SAR image semantic segmentation method for fast ridgelet deconvolution structure learning model

Abstract

The invention discloses an SAR image semantic segmentation method based on a fast ridgelet deconvolution structure learning model, which mainly solves the problems of inaccurate segmentation image and long model training time in the prior art. The method is performed through the following steps: 1. according to the sketch model of an SAR image, extracting a sketch graph; 2. according to the sketch graph, obtaining a region graph; using the region graph to divide the SAR image into a hybrid pixel subspace, a homogeneous pixel subspace and a structural pixel subspace; 3. Building a ridgelet filter set and a fast ridgelet deconvolution structure learning model and using the learning model to segment the hybrid pixel subspace; 5. using the visual semantics and sketch characteristics to sequentially segment the structural pixel subspace and the homogeneous pixel subspace; and 6. Combining the segmentation results of the three pixel subspaces to obtain a final segmentation result. The method of the invention improves the segmentation effect of an SAR image and can be used for detecting and recognizing subsequent SAR images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a semantic segmentation of SAR images, which can be used for target detection and recognition on subsequent synthetic aperture radar SAR images. Background technique [0002] Compared with other types of imaging technologies, synthetic aperture radar (SAR) has very important advantages. It is not affected by atmospheric conditions such as clouds, rain or heavy fog, and light intensity, and can obtain high-resolution remote sensing data all day and all weather. SAR technology has important guiding significance for military, agriculture, geography and many other fields. Image segmentation refers to the process of dividing an image into several disjoint regions based on features such as color, grayscale, and texture. Interpreting SAR images by computer is a huge challenge at present, and SAR image segmentation is a necessary step, which has a great impact on further ...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the boundary positioning of the aggregated area is not accurate enough; the segmentation result of the homogeneous area is poorly consistent, and the number of categories is not reasonable; and the independent target is not processed in the segmentation result of the structural area

Although this method achieves unsupervised learning of image features, the disadvantage of this method is that whe

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