An unsupervised semantic segmentation method that combines the ImSE-Net model with SLICm superpixel optimization

Zenan Yanga School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, People’s Republic of ChinaView further author information

Haipeng Niua School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, People’s Republic of ChinaCorrespondence[email protected]
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Xiaoxuan Wangb Key Laboratory of Spatio-temporal Information and Ecological Restoration of Mines of Natural Resources of the People’s Republic of China, Henan Polytechnic University, Jiaozuo, People’s Republic of ChinaView further author information

Liang Huangc Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming, People’s Republic of ChinaView further author information

Kui Yanga School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, People’s Republic of ChinaView further author information

An unsupervised semantic segmentation method that combines the ImSE-Net model with SLICm superpixel optimization

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An unsupervised semantic segmentation method that combines the ImSE-Net model with SLICm superpixel optimization

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