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|a 10.1109/TIP.2024.3492724
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|a eng
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|a Liu, Huan
|e verfasserin
|4 aut
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|a SegHSI
|b Semantic Segmentation of Hyperspectral Images with Limited Labeled Pixels
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|c 2024
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|a Date Revised 12.11.2024
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|a Citation Status Publisher
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|a Hyperspectral images (HSIs), with hundreds of narrow spectral bands, are increasingly used for ground object classification in remote sensing. However, many HSI classification models operate pixel-by-pixel, limiting the utilization of spatial information and resulting in increased inference time for the whole image. This paper proposes SegHSI, an effective and efficient end-to-end HSI segmentation model, alongside a novel training strategy. SegHSI adopts a head-free structure with cluster attention modules and spatial-aware feedforward networks (SA-FFN) for multiscale spatial encoding. Cluster attention encodes pixels through constructed clusters within the HSI, while SA-FFN integrates depth-wise convolution to enhance spatial context. Our training strategy utilizes a student-teacher model framework that combines labeled pixel class information with consistency learning on unlabeled pixels. Experiments on three public HSI datasets demonstrate that SegHSI not only surpasses other state-of-the-art models in segmentation accuracy but also achieves inference time at the scale of seconds, even reaching sub-second speeds for full-image classification. Code is available at https://github.com/huanliu233/SegHSI
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|a Journal Article
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|a Li, Wei
|e verfasserin
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|a Xia, Xiang-Gen
|e verfasserin
|4 aut
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| 700 |
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|a Zhang, Mengmeng
|e verfasserin
|4 aut
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| 700 |
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|a Guo, Zhengqi
|e verfasserin
|4 aut
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| 700 |
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|a Song, Lujie
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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|u http://dx.doi.org/10.1109/TIP.2024.3492724
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