Multi-spectral remote sensing image change detection method based on spectral reflectivity neighborhood difference chart and neighborhood probability fusion

Abstract

The invention discloses a multi-spectral remote sensing image change detection method based on a spectral reflectivity neighborhood difference chart and neighborhood probability fusion. The method mainly comprises the steps that 1, a registered two-time-phase multi-spectral image set at the same area is input and is subjected to Wiener filtering noise reduction and normalization processing; 2, the processed image set is converted into a relative ground object spectral reflectivity image set; 3, the spectral reflectivity change module value is calculated, and a spectral reflectivity change module value chart is obtained; 4, the spectral reflectivity angle value is calculated, and a spectral reflectivity angle value chart is obtained; 5, a neighborhood difference value of the spectral reflectivity is calculated and the neighborhood difference chart is obtained; 6, clustering is carried out on the spectral reflectivity change module value chart, the spectral reflectivity angle chart and the neighborhood difference chart, and a binary image is obtained; 7, the obtained binary image is fused based on the neighborhood probability, and a change detection result chart is obtained. Manual participation is not needed, the detection precision is high, and the method can be used for the fields such as urban expansion monitoring, forest and vegetation change monitoring and the like.

Description

Technical field [0001] The invention belongs to the technical field of optical remote sensing image processing, and is specifically a multi-spectral remote sensing image change detection method based on spectral reflectance neighborhood difference map and neighborhood probability fusion, which can be used for change monitoring of land use, vegetation coverage and the like. Background technique [0002] The change detection of remote sensing images is the process of identifying changes in the state of objects or changes in phenomena by analyzing and extracting differences in electromagnetic spectrum characteristics or spatial structure characteristics between remote sensing images of different time phases in the same area. It has been widely used in many fields of national economy and national defense construction, such as agricultural survey, forest and vegetation change monitoring. Since the changes of different feature types may be reflected in different spectral ranges, and mu...

AI Technical Summary

Problems solved by technology

However, when this method is applied to multispectral images, due to the lack of band information, the difference between the corresponding ground and object spectral vectors between different time phase images is not large enough. Therefore, the change detection results obtained by classifying the difference images obtained by the SAM method In , although the pseudo-change information is reduced, a lot of missed detections are introduced at the same time

[0004] The above-mentioned change detection methods all use the spectral information of the image, and do not consider the neighborhood information of the pixel, making the change detection results unsatisfactory.

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