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
Published in:International journal of digital earth Vol. 15; no. 1; pp. 1101 - 1124
Main Authors: Yang, Zenan, Niu, Haipeng, Huang, Liang, Wang, Xiaoxuan, Fan, Liangxin, Xiao, Dongyang
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 31.12.2022
Taylor & Francis Ltd
Taylor & Francis Group
Subjects:
Algorithms
Artificial neural networks
automatic fuzzy clustering
Automation
Clustering
Complexity
Decision making
Deep convolution neural network model
Entropy
Feature extraction
Fuzzy logic
image analysis
Image processing
Image segmentation
Iterative methods
Machine learning
Neural networks
Pixels
prior entropy fuzzy C-Means clustering algorithm
Remote sensing
remote sensing images
Residential density
Resolution
super-pixel algorithm
ISSN:1753-8947, 1753-8955, 1753-8955
Online Access:Get full text
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Summary: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 complexity of the iterative clustering process. To address this problem, a segmentation method based on a self-learning super-pixel network (SLSP-Net) and modified automatic fuzzy clustering (MAFC) is proposed. SLSP-Net performs feature extraction, non-iterative clustering, and gradient reconstruction. A lightweight feature embedder is adopted for feature extraction, thus expanding the receiving range and generating multi-scale features. Automatic matching is used for non-iterative clustering, and the overfitting of the network model is overcome by adaptively adjusting the gradient weight parameters, providing a better irregular super-pixel neighborhood structure. An optimized density peak algorithm is adopted for MAFC. Based on the obtained super-pixel image, this maximizes the robust decision-making interval, which enhances the automation of regional clustering. Finally, prior entropy fuzzy C-means clustering is applied to optimize the robust decision-making and obtain the final segmentation result. Experimental results show that the proposed model offers reduced experimental complexity and achieves good performance, realizing not only automatic image segmentation, but also good segmentation results.
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ISSN:1753-8947
1753-8955
1753-8955
DOI:10.1080/17538947.2022.2083247