Fuzzy C-Mean Algorithm with Morphology Similarity Distance

Li, Zhong; Yuan, Jinsha; Zhang, Weihua

doi:10.1109/fskd.2009.180

Cited by 10 publications

(4 citation statements)

References 7 publications

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“…, c. To explore the separability of the proposed feature, the morphological similarity distances of crop time series curves in VH polarization and the combined feature (VV − VH)/(VV + VH) were calculated. The morphological similarity distance [50] is defined based on the classical Euclidean distance, considers the specific distribution differences of each dimension of the features and measures the similarity by combining size and shape factors. The 𝑝𝑝 𝑛𝑛−dimensional data 𝐿𝐿 𝑖𝑖 = (𝑙𝑙 𝑖𝑖1 , … 𝑙𝑙 𝑖𝑖𝑏𝑏 ) 𝑇𝑇 and 𝐿𝐿 𝑗𝑗 = (𝑙𝑙 𝑗𝑗1 , … 𝑙𝑙 𝑗𝑗𝑏𝑏 ) 𝑇𝑇 , 𝑖𝑖, 𝑗𝑗 = 1,2, … , 𝑝𝑝, and the morphological similarity distances of 𝐿𝐿 𝑖𝑖 and 𝐿𝐿 𝑗𝑗 are defined as:…”

Section: Methodsmentioning

confidence: 99%

“…where 𝐷𝐷 𝑀𝑀𝑀𝑀𝑀𝑀 is the morphological similarity distance between 𝐿𝐿 𝑖𝑖 and 𝐿𝐿 𝑗𝑗 ; 𝐷𝐷 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖𝑏𝑏 is the Euclidean distance between 𝐿𝐿 𝑖𝑖 and 𝐿𝐿 𝑗𝑗 ; SAD is the Manhattan distance between 𝐿𝐿 𝑖𝑖 and 𝐿𝐿 𝑗𝑗 ; and ASD is the absolute value of the sum of all dimensional difference values between 𝐿𝐿 𝑖𝑖 and 𝐿𝐿 𝑗𝑗 : To explore the separability of the proposed feature, the morphological similarity distances of crop time series curves in VH polarization and the combined feature (VV − VH)/(VV + VH) were calculated. The morphological similarity distance [50] is defined based on the classical Euclidean distance, considers the specific distribution differences of each dimension of the features and measures the similarity by combining size and shape factors. The p n−dimensional data L i = (l i1 , .…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Rice Mapping in a Subtropical Hilly Region Based on Sentinel-1 Time Series Feature Analysis and the Dual Branch BiLSTM Model

Sun

Zhang

et al. 2022

Remote Sensing

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Timely and accurate information on rice cultivation makes important contributions to the profound reform of the global food and agricultural system, and promotes the development of global sustainable agriculture. With all-day and all-weather observing ability, synthetic aperture radar (SAR) can monitor the distribution of rice in tropical and subtropical areas. To solve the problem of misclassification of rice with no marked signal during the flooding period in subtropical hilly areas, this paper proposes a new feature combination and dual branch bi-directional long short-term memory (DB-BiLSTM) model to achieve high-precision rice mapping using Sentinel-1 time series data. Based on field investigation data, the backscatter time series curves of the rice area were analyzed, and a characteristic index (VV − VH)/(VV + VH) (VV: vertical emission and vertical receipt of polarization, VH: vertical emission and horizontal receipt of polarization) for small areas of hilly land was proposed to effectively distinguish rice and non-rice crops with no marked flooding period. The DB-BiLSTM model was designed, ensuring the independent learning of multiple features and effectively combining the time series information of both (VV − VH)/(VV + VH) and VH features. The city of Shanwei, Guangdong Province, China, was selected as the study area. Experimental results showed that the overall accuracy of the rice mapping results was 97.29%, and the kappa coefficient reached 0.9424. Compared to other methods, the rice mapping results obtained by the proposed method maintained good integrity and had less misclassification, which demonstrated the proposed method’s practical value in accurate and effective rice mapping tasks.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Rice Mapping in a Subtropical Hilly Region Based on Sentinel-1 Time Series Feature Analysis and the Dual Branch BiLSTM Model

Sun

Zhang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Morphological similarity distance : Zhong Li et al [25] show that most clustering algorithms are based on Euclidean distance, but the traditional distance can't accurately measure the similarity as shown in Fig. 1.…”

Section: Morphological Similarity Distancementioning

confidence: 99%

“…The FS index [16] combines the membership degree u and the Euclidean distance between the data points and the data center, which can well measure the compactness within the cluster, but ignores the geometric characteristics of the data and produces certain randomness [38]. Therefore, in order to consider the geometric characteristics of the data, the Euclidean distance in FS index is replaced by morphological similarity distance (MSD) [25,29]. Compared with Euclidean distance, MSD takes into account the shape difference between vectors and reduces the randomness caused by geometric features.…”

Section: The Compactness and Separation Of Fuzzy Clustering Indexesmentioning

confidence: 99%

Fuzzy clustering optimal k selection method based on multi-objective optimization

2023

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Because of the complexity of data sets from the real world, it is difficult to classify the data sets clearly and effectively, thus we prefer to adopt fuzzy clustering approaches to analyze the data sets. However, due to the variety of fuzzy clustering algorithms, and the different number of clusters will lead to different clustering results. The number of clusters is closely related to the clustering division, so how to determine the number of fuzzy clustering (k ) has become a problem. Until now, many researchers have proposed utilizing fuzzy clustering validity indexes to deal with this kind of problem. However, the effectiveness index of fuzzy clustering can only be evaluated on the basis of the fuzzy clustering algorithm FCM to divide the clusters. When the range of k value is too large, FCM's clustering for different k values is quite time-consuming. From this perspective, this paper proposes a fuzzy clustering optimal k selection method based on multiobjective optimization (FMOEA-K). Different from the traditional methods, this method combines the fuzzy clustering effectiveness index with multi-objective optimization algorithm (MOEA), and uses multi-objective optimization algorithm to search the appropriate cluster center concurrently. Because of the concurrency of the multi-objective optimization algorithm, the calculation time is shortened. The experimental results show that compared with the traditional method, the FMOEA-K can shorten the calculation time and improve the accuracy of calculating the optimal k value.

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A novel cluster validity index for fuzzy C-means algorithm

Yang

Liang

et al. 2016

Soft Comput

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Fuzzy C-Mean Algorithm with Morphology Similarity Distance

Cited by 10 publications

References 7 publications

Rice Mapping in a Subtropical Hilly Region Based on Sentinel-1 Time Series Feature Analysis and the Dual Branch BiLSTM Model

Rice Mapping in a Subtropical Hilly Region Based on Sentinel-1 Time Series Feature Analysis and the Dual Branch BiLSTM Model

Fuzzy clustering optimal k selection method based on multi-objective optimization

A novel cluster validity index for fuzzy C-means algorithm

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