24 results about "Polarimetric sar" patented technology

The invention discloses a crop height inversion method and device based on compact polarimetric SAR data, and relates to the technical field of polarimetric radar remote sensing quantitative inversion, and the crop height inversion method based on compact polarimetric SAR data mainly comprises the steps: carrying out the data preprocessing of an original polarimetric SAR image, and obtaining compact polarimetric data; a multi-dimensional feature set is obtained according to polarization parameters, a machine learning algorithm model is trained and verified by combining a field actually-measured crop height data set to obtain a height prediction model, and crop height estimation data is obtained accordingly; and feature subset optimization is carried out by using a forward feature selection method, the machine learning algorithm model is trained and verified again based on the optimized feature subset to obtain a height prediction model, and the optimized feature subset is predicted to obtain a higher-precision crop height inversion result. According to the crop height inversion method and device based on the compact polarimetric SAR data, the inversion efficiency and precision can be effectively improved.

The application provides a polarization SAR image simulation method for forests under complex terrain conditions, comprising the following steps: (1) setting parameters of a polarization SAR imaging process, including radar imaging parameters, forest parameters and terrain parameters; (2) generating a corresponding terrain model according to the terrain parameters; (3) constructing a forest model according to the forest parameters; (4) calculating a polarization SAR image according to the forest parameter model, and finally simulating the polarization SAR image of the forest under the complex terrain conditions. The method can improve the simulation of the polarization SAR image of the forest under the complex terrain conditions, can be used for understanding the remote sensing mechanism, testing new applications and designing the inversion algorithm of the forest structure, and can further explore the influence of the terrain on the polarization SAR image imaging process.

The application relates to a fast maximum likelihood estimation method of a polarized SAR image equivalent view number graph and relates to the technical field of imaging radar image processing. The method comprises the following steps: obtaining a logarithmic determinant graph of polarized SAR image data by adopting logarithmic operation; calculating a local mean value graph of the polarized SAR logarithmic determinant graph; calculating a local mean value graph of each channel data of the polarized SAR; calculating the determinant value of all pixel data of the polarized SAR image after local averaging, forming a corresponding determinant graph, and obtaining a logarithmic determinant graph by adopting logarithmic operation; obtaining a local sample statistic graph of the polarized SAR data equivalent view number estimation by adopting matrix subtraction; and quickly estimating the equivalent view number graph of the polarized SAR image by using an analytical approximation solving formula of the ML estimation and based on matrix operation. The method avoids the iterative numerical operation and initial interval setting problem of a traditional method, and the fast calculation method based on matrix operation and convolution has obvious efficiency advantages when the equivalent view number graph of the polarized SAR image is estimated.

The invention discloses a passive microwave constrained time sequence complete polarization soil humidity inversion method, which belongs to the technical field of remote sensing image processing, and comprises the following steps of: calculating time and space change ranges of earth surface dielectric constants of a soil humidity mapping area by using time sequence passive microwave observation data as constraint conditions for solving unknown parameters; filtering vegetation body scattering and dihedral angle scattering information by using a dual-stage iterative polarization target decomposition algorithm, and adaptively and optimally extracting back scattering coefficients of surface scattering HH and VV polarization; based on an alpha approximation model and a scattering coefficient ratio algorithm, a time sequence soil humidity inversion algorithm model combining HH and VV polarization observation data is constructed, a passive microwave soil dielectric constant constraint condition is introduced, a soil dielectric constant is solved, and the soil dielectric constant is converted into soil humidity by utilizing a dielectric constant and soil humidity empirical relation model. According to the method, high-resolution soil humidity drawing can be ensured, and the soil humidity inversion and drawing precision of the vegetation coverage area can be improved.

The invention belongs to the technical field of image processing, and discloses a full-polarization SAR image sea surface oil spill extraction method based on an active contour. The method comprises the following steps: S1, obtaining four-channel data of a complete polarization SAR image, wherein the four-channel data covers polarization characteristic difference of oil spill; s2, selecting a closed curve surrounding a suspected oil spill area through visual interpretation based on fusion features of the complete polarization image, and optimizing a contour line through smooth and uniform sampling; s3, maintaining the continuity and expansion characteristics of the contour line by constructing an internal energy function; s4, iteratively adjusting the positions of the contour points according to the minimum energy principle, and judging the termination opportunity by combining the contour stability; s5, filling holes in the area through morphological operation, removing edge small bulges, and optimizing the area form; and S6, verifying the extraction precision through field observation, and judging whether a result meets an application standard or not. According to the invention, accurate segmentation of the oil spill area is realized through the multi-energy item active contour, and the method can be applied to marine environment monitoring and marine oil spill emergency response scenes.

The application discloses a full polarization SAR image ship detection method, comprising the following steps: S1, pre-processing a full polarization SAR image to obtain different types of scattering power and polarization wave anisotropy parameters of each pixel; S2, using the decomposed different scattering power and combining the polarization wave anisotropy parameters to construct a full polarization SAR image detector; S3, using a generalized gamma distribution to describe the statistical characteristics of the full polarization SAR image detector to realize constant false alarm rate adaptive detection of the full polarization SAR image detector. The scattering characteristics and anisotropy of the scattering wave of the ship target are used to fully represent the ship target, so that the ship is strengthened and the background is weakened, and the ship can be detected from the complex marine background.

The invention provides a satellite-borne SAR image denoising method and device, a storage medium and equipment, and the method comprises the steps: obtaining an original polarimetric SAR image which is polarimetric SAR data which is not subjected to the past noise processing and is provided with speckle noise; inputting the original polarimetric SAR image into an SIRV model, and solving the SIRV model based on a fixed point estimation algorithm to obtain a first normalized polarimetric coherence matrix and a first texture grayscale image; and obtaining a polarization coherence matrix of the original polarization SAR image based on the first normalized polarization coherence matrix and the first texture grayscale image. According to the method and the device, polarization denoising can be carried out on the polarization coherence matrix of the satellite-borne SAR image based on the SIRV model, and the problems of large calculation amount, great denoising effect and the like of a mode of carrying out polarization denoising on the polarization coherence matrix of the satellite-borne SAR image by utilizing a Wishart priori distribution SAR speckle filtering algorithm in the prior art are solved.

The invention discloses a multi-band polarized SAR (Synthetic Aperture Radar) image ground object classification method based on wavelet enhancement Swin Transform, and belongs to the technical field of radar remote sensing image processing. Comprising the steps of polarized SAR image preprocessing, image block division, Swin Transform branch feature extraction, wavelet transform branch feature extraction, feature dimension alignment, cross-band attention feature fusion, classification prediction, loss function optimization and end-to-end training optimization. Through a cross-band attention fusion module, a multi-directional cross attention mechanism and learnable weighted residual connection are adopted to realize adaptive deep fusion of spatial domain and multi-band polarization features, and the method solves the contradiction between global context modeling and local detail keeping in a traditional method. The precision and robustness of multi-band polarized SAR image ground feature classification are significantly improved, and a new technical approach is provided for accurate ground feature recognition in a complex ground surface environment.

The application provides a polarimetric SAR bridge detection method fusing section probability modeling and graph topology analysis, first, water and land segmentation is performed on the polarimetric SAR image, and independent water area branches are extracted; for the end of each water area branch, a particle filtering algorithm is used for probability modeling, and a rectangular section representing a potential bridge connection point is robustly extracted; a water area network graph is constructed with the water area branches as nodes and matched section pairs as edges, the sections are accurately connected through a two-stage matching strategy combining bidirectional propagation verification and geometric constraint, and non-river water bodies are filtered out based on geometric features; edges in the network graph are traversed, and the bridge region is accurately positioned according to the geometric relationship between the matched sections. The application utilizes the stable topological characteristics that bridges cause water area rupture and form symmetrical sections, overcomes the problems that traditional methods are unstable, easy to miss detection and misjudgment under complex water area networks, narrow tributaries and strong noise interference, and realizes high-precision and high-robustness detection of multi-scale bridges.

The application discloses a polarimetric SAR image classification method and system, a medium and a program product, relates to the field of polarimetric SAR image classification, and comprises the following steps: acquiring a polarimetric data set; constructing an anchor sample training set and a positive sample pair data set according to the polarimetric data set; constructing a residual neural network model; performing first stage training on the residual neural network model by using the positive sample pair data set, obtaining an intermediate residual neural network model trained through the first stage; performing second stage training on the intermediate residual neural network model by using the polarimetric data set and the anchor sample training set, obtaining a residual neural network model trained through the second stage; and performing clustering on a polarimetric SAR image to be classified by using the residual neural network model trained through the second stage and generating a classification result map. The application can effectively and accurately classify and identify the polarimetric SAR image without any labeling cost.

This invention discloses a five-component power decomposition method and system for compressed polarimetric SAR data, belonging to the field of synthetic aperture radar target decomposition technology. The invention preprocesses the covariance data of each pixel in the acquired compressed polarimetric SAR image to extract two dominant scattering mechanism discriminators for each pixel; determines the dominant scattering mechanism based on the extracted discriminators; selects a scattering model for power decomposition based on the dominant scattering mechanism of each pixel; and performs power decomposition on each scattering model to obtain five-component power. This invention is used for radar target decomposition.

The application belongs to the technical field of image processing, and discloses a sea surface oil spill extraction method based on an active contour of full polarization SAR image; the method comprises the following steps: S1, four-channel data of the full polarization SAR image is acquired, and polarization characteristic differences of the oil spill are covered; S2, based on the fusion features of the full polarization image, a closed curve surrounding a suspected oil spill region is selected by visual interpretation, and the contour line is optimized by smoothing and uniform sampling; S3, the continuity and inflation characteristics of the contour line are maintained by constructing an internal energy function; S4, the position of the contour point is iteratively adjusted according to the minimum energy principle, and the termination time is judged in combination with the contour stability; S5, the region morphology is optimized by filling holes in the region and removing small protrusions on the edge through morphological operation; and S6, the extraction accuracy is verified through field observation, and whether the result meets the application standard is judged. The application realizes accurate segmentation of the oil spill region through a multi-energy term active contour, and can be applied to marine environment monitoring and offshore oil spill emergency response scenarios.

The invention discloses a multi-band polarized SAR (PolSAR) image registration method based on comparative learning in the technical field of SAR image processing. Aiming at the defect of low feature matching precision caused by the influence of speckle noise on a PolSAR image in the traditional registration method, the invention provides a method for improving the registration precision of a multi-band (L, C and X bands) PolSAR image by using comparative learning. The method comprises the following steps: firstly, performing spot removal processing on an input image by using a Lee filter; then, Pauli component decomposition is carried out on the filtered image to generate RGB representation; a Siamese ResNet-50 network is used to carry out comparative learning feature extraction; according to the method, initial registration is carried out on Thin-Plate Spline (TPS) based on extracted features, Kullback-Leibler (KL) divergence is calculated to serve as similarity measurement, and iterative optimization is carried out on TPS parameters; and finally, outputting a registration image. Compared with ORB, SIFT and other traditional methods, the method has the advantages that MAE and MME indexes are remarkably reduced, high-precision non-rigid registration is achieved, and the effect of subsequent multi-band PolSAR image fusion is improved.

The application discloses a time series polarimetric SAR cumulative change detection method based on a similarity matrix, relates to the technical field of remote sensing image change detection, and comprises the following steps: a similarity matrix of all time phase polarimetric SAR images in a time series is constructed; the similarity matrix is linearly transformed, and the maximum eigenvalue of the similarity matrix is calculated; a difference map of cumulative change of the polarimetric SAR images in the time series is calculated according to the maximum eigenvalue; and the cumulative change detection result of the polarimetric SAR images in the time series is obtained by segmenting the difference map. The time series polarimetric SAR cumulative change detection method based on the similarity matrix provided by the application does not repeatedly calculate a difference map, and compared with a traditional detection method of continuously detecting two changes in a time series, the detection precision and operation efficiency are improved.

This invention discloses a polarimetric SAR change detection method and system based on a Siamese attention complex convolutional neural network. It addresses the issues of poor quality difference maps generated by speckle noise in PolSAR images, and the problem that current real-number networks for SAR image change detection can disrupt the integrity of the complex structure of PolSAR data. This invention utilizes a Siamese network architecture to construct a more robust multi-scale feature difference map to suppress the influence of speckle noise. Furthermore, it combines a complex network to maintain the integrity of the PolSAR data structure and reduce polarimetric information loss. A corresponding complex attention module is also designed to enhance the identification of changed regions. Finally, the change detection results are obtained by decoding the multi-scale feature difference map. By using a Siamese attention complex convolutional neural network, the invention enhances the utilization of polarimetric information while suppressing the influence of speckle noise, thereby improving the accuracy of PolSAR change detection.

The invention particularly relates to a polarimetric SAR image classification method based on a complex valued graph U-Net. The method comprises the steps of extracting a complex valued scattering matrix of original polarimetric SAR data, six independent complex elements of a coherence matrix and a Pauli component, and converting the complex valued scattering matrix, the six independent complex elements and the Pauli component into a Lab color space; the edge weight is calculated according to fusion of the improved Wishart distance and the Lab color L1 distance; constructing a hierarchical superpixel structure HiAS and an incidence matrix; constructing a complex value graph convolutional network CV-GCN, extracting complex value discrimination features, and dynamically optimizing an adjacent matrix in combination with an attention mechanism; according to the incidence matrix of the HiAS and the CV-GCN, a complex value graph U-Net is constructed, and multi-scale complex value feature fusion is realized through jump connection; and refining multi-scale complex value features by using a complex value convolutional layer, inputting a full connection layer and a Softmax layer to estimate category probabilities, and further outputting a pixel-level classification result. According to the method, the influence of phase information loss and multi-scale boundary inconsistency is reduced, the characteristic expression of the polarimetric SAR data is more complete, the space consistency is stronger, and the classification precision is remarkably improved.

The application provides a polarimetric SAR relative similarity measurement and classification algorithm based on a graph convolution network, performs polarimetric superpixel segmentation (Pol-ASLIC) on a preprocessed polarimetric SAR image, then uses a proposed ||WA||2 distance which fuses a symmetric revised Wishart distance and an AIRM distance to calculate the similarity degree between polarimetric covariance matrices and construct an adjacency matrix, and finally inputs the polarimetric covariance matrix as a superpixel region feature into a GCN graph convolution network for semi-supervised classification. The measurement effect of the polarimetric SAR data in the graph convolution network is better than that of the currently popular symmetric revised Wishart distance and AIRM distance, and better polarimetric SAR classification effect and precision can be achieved.

The application discloses an optimal polarization filtering method for interference suppression in a dual-polarized SAR system, and belongs to the technical field of synthetic aperture radar signal processing. The application constructs the interference suppression problem as a generalized Rayleigh quotient maximization problem, solves the problem through generalized eigenvalue decomposition, and obtains an optimal signal-to-interference-and-noise ratio (SINR). Under the assumption that target components in polarization channels are not related, a closed-form solution of the optimal filtering weight is derived. When the interference polarization degree is 0, the SINR gain can reach about 19 dB. The application keeps stable gain within the range of 40 dB input SINR, is not sensitive to the interference bandwidth, and can effectively suppress full-bandwidth interference.

This invention relates to a method for feature extraction from polarimetric SAR images, comprising the following steps: acquiring raw polarimetric SAR image data to obtain the coherence matrix of the polarimetric SAR image; performing polarization pointing angle compensation and ellipticity angle compensation on the coherence matrix; calculating the proportion of power corresponding to the asymmetric scattering component to the total power; decomposing the coherence matrix after ellipticity angle compensation into a symmetric scattering part and an asymmetric scattering part, obtaining the coherence matrix and scattering power of each part; determining an adaptive volume scattering model; decomposing the obtained symmetric scattering part into surface scattering component, even-order scattering component, and adaptive volume scattering component; and decomposing the asymmetric scattering part into helical scattering component, directional dipole scattering component, and composite asymmetric scattering component. This invention can provide a more detailed description of ground features in different states, effectively extracting scattering information from different ground features, and playing a significant role in analyzing the scattering characteristics of different ground features.

The invention discloses a compact polarimetric SAR calibration method and device based on an active calibrator, and belongs to the technical field of signal processing. The method comprises the following steps: establishing a calibration model of a compact polarimetric SAR system based on a Freeman model; substituting the three active scaler characteristic matrixes with different polarization characteristics into the system model, and respectively analyzing and calculating the receiving channel imbalance, the receiving channel crosstalk, the transmitting channel crosstalk and the Faraday rotation angle; and a rough Faraday rotation angle is calculated by using ionosphere total electron quantity data provided by a global navigation satellite system, and defuzzification is carried out on the Faraday rotation angle with phase ambiguity. According to the method, only three active calibrators are needed, analytical calculation and GNSS auxiliary ambiguity resolution are combined, calibration of the CTLR mode compact polarimetric SAR system with high precision, low equipment quantity and simplified process is achieved, and the method has high noise resistance and calibrator error resistance.

This invention discloses a simplified polarimetric SAR (SAR) method and system for detecting marine oil spills. The method includes: S1, preprocessing a fully polarimetric SAR image and extracting simplified polarimetric SAR data; S2, extracting total scattering power and entropy based on the simplified polarimetric SAR data to construct a simplified polarimetric SAR oil spill detector; S3, performing random forest classification on the polarization feature map extracted by the simplified polarimetric SAR detector to obtain an oil-water separation map, and quantitatively evaluating the oil spill detection capability of the oil-water separation map. This invention features low scattering power... esa By combining polarization characteristic parameters from both backscattering energy and scattering mechanism, the extraction of oil spill information can be improved, thus making oil spill detection more accurate.

The invention discloses a polarimetric SAR image classification method based on polarization rotation domain ViT and complex phase gating activation in the technical field of image processing, which comprises the following steps: generating a rotation domain feature sequence through polarization coherence matrix rotation transformation, and performing complex convolution embedding, CPGLU activation function processing and azimuth angle position coding enhancement to obtain a rotation domain feature sequence; inputting a rotation domain ViT encoder to extract global spatial and temporal features, optimizing regional consistency in combination with superpixel constraint loss, and outputting classification confidence; model training adopts transfer learning and an AdamW optimizer, parameters are updated through a data enhancement strategy in each round of training, and the problems of insufficient utilization of complex information, insufficient mining of rotation sensitive features and speckle noise interference in a traditional polarimetric SAR classification technology are solved; the method can be widely applied to polarimetric SAR remote sensing interpretation scenes such as agricultural monitoring, urban mapping, disaster assessment and the like.

The application discloses a polarimetric SAR image classification method based on a deep semantic topology fusion network, and comprises the following steps: acquiring a polarimetric SAR image to be classified and a corresponding ground object real label image; preprocessing the polarimetric SAR image to be classified, and normalizing a polarimetric coherence matrix of each pixel point of the polarimetric SAR image after preprocessing; extracting a feature vector of each pixel point from the normalized polarimetric coherence matrix, and constructing a feature matrix of the polarimetric SAR image to be classified according to a feature vector set of all pixel points; constructing a training data set and a test data set according to the ground object real label image and the feature matrix of the polarimetric SAR image to be classified; constructing a deep semantic topology fusion network model; training the deep semantic topology fusion network model by using the training data set; and classifying the polarimetric SAR image to be classified in the test data set by using the trained deep semantic topology fusion network model. The application can effectively improve the classification accuracy of the polarimetric SAR image.