SAR Image Segmentation Method Based on Ridgelet Filter and Convolution Structure Learning Model

Abstract

The invention discloses an SAR image segmentation method based on ridgelet filters and a convolution structure model. The SAR image segmentation method based on ridgelet filters and a convolution structure model mainly solves the problem that in the prior art, segmentation of SAR images is not accurate. The SAR image segmentation method based on ridgelet filters and a convolution structure model includes the following steps: 1) sketching an SAR image, and obtaining a sketch image; 2) according to an area image of the SAR image, dividing the pixel subspace of the SAR image; 3) constructing a ridgelet filter set; 4) constructing a convolution structure learning model; 5) utilizing the SAR image segmentation method based on the ridgelet filters and the convolution structure model to segment the pixel subspace of a hybrid aggregation structure natural object; 6) based on the gathering feature of sketch lines, performing segmentation of an independent object; 7) based on visual sense semantic rules, performing segmentation of line object; 8) based on polynomial logic regression prior model, segmenting the pixel subspace of a formal area; and 9) combining the segmentation results. The SAR image segmentation method based on ridgelet filters and a convolution structure model can acquire good segmentation effect of SAR images, and can be used for semantic segmentation of the SAR images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a synthetic aperture radar (SAR) image segmentation method based on a ridgelet filter and a convolutional structure learning model in the technical field of target recognition. The invention can accurately segment the pixel subspace of mixed aggregation structures with different characteristics in the synthetic aperture radar SAR image, and can be used for target detection and recognition of the subsequent synthetic aperture radar SAR image. Background technique [0002] Synthetic aperture radar (SAR) is an important progress in the field of remote sensing technology, which is used to obtain high-resolution images of the earth's surface. Compared with other types of imaging technologies, SAR has the advantages of all-time, all-weather, multi-band, multi-polarization, variable side angle of view and high resolution. It can also collect subsurface information throug...

AI Technical Summary

Problems solved by technology

However, the disadvantage of this method is that it does not introduce the high-level semantic knowledge of the SAR image, and only segments the SAR image at the pixel level, resulting in inaccurate SAR image segmentation results. At the same time, the support vector machine is a supervised method, requires a class

Although this method achieves the purpose of autonomous learning of image features, the disadvantage of this method is that for the convenience of processing, this method normalizes the input image, which destroys the original structure of the image. information

The disadvantage of this method is that the input of the depth autoencoder used to automatically extract image features is a one-dimensional vector, which will destroy the spatial structure features of the image. Therefore, the essential features of the image cannot be extracted, which reduces the efficiency of SAR image segmentation. precision

The disadvantages of this method are that the positioning of the aggregated area is not precise enough, the determination of the number of categories in the homogeneous area is not reasonable enough, the regional consistency of the segmentation result is poor, and the independent target is not processed in the segmentation result of the structural area.

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