SAR image classification based on multi-feature and non-negative automatic encoder

Abstract

The invention relates to a SAR image classification method based on a multi-feature and a non-negative automatic encoder, belonging to the technical field of image processing. The method comprises steps: extracting Spatial Features of SAR Image Blocks Based on Gray Level Gradient Co-occurrence Matrix; feature Extraction of SAR Image Block Transform Domain Based on Two-Dimensional Gabor Transform;combining the spatial domain features and the transform domain features of an image block; the training sample set and test sample set of SAR image block being selected. training multi-layer non-negative automatic encoder and softmax classifier with training sample set; classification being based on the trained non-negative automatic encoder network, so that a classification result diagram is obtain. The invention combines the spatial information of the SAR image and the information of the transform domain, obtains the multi-dimensional features of the SAR image, optimizes the features by using a non-negative automatic encoder, improves the distinctiveness of the features, further effectively improves the accuracy of classification, and can be used for the classification of the ground objects of the high-resolution SAR image and the target recognition, and the like.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a SAR image classification method based on multi-features and non-negative automatic encoders in the field of object classification, which can be used for SAR image object classification and target recognition. Background technique [0002] Synthetic aperture radar (SAR) is an active imaging sensor with all-weather and all-time data acquisition capabilities, which has obvious advantages over traditional optical remote sensing technology. With the continuous development of remote sensing technology, the resolution of images acquired by SAR systems is getting higher and higher. High-resolution SAR images can reflect more detailed ground object information, which meets the needs of many practical applications. SAR image classification is an important content of SAR image interpretation, and has a wide range of applications in military reconnaissance, resource detection, geo...

AI Technical Summary

Problems solved by technology

The above method does not consider the influence of coherent speckle noise in SAR images and does not fully exploit the different features of SAR images, and does not effectively use the deep network to improve the distinction of features, thus affecting the classification effect

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