SAR (Synthetic Aperture Radar) image classification method based on SPM (Spatial Pyramid Matching) and depth increment SVM (Support Vector Machine)

Abstract

The invention discloses an SAR (Synthetic Aperture Radar) image classification method based on an SPM (Spatial Pyramid Matching) and depth increment SVM (Support Vector Machine). The method comprises the following steps of (1) inputting an SAR image; (2) extracting SAR image dense SIFT features; (3) building a dictionary; (4) performing sparse coding; (5) performing spatial pyramid pooling; (6) calculating normalization characteristics; (7) building an incremental training set; (8) initializing a depth increment SVM; (9) calculating the initial classification accuracy of a test sample; (10) updating the depth increment SVM; (11) calculating the classification accuracy of the test sample. The space information of the image can be effectively extracted; the advantages of depth study and increment study are combined; the advantages of improving the SAR image classification precision and reducing the training time are realized.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a synthetic aperture of a spatial pyramid matching (Spatial Pyramid Matching, SPM) model and a depth incremental Support Vector Machine (Support Vector Machine, SVM) in the technical field of synthetic aperture radar image target classification Radar (Synthetic Aperture Radar, SAR) image classification method. The invention can be used for target classification and recognition of SAR images. Background technique [0002] Synthetic Aperture Radar (SAR) has the characteristics of all-weather, long-distance, extremely strong penetrating power and high resolution, and is widely used in the national economy and military fields. In the face of the ever-increasing SAR image data collection capabilities, how to accurately and quickly understand and identify these images has attracted more and more attention and attention. [0003] Xidian University disclosed a method bas...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the feature extraction part is not targeted, so that the learned features cannot represent the original image well, resulting in a low recognition rate

However, the disadvantage of this method is that when the number of training samples changes, all training samples must be recombined, and the computational complexity increases significantly, resulting in long training time, low recognition efficiency, and poor real-time performance.

Images