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Spatial gravity model based fuzzy c-means remote sensing image automatic classification method

A remote sensing image and gravitational model technology, which is applied in image analysis, image data processing, character and pattern recognition, etc., can solve the problem of not considering the membership degree value of the central pixel, the edge of the region is too smooth, and the fuzzy factor calculation method has no physical meaning, etc. question

Active Publication Date: 2014-10-29
CHINA UNIV OF MINING & TECH
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Problems solved by technology

However, when the image noise is large, the segmentation accuracy of the FLICM algorithm is still low. The main reason is that in FLICM, the spatial distance between the central pixel and the neighboring pixels and the membership value of the neighboring pixels are simply considered. without considering the membership degree value of the central pixel, and the proposed fuzzy factor calculation method has no physical meaning
In the segmentation and classification results of FLICM, the edge of the region is over-smoothed and a lot of detail information is lost.

Method used

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  • Spatial gravity model based fuzzy c-means remote sensing image automatic classification method

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Embodiment 1

[0043] Embodiment 1. The QuickBird remote sensing image with a resolution of 0.61 meters and a size of 200×200 pixels including red, green and blue bands is used. This data is located in the urban area of ​​Xuzhou City, Jiangsu Province, China, and the acquisition time is 2005 August, if image 3 (a) and (b) are the original classification image and the reference data image respectively. The images are divided into 4 categories: structures, bare land, water and vegetation, and the fuzzy index is 2.

[0044] image 3 (c)-(e) represent the classification results of FCM, FLICM and FLNAICM, respectively, image 3 In (c), due to the similarity of the spectrum and the existence of image noise, FCM only uses the spectral characteristics of the image without considering the spatial context information, resulting in many "salt and pepper" phenomena in the classification results. From image 3 (d) and image 3 (e) It can be seen that the classification effect of FLICM and FLNAICM is...

Embodiment 2

[0048] In Example 2, a QuickBird remote sensing image with a size of 200×200 pixels and a resolution of 0.61 meters including red, green and blue bands was used. This data is located in the suburban area of ​​Xuzhou City, Jiangsu Province, China. The acquisition time for August 2005, as Figure 4 (a) and Figure 4 (b) are the original classification image and the reference data image respectively. The image is divided into 4 categories: road, bare land, water and vegetation, and the fuzzy index is 2.

[0049] Figure 4 (c)-(e) are the classification results of FCM, FLICM and FLNAICM respectively, Figure 4 In (c), due to the similarity of the spectrum and the existence of image noise, the FCM classification method only uses the spectral characteristics of the image without considering the spatial context information, and a large number of "salt and pepper" phenomena appear in the classification results. From Figure 4 (d) and Figure 4(e) It can be seen that the classific...

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Abstract

Disclosed is a spatial gravity model based fuzzy c-means remote sensing image automatic classification method which is suitable for automatic segmentation and classification of remote sensing images, medical images and other image. The spatial gravity model based fuzzy c-means remote sensing image automatic classification method comprises the steps of determining the number of pixels of a remote sensing digital image and performing clustering on the image through a standard FCM (Fuzzy C-Means) model to obtain an initial fuzzy membership matrix and clustering center; solving the spatial gravity and spatial constraint penalty factors between every pixel and other pixels in neighborhood windows of the pixel in turn to obtain a fuzzy factor as the formula finally; adding the fuzzy factor as the formula to the standard FCM model to obtain a new clustering objective function; solving the fuzzy matrix as the formula and the clustering center as the formula in a circulating mode until the clustering center does not continue to be changed any longer or the operation reaches the maximum number of iterations; performing class marking on every pixel point of the remote sensing image through the maximum membership matrix criterion according to the finally solved fuzzy membership matrix as the formula to determine the class of every pixel to form a remote sensing image classification thematic map so as to implement the automatic classification of remote sensing digital images. The spatial gravity model based fuzzy c-means remote sensing image automatic classification method is simple, high in automation degree, small affected by image noise and high in image segmentation classification accuracy.

Description

[0001] technology neighborhood [0002] The invention relates to an automatic classification method for remote sensing images, in particular to a fuzzy C-mean automatic classification method for remote sensing images based on a spatial gravity model used in the automatic segmentation and classification of remote sensing images, medical images and other images. Background technique [0003] Remote sensing data classification is an important technology for extracting thematic category data from remote sensing data, which provides a rich source of data for information extraction in various industries. The current classification methods are mainly divided into supervised classification and unsupervised classification methods. Compared with supervised classification methods, unsupervised classification methods can extract information from remote sensing data without prior knowledge. It plays a very important role in classification. [0004] The existing unsupervised classification...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/46G06T7/00
Inventor 张华郑南山
Owner CHINA UNIV OF MINING & TECH
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