A new modified fuzzy c-means algorithm for multispectral satellite images segmentation

In this paper, linking with the basic principle of FCM algorithm, on the basis of theory research, a method of the cluster analysis that FCM and the genetic algorithm are combined together is proposed. Firstly, the approximate optimal solution obtained by the genetic algorithm is taken as the original value of the FCM algorithm, then carrying on the local search to obtain the global optimal solution, the final segmentation result is achieved at last. The experiment results prove that in the view of the flame image segmentation, this method shows the good performance, and has the widespread serviceability, so it is the practical method in image segmentation.

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“…The influence of the flame edge and the determine factors of the flame intensity are also discussed [2]. …”

Section: B Improved Fcm Algorithm (M-fcm)mentioning

confidence: 99%

The Research on Flame Image Segmentation Based on Fuzzy Clustering and Genetic Algorithm

Liu

Zhang

Zhao

et al. 2009

2009 First International Conference on Information Science and Engineering

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“…Supervised classification algorithms can be further divided into statistical algorithms [52], decision tree algorithms [53], artificial neural networks [54] and support vector machine (SVM) algorithms [55][56][57]. Unsupervised classification algorithms can be further divided into K-means algorithms [58], fuzzy c-means algorithms [59] and Affinity Propagation (AP) clustering algorithms [60]. Semi-supervised classification methods can improve an algorithm's performance by utilizing non-tagged samples [61][62][63][64].…”

Section: Svm Algorithm For Luc Estimationmentioning

confidence: 99%

Identifying Key Hydrological Processes in Highly Urbanized Watersheds for Flood Forecasting with a Distributed Hydrological Model

Wang

Chen

2019

Water

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The world has experienced large-scale urbanization in the past century, and this trend is ongoing. Urbanization not only causes land use/cover (LUC) changes but also changes the flood responses of watersheds. Lumped conceptual hydrological models cannot be effectively used for flood forecasting in watersheds that lack long time series of hydrological data to calibrate model parameters. Thus, physically based distributed hydrological models are used instead in these areas, but considerable uncertainty is associated with model parameter derivation. To reduce model parameter uncertainty in physically based distributed hydrological models for flood forecasting in highly urbanized watersheds, a procedure is proposed to control parameter uncertainty. The core concept of this procedure is to identify the key hydrological and flood processes in the highly urbanized watersheds and the sensitive model parameters related to these processes. Then, the sensitive model parameters are adjusted based on local runoff coefficients to reduce the parameter uncertainty. This procedure includes these steps: collecting the latest LUC information or estimating this information using satellite remote sensing images, analyzing LUC spatial patterns and identifying dominant LUC types and their spatial structures, choosing and establishing a distributed hydrological model as the forecasting tool, and determining the initial model parameters and identifying the key hydrological processes and sensitive model parameters based on a parameter sensitivity analysis. A highly urbanized watershed called Shahe Creek in the Pearl River Delta area was selected as a case study. This study finds that the runoff production processes associated with both the ferric luvisol and acric ferralsol soil types and the runoff routing process on urban land are key hydrological processes. Additionally, the soil water content under saturated conditions, the soil water content under field conditions and the roughness of urban land are sensitive parameters.

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“…Standard FCM is the non-surveillance clustering algorithm, and its success is mainly due to that in order to solve the membership of each image pixel, the fuzziness is introduced. Compared with the crisp or the hard segmentation method, FCM can retain more information of the primitive image [2] .The steps of FCM: ,calculate the membership degree that i…”

Section: A Fuzzy C-means Clustering Algorithmmentioning

confidence: 99%

Fuzzy C-Mean Clustering Image Segmentation Algorithm Research for MRI Sport Graphics Based on Genetic Algorithm

Tian

Wang

2014

AMM

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In this paper, linking with the basic principle of FCM (Fuzzy c-means clustering) algorithm, on the basis of theory research, a method of the cluster analysis of FCM based on genetic algorithm is proposed. The genetic algorithm is promising in MRI (magnetic resonance image) processing because it is inherently parallel and coincides with the self-governing biological process. Firstly, the approximate optimal solution obtained by the FCM algorithm is taken as the original value, then combined with intensity-texture-position feature space in order to produce connected regions shown in the image, the final segmentation result is achieved at last. The experiment results prove that in the view of the MRI sport image segmentation, this algorithm provides fast segmentation with high perceptual segmentation quality.

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A new modified fuzzy c-means algorithm for multispectral satellite images segmentation

Cited by 20 publications

References 6 publications

The Research on Flame Image Segmentation Based on Fuzzy Clustering and Genetic Algorithm

The Research on Flame Image Segmentation Based on Fuzzy Clustering and Genetic Algorithm

Identifying Key Hydrological Processes in Highly Urbanized Watersheds for Flood Forecasting with a Distributed Hydrological Model

Fuzzy C-Mean Clustering Image Segmentation Algorithm Research for MRI Sport Graphics Based on Genetic Algorithm

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