Research on SAR image change detection algorithm based on hybrid genetic FCM and image registration

Li, Yufeng; He, Wei

doi:10.1007/s11042-017-4687-9

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…After that, the difference image was extracted and its dimension was reduced using Principal Component Analysis (PCA). The feature vector space was classified using hybrid genetic fuzzy Cmeans (FCM) algorithm before being compared with the reference image [28]. The registration of cadastral images was investigated by first using ℎ𝑎𝑎𝑚𝑚𝑚𝑚𝑎𝑎𝑚𝑚 to perform feature detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigation of Image Stitching Refinement With Enhanced Correlation Coefficient

Hwooi

Sabri

2020

MJCS

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Image stitching combines multiple overlapping scenes into a panorama. It can be applied in video stitching, super-resolution, and medical imaging. Post-processing routines such as exposure compensation, seam line adjustment, and blending are often performed to increase its visual appeal. Our aim is to increase the registration accuracy between adjacent image pairs in a stitching algorithm. This is done by adding an area-based registration step, namely Enhanced Correlation Coefficient (ECC), which refines the original feature-based image registration. The performance of registrations in the stitching algorithm is evaluated using root-mean-squared error of control points, structural similarity index, and universal image quality index. The Boat and Graffiti datasets from the Oxford Robotics Database are used for experiments. It is found that ECC largely improves registrations in stitching except for a slight increase in root-mean-squared error of control points in the Boat dataset. In addition, ECC’s enhancement makes the registration outperform the ground truth in one image of both datasets.

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Section: Literature Reviewmentioning

confidence: 99%

Investigation of Image Stitching Refinement With Enhanced Correlation Coefficient

Hwooi

Sabri

2020

MJCS

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“…The result obtained are encouraging for discovering optimal transformation outcome. Yufeng, et al [19] illustrated a hybrid satellite IR model combining both genetic algorithm (GA) FCM (Fuzzy C Means). This technique applied in image registration, which taken two similar region based images but various instance utilizing SIFT and Harris operator.…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid Invariant Local Features for Multiple Satellite Image Matching and Registration

Anil*,

D.N

2019

IJITEE

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Automatic image registration (IR) is very challenging and very important in the field of hyperspectral remote sensing data. Efficient autonomous IR method is needed with high precision, fast, and robust. A key operation of IR is to align the multiple images in single co-ordinate system for extracting and identifying variation between images considered. In this paper, presented a feature descriptor by combining features from both Feature from Accelerated Segment Test (FAST) and Binary Robust Invariant Scalable Key point (BRISK). The proposed hybrid invariant local features (HILF) descriptor extract useful and similar feature sets from reference and source images. The feature matching method allows finding precise relationship or matching among two feature sets. An experimental analysis described the outcome BRISK, FASK and proposed HILF in terms of inliers ratio and repeatability evaluation metrics.

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“…At present, the traditional "many to many" cluster point information collection is the main way of vehicle image information in China [1]. Since the beginning of the 21st century, the rapid development of a variety of information technologies in China has also led to the transformation of obtaining source data from vehicle images.…”

Section: Introductionmentioning

confidence: 99%

Sparse representation optimization of Gaussian mixed feature of image based on convolution neural network

2022

Soft Comput

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With the rapid development of intelligent algorithm and image processing technology, the limitations of traditional image processing methods are more and more obvious. Based on this, this paper studies a new pattern of sparse representation optimization of image Gaussian mixture feature based on convolution neural network, and designs a sparse representation system model of vehicle detection image based on convolution neural network. The vehicle image data is collected from many aspects, and the convolution neural network is used for comprehensive analysis and evaluation. The model can extract the feature information of the vehicle detection image better by making the scheme of the real-time vehicle detection image and according to the image features and convolution neural network algorithm. The results show that the Gaussian mixture feature sparse representation optimization model based on convolution neural network has the advantages of high feasibility, high data accuracy and high response speed, which can enhance the processing efficiency of vehicle detection image and improve the utilization of local environmental information in the image.

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Research on SAR image change detection algorithm based on hybrid genetic FCM and image registration

Cited by 8 publications

References 18 publications

Investigation of Image Stitching Refinement With Enhanced Correlation Coefficient

Investigation of Image Stitching Refinement With Enhanced Correlation Coefficient

Hybrid Invariant Local Features for Multiple Satellite Image Matching and Registration

Sparse representation optimization of Gaussian mixed feature of image based on convolution neural network

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