SAR image classification method based on hierarchical sparse filtering convolutional neural network

Abstract

The invention discloses an SAR image classification method based on a hierarchical sparse filtering convolutional neural network. The SAR image classification method comprises the steps of 1. dividing an SAR database sample set to a training data set and a testing sample set; 2. studying a first-layer sparse dictionary from the training data set; 3. extracting a first-layer sparse characteristic chart by means of the first-layer sparse dictionary and performing nonlinear transformation, studying a second-layer sparse dictionary from the first-layer sparse characteristic chart; 4. extracting a second-layer sparse characteristic chart by means of the second-layer sparse dictionary and performing nonlinear transformation; 5. cascading the first-layer nonlinear transformation characteristic and the second-layer nonlinear transformation characteristic and training an SVM classifier; and 6. extracting the sparse characteristic of a testing set by means of the first-layer sparse dictionary and the second-layer sparse dictionary, and performing classification by means of the SVM classifier. The SAR image classification method settles the problems of high complexity, low universality, low noise resistance and low classification precision in prior art. Furthermore the SAR image classification method can be used for target recognition.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a SAR image classification method, which can be used for target recognition. Background technique [0002] Synthetic Aperture Radar (SAR) is a microwave imaging radar with good resolution. It can not only observe terrain and landform in detail and accurately, obtain information on the earth's surface, but also collect information below the earth's surface through the surface and natural vegetation. SAR is an effective means of earth observation from space. It can generate high-resolution maps of ground target areas or regions, and provide radar images similar to optical photos. It has been widely used in military and other earth observation fields. [0003] The concept of synthetic aperture radar was first proposed in June 1951 by Carl Wiley of Goodyear Aerospace Corporation of the United States. SAR is an active microwave imaging sensor. It uses pulse compression...

AI Technical Summary

Problems solved by technology

These factors limit the application of traditional image classification techniques such as template matching, model-based and kernel-based classification techniques in SAR image classification

At present, there are three main problems in SAR image recognition technology that need to be solved urgently: (1) Due to the large amount of coherent speckle noise in SAR images, it is difficult to overcome the influence of noise by using common feature extraction methods, and the classification accuracy is not high; (2) Due to the The scene of similar ground objects in the image is complex, and the traditional feature extraction method is time-consuming and laborious in design, and has great limitations and is not self-adaptive.

(3) Due to the cumbersome and laborious labeling process of SAR image features, it is necessary to classify when there are fewer labeled samples. In this case, the traditional classification method has low classification accuracy and unstable classification results.

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