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

A remote sensing image and dynamic threshold technology, which is applied in image enhancement, image analysis, image data processing, etc., can solve the problems of ignoring characteristics, inability to accurately describe category changes, and inability to accurately detect category changes, so as to achieve the effect of improving detection accuracy

Active Publication Date: 2017-11-14
CHINA UNIV OF MINING & TECH
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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

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

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Embodiment Construction

[0032] Embodiments of the invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0033] Such as figure 1 As shown, a dynamic threshold detection method for multi-category changes using multi-features of remote sensing images includes the following steps:

[0034] Step 1: Perform preprocessing such as geometric correction, registration, and radiation correction on the two-period remote sensing images.

[0035] Step 2: Calculate the spectral change features and spectral gradient difference features of the images in the two periods respectively, and generate multiple features through band superposition. On this basis, the principal component analysis method is used to conduct principal component analysis on the superimposed features, select the approp...

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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...

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Application Information

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IPC IPC(8): G06T7/33
CPCG06T7/33G06T2207/10032G06T2207/30181
Inventor 郝明张华范洪冬史文中邓喀中
Owner CHINA UNIV OF MINING & TECH
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