CNN and selective attention mechanism based SAR image target detection method

Abstract

The invention discloses a CNN and selective attention mechanism based SAR image target detection method. An SAR image is obtained; a training data set is expanded; a classification model composed of the CNN is constructed; the expanded training data set is used to train the classification model; significance test is carried out on a test image via a simple attention model (a spectral residual error method) of image visual significance to obtain a significant characteristic image; and morphological processing is carried out on the significant characteristic image, the processed characteristic image is marked with connected domains, target candidate areas corresponding to different mass centers are extracted by taking the mass centers of the connected domains as the centers, and the target candidate areas are translated within pixels in the surrounding to generate an target detection result. According to the invention, the CNN and the selective attention mechanism are applied to SAR image target detection in a combined way, the efficiency and accuracy of SAR image target detection are improved, the method can be applied to target classification and identification, and the problem that detection in the prior art is low in detection efficiency and accuracy is solved mainly.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a synthetic aperture radar SAR (Synthetic Aperture Rader) image target detection method based on a convolutional neural network (Convolutional Neural Network) and a selective attention mechanism, and can be used for subsequent synthetic aperture radar SAR images object classification and recognition. Background technique [0002] As an active sensor, the resolution of synthetic aperture radar has nothing to do with the observation distance, so it can complete the long-distance observation task under the condition of guaranteeing the resolution, which is one of the important remote sensing means. Compared with passive imaging equipment such as infrared and optics, its imaging process is not affected by environmental factors such as light, climate, and clouds, and it has the ability to observe and collect data on the ground all-weather and all-day. At present, SAR has bec...

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Problems solved by technology

[0005] The purpose of the present invention is to address the above problems, propose a SAR image target detection method based on CNN (convolutional neural network) and selective attention mechanism, overcome the detection performance of many current SAR target detection based on pixel level in complex scenes Poor, there are many false alarms, which improves the accuracy of target detection and positioning

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