Automatic Determination of Number of Homogenous Regions in SAR Images Utilizing Splitting and Merging Based on a Reversible Jump MCMC Algorithm

Askari, Ghasem; Xu, Aigong; Li, Yu; Alavipanah, Seyed Kazem

doi:10.1007/s12524-013-0269-0

Cited by 8 publications

(7 citation statements)

References 33 publications

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“…Most scholars in the process of constructing remote sensing image segmentation model, spatial information is used as an important constraint information, and space-constrained high-resolution remote sensing image segmentation algorithm. 9,10 As a classical model in graph theory, the minimum spanning tree is widely concerned because it can express the spatial correlation and spectral information contained in remote sensing images completely and efficiently. [11][12][13][14] MST is the undirected adjacency graph in the global right.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Remote sensing image landmark segmentation algorithm based on improved GSA and PCNN combination

Liu¹,

Chen

2020

International Journal of Electrical Engineering & Education

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Application of Remote Sensing Technology in the Development of Urban Functions is presented. Remote sensing image processing for ecological protection and monitoring, this paper proposes a remote sensing image landmark segmentation algorithm based on the IGSA and PCNN. Because the GSA algorithm has the disadvantages of premature convergence and is easy to fall into the local optimal solution, the improved IGSA algorithm is used to extract the ratio of the image entropy and energy to IGSA, and the entropy change value is used as the IGSA algorithm. Based on the global search ability of IGSA, the optimal value of the key parameters affecting the segmentation effect in PCNN model is found. Finally, through experimental comparison, the proposed method has strong advantages in segmentation effect, real-time and robustness

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Remote sensing image landmark segmentation algorithm based on improved GSA and PCNN combination

Liu¹,

Chen

2020

International Journal of Electrical Engineering & Education

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“…Interval type-2 fuzzy model based on FCM [22][23][24] Under the framework of FCM, construct the interval type-2 FCM model by fuzzy exponential factorization, fuzzy membership function or fuzzy clustering center…”

Section: Image Segmentation/ Classification Edge Detectionmentioning

confidence: 99%

Supervised Classification High-Resolution Remote-Sensing Image Based on Interval Type-2 Fuzzy Membership Function

Wang

2018

Remote Sensing

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Because of the degradation of classification accuracy that is caused by the uncertainty of pixel class and classification decisions of high-resolution remote-sensing images, we proposed a supervised classification method that is based on an interval type-2 fuzzy membership function for high-resolution remote-sensing images. We analyze the data features of a high-resolution remote-sensing image and construct a type-1 membership function model in a homogenous region by supervised sampling in order to characterize the uncertainty of the pixel class. On the basis of the fuzzy membership function model in the homogeneous region and in accordance with the 3σ criterion of normal distribution, we proposed a method for modeling three types of interval type-2 membership functions and analyze the different types of functions to improve the uncertainty of pixel class expressed by the type-1 fuzzy membership function and to enhance the accuracy of classification decision. According to the principle that importance will increase with a decrease in the distance between the original, upper, and lower fuzzy membership of the training data and the corresponding frequency value in the histogram, we use the weighted average sum of three types of fuzzy membership as the new fuzzy membership of the pixel to be classified and then integrated into the neighborhood pixel relations, constructing a classification decision model. We use the proposed method to classify real high-resolution remote-sensing images and synthetic images. Additionally, we qualitatively and quantitatively evaluate the test results. The results show that a higher classification accuracy can be achieved with the proposed algorithm.

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“…Spatial and spectral information simultaneously can be used for improved identification of water body. Objective of this paper is to segment the image based on colour transformed clustering (Chitade and Katiyar, 2010;Plataniotis and Venetsanopoulos, 2013) method to automatically identify number of homogeneous regions (Askari et al, 2013) and further extracting the surface water bodies from the similar spectral signature areas (Feyisa et al, 2014). This work is an extended work of water extraction based on spectral characteristics (Parveen et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Colour transformed clustering-based water body extraction using IRS-1C LISS III image

Parveen

Kulkarni

Mytri³

2019

IJSTDS

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The algorithm presented in this research article extracts and delineates the water areas using IRS-1C LISS III images. Methods available in the literature are biased with user-defined thresholds. The objective of the proposed algorithm is to provide accurate information about surface water. Initially, the input image is subjected to colour transformation clustering to extract all the similar hydrological characteristics geo-spatial features in the picture. Every cluster is then submitted to surface water detection by considering spectral information. Finally, the surface water bodies are outlined with sharp inter-regional boundaries and made visually vibrant. Thus the task of identification of water bodies is made simple, accurate and easy for the user with satisfactory qualitative analysis. Results obtained are compared with statistics obtained by structural filtering, NDVI method, and spectral segmentation method. The excellent potential surface water areas can be extracted by using the proposed method. . Her main interesting research areas are digital image processing, geo-spatial information science, satellite image analysis and geo-spatial feature extraction.Colour transformed clustering-based water body extraction 85

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Automatic Determination of Number of Homogenous Regions in SAR Images Utilizing Splitting and Merging Based on a Reversible Jump MCMC Algorithm

Cited by 8 publications

References 33 publications

RETRACTED: Remote sensing image landmark segmentation algorithm based on improved GSA and PCNN combination

RETRACTED: Remote sensing image landmark segmentation algorithm based on improved GSA and PCNN combination

Supervised Classification High-Resolution Remote-Sensing Image Based on Interval Type-2 Fuzzy Membership Function

Colour transformed clustering-based water body extraction using IRS-1C LISS III image

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