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- W2965867926 abstract "The computational capacities increase, decrease of equipment costs, growing need for information, among other reasons; It makes possible increasingly common access to satellite In this context. The investigation of techniques related to remote sensing becomes very important because it provide important information about Earth’s surface. Currently, segmentation is an essential step in applications that make use of satellite images. However, main problem is: the data in a multispectral image shows a low statistical separation and a long quantity of data. In this article we propose to improve balancing of elements for clusters. We use a new term to estimate influence that each element must have for each cluster. This new term can be understood as a repulsion factor and aims to increase differences between groups. This modification is inspired by new term that was integrated into NFCC algorithm (New Fuzzy Centroid Cluster).For tests, we use internal validity of cluster to compare algorithms. Using index we measure characteristics of segmentation and corroborate them with final visual results. Therefore, we conclude that addition of this new term allows balancing elements for each group. As a result we conclude that new term organizes elements better because it avoids a fast convergence of algorithm. Finally, results show that this new factor generates clusters with lower entropy and greater similarity between elements." @default.
- W2965867926 created "2019-08-13" @default.
- W2965867926 creator A5049034346 @default.
- W2965867926 date "2019-06-01" @default.
- W2965867926 modified "2023-10-16" @default.
- W2965867926 title "Classification of satellite images using Rp fuzzy c means for unsupervised classification algorithm" @default.
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- W2965867926 doi "https://doi.org/10.1109/colcaci.2019.8781988" @default.
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