SAR Image Segmentation Method Based on Decomposition Evolutionary Multi-objective Optimization and FCM

A multi-objective optimization and image segmentation technology, applied in image analysis, image data processing, character and pattern recognition, etc., can solve the problems of single evaluation index, poor image detail retention performance, and high computational complexity of image information

Active Publication Date: 2017-01-25
XIDIAN UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to: aim at the shortcomings of the above-mentioned single-objective optimization problem that the single evaluation index leads to less utilization of image information and some multi-objectiv

Method used

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  • SAR Image Segmentation Method Based on Decomposition Evolutionary Multi-objective Optimization and FCM
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  • SAR Image Segmentation Method Based on Decomposition Evolutionary Multi-objective Optimization and FCM

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Embodiment 1

[0076] The invention proposes a SAR image segmentation method based on decomposition evolutionary multi-objective optimization and FCM, belongs to the technical field of image processing, and further relates to a segmentation method in the technical field of texture image segmentation. The simulation of this example is carried out in the hardware environment of Pentium Dual_Core CPU E5200 with a main frequency of 2.3GHZ, a memory of 4GB and a software environment of MATLAB R2009a.

[0077] The invention is a SAR image segmentation method based on decomposition evolutionary multi-objective optimization and FCM. Aiming at the shortcomings of the prior art, such as single evaluation index, high computational complexity, and poor detail retention performance, the invention proposes a method based on decomposition Evolutionary Multi-Objective Optimization and FCM for SAR Image Segmentation. In the method, the fusion features are extracted as the data to be clustered to better prese...

Embodiment 2

[0118] The SAR image segmentation method based on decomposition evolution multi-objective optimization and FCM is the same as embodiment 1, in order to possess practicability, further detailed description of the present invention is as follows:

[0119] Wherein the further detailed description of image feature extraction in step 2 is as follows:

[0120] 2.1.1 The process of using the Gabor filter to extract the mid-low frequency texture feature vector of the image includes: the two-dimensional Gabor kernel function can be defined as:

[0121] g ( x , y ) = 1 2 πσ x σ y exp [ - 1 2 ( x 2 σ x 2 + ...

Embodiment 3

[0148] The multi-target remote sensing image segmentation method based on decomposition is the same as embodiment 1-2, and the segmentation effect of the present invention can be further illustrated by the following experiments:

[0149] The experimental simulation environment is: Pentium Dual_Core CPU E5200 with a main frequency of 2.3GHz, a hardware environment with a memory of 2GB, and a software environment of MATLAB R2009a.

[0150] In the experiment, the fuzzy C-means clustering algorithm (FCM) in the prior art, IMIS (immune multi-objective image segmentation algorithm integrating Gabor filtering and gray-level co-occurrence complementary features), NSGA-II (A fast and elitist multi-objective Genetic algorithm) algorithm is also respectively applied in the segmentation of original picture, compares with above-mentioned three kinds of segmentation methods with the present invention.

[0151] The setting of the algorithm of the present invention and the setting of comparison...

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Abstract

The invention discloses an SAR image segmentation method based on decomposition evolution multi-objective optimization and FCM. The method mainly solves the problem that in the prior art of image segmentation, image segmentation precision is not high, the evaluation index is single, and the segmentation effect is not ideal. The method comprises the steps that the Gabor feature and gray level symbiotic feature of each pixel of an image are extracted, and a superpixel is obtained through rough segmentation of a watershed, superpixel features are used as data to be clustered, a clustering center is used as individual species, the species are optimized through the decomposition evolution multi-objective method, the species obtained after evolution are used as the clustering center to initialize the FCM algorithm, a new clustering center is obtained and used as new species for participating in the next evolution of the decomposition evolution multi-objective algorithm. According to the SAR image segmentation method, the better clustering center is obtained through cross adoption of the decomposition evolution multi-objective algorithm and the FCM algorithm, the defect that the FCM initial value is sensitive and falls into a local optimal solution easily is overcome, and the better image segmentation result can be obtained.

Description

technical field [0001] The invention belongs to the field of intelligent image processing, and relates to remote sensing image segmentation technology, in particular to a SAR image segmentation method based on decomposition evolutionary multi-objective optimization and FCM, which is used for the segmentation of optical remote sensing images and synthetic aperture radar (SAR) images. To achieve the purpose of target recognition, it can be used in many fields such as remote sensing mapping, missile terminal guidance, marine resource monitoring, military reconnaissance, geological and mineral resource exploration, urban planning, and earthquake relief. Background technique [0002] With the rise of computer vision theory, image segmentation has become a hot spot in the field of image understanding. As a frontier subject, image segmentation is full of challenges and has attracted many scholars to study in this field. Image segmentation is the technology and process of dividing t...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06K9/46G06K9/62
Inventor 戚玉涛刘芳杨鸽李玲玲焦李成郝红侠李婉尚荣华马晶晶马文萍
Owner XIDIAN UNIV
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