7 results about "Synthetic aperture radar" patented technology

The invention discloses a polarized SAR image classification method based on a K mean value and a depth SVM. The polarized SAR image classification method mainly solves the problems of low classification accuracy and classification efficiency of an existing polarized synthetic aperture radar (SAR) classification method. The polarized SAR image classification method comprises the steps of (1) inputting images; (2) performing filtering; (3) extracting features; (4) establishing a misclassification set; (5) establishing a nearest neighbor sample set; (6) establishing a final training set; (7) establishing a depth support vector machine classifier; (8) performing classification; (9) calculating accuracy. By means of the polarized SAR image classification method, polarized SAR images can be classified accurately, the classification time of the polarized SAR images can be shortened effectively, and target recognition and tracking of the polarized SAR images are achieved.

The invention discloses an SAR (Synthetic Aperture Radar) image object detection method based on a Primal Sketch algorithm, mainly solving the problem that the traditional object detection method cannot realize the detection on different types of artificial objects. The implementing process of the SAR image object detection method based on the Primal Sketch algorithm comprises the following steps of: (1) obtaining a line segment aggregation expressing image structure information by applying the Primal Sketch algorithm to original SAR images; (2) defining and calculating the regularity degree and the regularity ratio of all line segments in the line segment aggregation; (3) determining a seed line segment aggregation for region growing; (4) executing the region growing by taking the seed line segments as the reference to obtain a candidate object region aggregation including artificial objects and natural objects; and (5) screening according to the characteristics of the line segments in the candidate object region aggregation to obtain final artificial objects, namely bridges, ports and buildings. Compared with the prior art, the detection method disclosed by the invention has the advantages of strong applicability, capability of realizing the detection on different types of artificial objects, exact detection result and low false alarm rate; and the invention is suitable for the SAR image object detection under the condition of multiple object types and different sizes of objects.

A computer system for processing synthetic aperture radar (SAR) images includes a database for storing SAR images to be processed, and a processor for processing a SAR image from the database. The processing includes determining noise in a SAR image to be processed, selecting a noise threshold for the SAR image based on the determined noise, and mathematically adjusting an anisotropic diffusion algorithm based on the selected noise threshold. The adjusted anisotropic diffusion algorithm is applied to the SAR image.

The invention discloses a rapid multi-scale super-pixel segmentation method for a synthetic aperture radar image. The method comprises the following steps: measuring the final similarity of two pixelsin combination with backscattering gray similarity, edge information similarity and homogeneity similarity; performing graph structure representation on the SAR image; constructing a minimum spanningtree for the SAR image; sAR image fast multi-scale super-pixel segmentation is achieved based on the minimum spanning tree, a super-pixel segmentation boundary is obtained based on a segmentation result, and an SAR image segmentation result is generated. According to the invention, any number of superpixels with a tree structure can be generated immediately, so that superpixel results of different levels can be segmented. According to the multi-scale super-pixel segmentation method, image boundaries can be well kept, the good image local detail keeping capacity is achieved, the segmentation result of any scale can be generated for the SAR image in real time, target information of different sizes can be easily obtained, and the execution efficiency is remarkably improved.

The invention relates to a photoelectric collaborative processing system for panoramic imaging of a high-resolution synthetic aperture radar signal. The system comprises a light source module, a plurality of parallel sub data processing unit, a summation module, and a collection module. The light source module is used for generating a plurality of beams of same parallel laser beams as inputs of the sub data processing units. Each sub data processing unit includes a data modulation module, an imaging module, an interpolation operation module and a phase shift operation module to realize modulation of the SAR sub data block, generation of a low-resolution image, sinc interpolation operation and outputting after frequency spectrum shifting. The summation module is used for carrying out summation operation on beams outputted by all sub data processing units to obtain a high-resolution panoramic SAR image. The collection module is used for collecting and displaying the SAR image. Compared with the prior art, the system has advantages of parallel processing, real-time imaging, high flexibility, and the like.

The invention discloses a robust and efficient synthetic aperture radar multi-feature enhanced imaging method. The method comprises the following steps of: establishing an echo signal and a target multivariate feature enhancement framework; establishing a target multivariate feature enhancement solution equation, and solving target image data; applying entropy norm modeling synthetic aperture radar image focusing feature regularization prior, establishing a cost function, and solving an adjacent operator of the cost function to obtain a phase error and a focusing feature solution; applying l1norm modeling synthetic aperture radar image sparse feature regularization prior, and solving a sparse feature solution by using a soft threshold function and the phase error obtained in the step 3);and performing iteration on the target image data by using a Gaussian-Seidel method to obtain a synthetic aperture radar image with joint sparsity and focusing feature enhancement, and the like. According to the method, the sparse and focusing effects of the synthetic aperture radar image are recovered obviously in an experiment, and the focusing characteristics and sparse characteristics of the image can be enhanced quickly and effectively.

The invention relates to the technical field of radar detection, in particular to a satellite-borne down-looking ice-penetrating synthetic aperture radar transmission path calculation method, which comprises the following steps: acquiring earth model parameters, system parameters of a satellite-borne down-looking ice-penetrating synthetic aperture radar and echo simulation parameters; the echo simulation parameters comprise an azimuth time sequence, and an antenna position sequence and an in-ice target position in a rotating geocentric coordinate system; in the south pole, the north pole or other to-be-detected areas covered by ice, taking the part above the earth surface as an air medium and the part below the earth surface as an ice medium, for each azimuth moment, solving a corresponding geocentric angle of a corresponding transmission path in the ice medium, and further respectively determining the lengths of the transmission path in the air medium and the ice medium; and the transmission path is connected with the corresponding antenna position and the in-ice target position and is located in the beam range of the transmitting antenna. According to the method, the transmission path can be determined more accurately, and technical support is provided for radar imaging, echo simulation and the like.