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Xueliang Wang, Nan Yang, Enjun Liu, Wencheng Gu, Jinglin Zhang, Shuo Zhao, Guijiang Sun and Jian Wang
In order to solve the problem of manual labeling in semi-supervised tree species classification, this paper proposes a pixel-level self-supervised learning model named M-SSL (multisource self-supervised learning), which takes the advantage of the informa...
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Zifan Rong, Xuesong Jiang, Linfeng Huang and Hongping Zhou
Pan-sharpening aims to create high-resolution spectrum images by fusing low-resolution hyperspectral (HS) images with high-resolution panchromatic (PAN) images. Inspired by the Swin transformer used in image classification tasks, this research constructs...
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Jihyoung Ryu and Yeongmin Jang
Convolution neural networks have received much interest recently in the categorization of hyperspectral images (HSI). Deep learning requires a large number of labeled samples in order to optimize numerous parameters due to the expansion of architecture d...
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Guochao Zhang, Weijia Cao and Yantao Wei
With the development of the hyperspectral imaging technique, hyperspectral image (HSI) classification is receiving more and more attention. However, due to high dimensionality, limited or unbalanced training samples, spectral variability, and mixing pixe...
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Loganathan Agilandeeswari, Manoharan Prabukumar, Vaddi Radhesyam, Kumar L. N. Boggavarapu Phaneendra and Alenizi Farhan
Hyperspectral imaging (HSI), measuring the reflectance over visible (VIS), near-infrared (NIR), and shortwave infrared wavelengths (SWIR), has empowered the task of classification and can be useful in a variety of application areas like agriculture, even...
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