Sparse dynamic ensemble selection-based SAR (synthetic aperture radar) image terrain classification method

Abstract

The invention discloses a sparse dynamic ensemble selection-based SAR (synthetic aperture radar) image terrain classification method, which mainly solves the problem that the speed of the conventional dynamic ensemble selection algorithm and the conventional dynamic classifier selection algorithm for terrain classification in SAR images is low. The implementation process of the sparse dynamic ensemble selection-based SAR image terrain classification method is as follows: (1) a wavelet energy feature is extracted from an SAR image to be classified; (2) training data is acquired from the SAR image to be classified; (3) the SAR image to be classified is regionalized to obtain data to be classified; (4) training samples are utilized to learn ensemble systems; (5) a dictionary is learnt for each class of training data, and a synthetic dictionary is obtained; (6) dynamic ensemble selection is carried out on each atom in the synthetic dictionary; (7) samples to be classified are sparsely coded; (8) the samples to be classified are marked according to a sparse coefficient and classifier ensembles corresponding to the atoms; (9) the marks of the samples to be classified are mapped onto pixels in the SAR image, so that a terrain classification result is obtained. The sparse dynamic ensemble selection-based SAR image terrain classification method has the advantages of high speed and good classification effect, and can be used for SAR image target identification.

Description

technical field [0001] The invention belongs to the technical field of image processing, and can be used for classification of SAR image ground objects and as a basis for further SAR image understanding and interpretation. Background technique [0002] Synthetic aperture radar (SAR) imaging technology actively emits and receives electromagnetic waves, and forms images according to the reflection and scattering characteristics of objects. It makes full use of the principle of synthetic aperture to improve azimuth resolution, and has unique advantages in the field of remote sensing. SAR has all-weather and all-weather detection and reconnaissance capabilities, and the interpretation of SAR images has received more and more attention from national defense and civilian use. As a very important step in SAR image interpretation, the classification of SAR image features becomes more and more important. [0003] The existing single-polarization SAR image object classification metho...

AI Technical Summary

Problems solved by technology

[0004] Among them, the SAR image classification method based on a single classifier is to input the training data into a single classifier. After learning, the classifier has the ability to classify and recognize. This type of method has a faster classification speed, but the adjustment of classifier parameters is difficult and the results are not good. Stable; the SAR image object classification method based on classifier integration is to use a certain integration strategy to integrate multiple classifiers together, and multiple classifiers jointly make decisions on SAR image data. At present, the better SAR image based on integration The object classification strategy includes dynamic classifier selection method and dynamic integration selection classification method, although the classification effect is good, but the speed is slow

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