Automatic segmentation algorithm for high-spatial-resolution remote sensing images based on self-learning super-pixel convolutional network

Automatic segmentation algorithm for high-spatial-resolution remote sensing images based on self-learning super-pixel convolutional network

Super-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images segmentation. However, this model requires the number of clusters to be set manually, resulting in a low automation degree due to the complexi...

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Bibliographic Details
Main Authors: Zenan Yang, Haipeng Niu, Liang Huang, Xiaoxuan Wang, Liangxin Fan, Dongyang Xiao
Format: Article
Language:English
Published: Taylor & Francis Group 2022-12-01
Series:International Journal of Digital Earth
Subjects:
deep convolution neural network model
super-pixel algorithm
automatic fuzzy clustering
prior entropy fuzzy c-means clustering algorithm
remote sensing images
Online Access:http://dx.doi.org/10.1080/17538947.2022.2083247

Internet

http://dx.doi.org/10.1080/17538947.2022.2083247

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