Parallel classification method of polarized sar objects based on opencl

Abstract

A parallel polarization SAR object classification method based on OpenCL, the implementation steps are: (1) input the polarization SAR image to be classified and the corresponding polarization SAR real object classification; (2) remove coherent speckle noise; (3) Feature extraction; (4) Generate training sample set and test sample set; (5) Preprocessing; (6) Training support vector machine model; (7) Configuring open computing language OpenCL device side; (8) Parallel prediction test (9) color the test sample set ground object class label; (10) output the colored classification result map. The invention utilizes the multi-thread parallel processing of the OpenCL device to process the data to be predicted, and changes the original serial processing mode of the support vector machine prediction stage into a parallel processing method, so as to reduce the time spent in the prediction stage without affecting the classification accuracy of the test sample set .

Description

technical field [0001] The present invention belongs to the technical field of image processing, and further relates to a polarimetric synthetic aperture radar SAR (Polarimetric Synthetic Aperture Radar) object classification based on an Open Computing Language OpenCL (Open Computing Language) hardware device parallel processing prediction data in the technical field of image classification method. The invention can be used to extract the features of the polarimetric SAR image and use the features to classify the polarimetric SAR ground objects. Background technique [0002] The ground object classification method of polarimetric SAR image based on support vector machine is a very important classification method. However, due to the long running time of the support vector machine prediction stage, it is difficult to meet the requirements of the actual scene that needs to quickly obtain the classification result within seconds and perform subsequent image processing tasks. ...

AI Technical Summary

Problems solved by technology

This method can improve the accuracy and efficiency of polarimetric SAR image object classification to a certain extent. However, this method still has shortcomings: due to the complexity of the support vector machine model itself, the computational complexity of the prediction stage is high. As a result, the operation efficiency of the support vector machine prediction stage in this method is low and the operation time is long, which is difficult to meet the requirements of quickly obtaining classification results in seconds and performing subsequent image processing tasks in actual scenarios

However, since this method is based on the parallelized support vector machine implemented on the Spark platform, it is only suitable for distributed systems, and its portability is not high, which limits its application range

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