Multi-category change dynamic threshold detection method based on multiple features of remote sensing images

Abstract

The invention discloses a multi-category change dynamic threshold detection method based on multiple features of remote sensing images. First, the spectral change characteristics and the spectral gradient difference characteristics of images in two periods are extracted and superimposed by band, a principal component analysis is made of the characteristics after superimposition, the first two principal components are selected as final change characteristics, and the change intensity and the change direction are calculated. Then, the change intensity threshold is calculated under a polar coordinate frame, the pixels are divided into an unchanged part and a changed part, a threshold is set for the change direction of the changed pixels, and the change categories are identified. On the basis, the initial change detection result is analyzed, pixel sets containing only a single change are generated for all the change categories in turn, an optical change intensity threshold for each change category is acquired, and a change detection result based on a dynamic threshold is generated. Finally, the detection result is post-processed using a multi-phase level set algorithm. The detection accuracy of multi-category change is improved.

Description

technical field [0001] The invention relates to a multi-category detection method for remote sensing images, in particular to a multi-category changing dynamic threshold value detection method utilizing multiple features of remote sensing images, and belongs to the technical field of multi-category detection for remote sensing images. Background technique [0002] Remote sensing image change detection technology has become an effective monitoring method for land surface change, which is widely used in land use, land cover, environmental monitoring, disaster assessment and urban development monitoring and other fields. [0003] Existing multi-category change detection methods can be divided into supervised and unsupervised categories. Supervised change detection uses classifiers to classify images in different periods, and then finds changes by comparing the classification results. This type of method requires reference data for classifier training, and collecting reference d...

AI Technical Summary

Problems solved by technology

However, the existing unsupervised detection methods for multi-category changes only use spectral change features, which cannot accurately describe all category changes due to factors such as illumination, atmospheric radiation, and seasons. Setting a single threshold for multi-category change detection also ignores the The characteristics of each category of variation cannot be accurately detected for all categories of variation

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