SAR Image Classification Method Based on Hierarchical Sparse Filter Convolutional Neural Network

Abstract

The invention discloses a SAR image classification method based on layered sparse filter convolutional neural network. The steps are: 1. Divide the SAR database sample set into training data set and test sample set; 2. Learn the first layer sparse dictionary from the training data set; 3. Use the first layer sparse dictionary to extract the first layer sparse feature map and perform Non-linear transformation; 3. Learning the second-layer sparse dictionary from the first-layer nonlinear transformation feature map; 4. Using the second-layer sparse dictionary to extract the second-layer sparse feature map and performing nonlinear transformation; 5. Cascade first, The second-layer nonlinear transformation feature trains the SVM classifier; 6. Use the first and second-layer sparse dictionaries to extract the sparse features of the test set, and use the SVM classifier to classify. The invention solves the problems of complex design, poor universality and noise resistance, and low classification precision in the prior art, and 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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