram;

based on the initial network structure diagram, adopting a Bayesian network parameter estimation method to refine probability relations in a network and generating an optimized probability network diagram;

describing the conditional dependence relationship among the multi-source data by adopting a conditional random field algorithm based on the optimized probability network diagram, and generating a dependence relationship diagram;

4. A multi-source remote sensing image analysis method according to claim 3, wherein: based on the dependency graph, the generalized autoregressive conditional heteroscedastic model is adopted for analysis, and the fluctuation of a plurality of data sources is quantitatively predicted, and the step of generating a fluctuation analysis report specifically comprises the following steps:

based on the dependency graph, a time sequence analysis method is adopted to explore the time sequence characteristics of the data, and a time sequence fluctuation report is generated;

Analyzing data fluctuation by adopting an autoregressive model based on the time sequence fluctuation report, and generating an autoregressive model analysis report;

based on the autoregressive model analysis report, adopting a generalized autoregressive conditional heteroscedastic model to analyze the fluctuation trend, and generating a fluctuation trend analysis report;

based on the fluctuation trend analysis report, combining the analysis report content of the autoregressive model, and adopting a statistical analysis technology to synthesize a multi-fluctuation analysis result so as to generate a fluctuation analysis report;

5. The method of claim 4, wherein the steps of: based on the fluctuation analysis report, a multi-task learning model and a field self-adaptive transfer learning strategy are adopted, the design model processes multiple types of image data, and the step of generating a characteristic transfer model specifically comprises the following steps:

Based on the fluctuation analysis report, carrying out preliminary multi-source data fusion by adopting an algorithm combination technology to generate a preliminary multi-task processing frame;

based on the preliminary multi-task processing frame, adopting an optimization algorithm to adjust the processing capacity of the frame, and generating an optimized multi-task processing frame;

based on the optimized multi-task processing frame, optimizing the processing efficiency by adopting a field self-adaptive transfer learning strategy, and generating a field adaptive transfer learning model;

based on the field adaptive transfer learning model, combining the fluctuation analysis report, adopting a deep learning network adjustment technology to carry out final model design, and generating a characteristic transfer model;

6. The method of claim 5, wherein the steps of: based on the characteristic migration model, a nonlinear dynamics analysis method is adopted to analyze the dynamic behaviors of the multi-source image data, and the step of generating a dynamic behavior analysis report specifically comprises the following steps:

Based on the characteristic migration model, acquiring dynamic behavior characteristics of the image data by adopting a nonlinear dynamics analysis method, and generating a dynamic behavior characteristic report;

based on the dynamic behavior characteristic report, adopting chaos theory analysis to analyze the dynamic behavior of the image data and generate a chaos characteristic analysis report;

based on the chaos characteristic analysis report, fractal dimension calculation is adopted to evaluate the fractal attribute of the image data, and a fractal characteristic evaluation report is generated;

based on the fractal characteristic evaluation report, predicting future behaviors of the image data by adopting a dynamic prediction model, and generating a dynamic behavior analysis report;

7. The method of claim 6, wherein the steps of: based on the dynamic behavior analysis report, the frequency domain characteristics of the image are estimated by adopting a frequency spectrum analysis and deconvolution processing technology, and the frequency spectrum of the image data is optimized, and the steps for generating the optimized image data are specifically as follows:

Based on the dynamic behavior analysis report, exploring the frequency domain characteristics of the image data by adopting a spectrum analysis technology to generate a spectrum characteristic report;

based on the spectrum characteristic report, adopting harmonic analysis to evaluate frequency components of the image data, and generating a harmonic analysis report;

based on the harmonic analysis report, adopting a signal reconstruction technology to adjust the frequency domain representation of the image and generating signal reconstruction data;

based on the signal reconstruction data, carrying out final spectrum optimization processing by adopting a frequency domain optimization algorithm to generate optimized image data;

8. The method of claim 7, wherein the steps of: based on the optimized image data, adopting a geometric transformation algorithm and a convolutional neural network to synchronously learn, and carrying out image alignment, wherein the step of generating the aligned and synchronized image data comprises the following steps:

Based on the optimized image data, adopting a space transformation network to adjust the space characteristics of the image and generating space-adjusted image data;

based on the image data subjected to the space adjustment, performing image alignment operation by adopting an image alignment technology based on deep learning, and generating image alignment data;

based on the image alignment data, optimizing network representation by adopting a convolutional neural network-based network optimization technology, and generating network-adjusted data;

based on the data after the network adjustment, adopting an image fusion technology to perform final image processing to generate aligned and synchronized image data;

9. The method of claim 8, wherein the steps of: based on the aligned and synchronized image data, the steps of integrating the data by adopting a multi-view data fusion technology and generating a final image data set are specifically as follows:

Based on the aligned and synchronized image data, adopting a high-dimensional data mapping technology to convert a data form and generate mapped image data;

based on the mapped image data, combining the dependency relationship graph, adopting network analysis and a node clustering algorithm to identify key data connection and generating graph theory analysis data;

based on the graph theory analysis data, combining the fluctuation analysis report and the dynamic behavior analysis report, adopting a Bayesian model fusion and random forest fusion technology, integrating statistical features, and generating statistical feature fusion data;

based on the statistical feature fusion data, combining the optimized image data, adopting manifold learning fusion and deep learning fusion technology to realize final integration and generate a final image data set;

10. A multisource remote sensing image analysis system is characterized in that: the multi-source remote sensing image analysis method according to any one of claims 1-9, wherein the system comprises a data fusion module, a volatility analysis module, a feature migration module, a dynamic behavior analysis module, a frequency domain optimization module, a space alignment module, a deep learning alignment module and a final fusion module;