SAR image change detection method based on FPGA

Abstract

The invention discloses an SAR (Synthetic Aperture Radar) image change detection method based on an FPGA (Field Programmable Gate Array), which mainly solves the problems that the prior art is easilyinfluenced by noise and the detection speed is low. The method comprises the following implementation steps of: 1) reading in two SAR images at different periods; 2) carrying out parallel Lee filtering on the two SAR images; 3) generating two difference graphs with different characteristics according to the filtered image, namely a direct difference graph and a logarithm ratio difference graph; 4)transforming the difference image into a frequency domain by adopting parallel wavelet transformation, and performing fusion and parallel wavelet inverse transformation in the frequency domain to obtain a fused difference image; 5) using a parallel difference K-means method to perform two clustering on a fusion difference map to obtain a change detection result. The invention adopts a method of FPGA parallel acceleration and difference graph fusion, has the advantages of fast detection speed and good anti-noise performance, and can be used for fast and accurate SAR image change detection in complex environments.

Description

technical field [0001] The invention belongs to the field of remote sensing image processing, and further relates to a SAR image parallel change detection method, which can be used for rapid remote sensing image change detection in complex scenes. Background technique [0002] Remote sensing is a science and technology that can sense the electromagnetic wave information reflected, radiated or scattered by ground objects at a long distance, and analyze and process the obtained information. Remote sensing image change detection is the process of studying remote sensing images in different periods of the same area and analyzing the change information of ground objects. At present, the change detection technology of remote sensing images has developed rapidly, and has become an important part and research hotspot of remote sensing application research. Remote sensing image change detection technology has been widely used in various fields, such as natural disaster analysis, for...

AI Technical Summary

Problems solved by technology

This method manually selects the threshold to analyze the difference map, resulting in a large error in the detection result.

In 2012, Gong et al. proposed a FLICM clustering method to increase the size of the fuzzy factor to analyze the difference map, thereby improving the accuracy of membership degree calculation and reducing the impact of noise, but the traditional clustering method has cyclic iterations. The problem of taking too long

Amin et al. proposed a change detection method using deep learning and superpixel segmentation. This method uses a convolutional neural network to classify the pixels of the difference map to obtain change detection results. However, due to the limitation of the size of the data set, deep learning The method has the problems of unsatisfactory network fitting data distribution and huge consumption of storage and computing resources

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