High-resolution SAR image classification method based on non-down-sampling contourlet full-convolution network

Abstract

A high-resolution SAR image classification method based on a non-down-sampling contourlet full-convolution network is provided, which comprises: inputting a high-resolution SAR image to be classified; performing multi-layer non-down-sampling contourlet transform on each pixel in the image; obtaining the low-frequency coefficient and the high-frequency coefficient of each pixel; selecting and fusing the low-frequency coefficients and high-frequency coefficients to form a pixel-based characteristic matrix F; normalizing the element values in the characteristic matrix F to obtain a normalized characteristic matrix F1; dicing the normalized characteristic matrix F1 to obtain a characteristic block matrix F2 used as sample data; constructing a training data set characteristic matrix W1 and a testing data set characteristic matrix W2; constructing a classification model based on a full convolution neural network; training the classification model; utilizing the well-trained model to classify the testing data set T to obtain the category of each pixel in the testing data set T; comparing the obtained category of each pixel with a class diagram; and calculating the classification accuracy. With the method, the classification accuracy and speed are increased.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a high-resolution SAR image classification method based on a non-subsampling contourlet full convolution network, which can be applied to high-resolution SAR images and effectively improve target recognition accuracy. Background technique [0002] Synthetic Aperture Radar (SAR) is a remote sensing sensor that has been widely researched and applied in recent years. Compared with other sensors such as optics and infrared, SAR imaging is not limited by conditions such as weather and light, and can be used for interested The target conducts round-the-clock, round-the-clock reconnaissance. Moreover, SAR also has a certain penetration capability, and can detect targets under unfavorable conditions such as cloud interference, tree cover, or shallow buried targets. In addition, due to the special imaging mechanism of SAR, high-resolution SAR images contain different content f...

AI Technical Summary

Problems solved by technology

However, none of the above feature extraction methods take into account the multi-scale, multi-direction, and multi-resolution characteristics of high-resolution SAR images. Therefore, it is difficult to obtain high classification accuracy for high-resolution SAR images with complex backgrounds.

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