Multispectral image change detection method based on semi-supervised dimensionality reduction and saliency map

Abstract

The invention discloses a multispectral image change detection method based on semi-supervised dimensionality reduction and a saliency map, for solving the defect that the spectral characteristic relationship of a multispectral image cannot be accurately reflected in the prior art. The multispectral image change detection method comprises the following realization steps of: (1) inputting two time-phase multispectral images; (2) preprocessing; (3) generating a corresponding waveband difference map; (4) carrying out semi-supervised dimensionality reduction; (5) generating the saliency map; (6) carrying out K mean value clustering; (7) generating a change detection result map; (8) outputting the change detection result map. The method is capable of either keeping the edge information of a change area well or giving consideration to missing detection information and false-alarm information in a change detection result well, good in real-time performance, and high in change detection result accuracy. The method disclosed by the invention can be applied to the fields of urban area extended monitoring, forest and vegetation disaster monitoring, crop growth state dynamic monitoring, military reconnaissance and the like.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a multispectral image change detection method based on semi-supervised dimension reduction and saliency map in the technical field of remote sensing image processing. The invention can be applied to the exploration of land utilization, vegetation coverage, water resources and mineral resources, etc., and can quickly detect the change information of two-temporal multispectral remote sensing images. Background technique [0002] By analyzing multiple remote sensing images taken in different periods in the same area, change detection detects the information of changes in the ground features in the area over time. It has been applied in many fields of national economy and national defense construction. Hot Issues. Changes of different surface object types may be reflected in different spectral ranges, while multispectral remote sensing image data has multiple receivin...

AI Technical Summary

Problems solved by technology

Although this method can separate the changing regions with non-Gaussian distribution from multi-temporal images, it still has the disadvantage that this method needs to manually select the component images required after dimensionality reduction, and cannot be automated.

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