A pcanet image change detection method based on saliency analysis

Abstract

The invention discloses a PCANet image change detection method based on significance analysis. It mainly solves the problems of too many training samples caused by the existing Automatic PCANet method, too long processing time and the influence of the scattering noise of the SAR image on the classification result. The implementation steps are: obtain the difference map of the two-temporal SAR image; analyze the significance of the difference map; use the threshold method to extract the salient area, and then use the threshold method to classify the positive samples, negative samples and uncertain pixels; use PCANet to extract positive samples. , negative samples, and features corresponding to uncertain pixels, use the features corresponding to positive and negative samples to train the support vector machine SVM module, and then input the features of uncertain pixels into SVM for final classification. Compared with the existing Automatic PCANet method, the invention has high detection precision and operation efficiency, good anti-noise performance, and can be used for SAR image change detection.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to image change detection, in particular to a PCANet image change detection method based on saliency analysis, which can be used for resource and environment monitoring, military target detection, and monitoring and evaluation of natural disasters. Background technique [0002] SAR image change detection refers to the process of using SAR images in different periods to obtain ground object change information. It is a data analysis method established according to the characteristics of SAR images to identify changes in the surface state. Microwave remote sensing represented by Synthetic Aperture Radar (SAR) is an active microwave imaging sensor, which can obtain large-area high-resolution remote sensing images, and shoot all-weather, strong penetrating energy to the ground surface, ground texture features and metal target detection It has strong capability and has multi-band an...

AI Technical Summary

Problems solved by technology

However, in many SAR change detection images, the unchanging pixels account for most of the image, and the unchanging pixels contain a large amount of coherent speckle noise. The above method selects training samples from all pixels, and the processing time is too long, and the scattering of the SAR image Noise may affect the classification results, and then affect the sample selection, eventually leading to wrong classification

[0004] The existing SAR image transformation detection method based on discriminant random field also includes PCANet-based deep learning network, but the above-mentioned change detection method is not good for SAR image change detection results in terms of overall accuracy and execution efficiency, and the classification results are affected. Scattering noise is more serious

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