S<sup>2</sup>PNet: An Interactive Learning Framework for Addressing Spatial&#x2013;Spectral Heterogeneity in H<sup>2</sup> Imagery Classification

S<sup>2</sup>PNet: An Interactive Learning Framework for Addressing Spatial&#x2013;Spectral Heterogeneity in H<sup>2</sup> Imagery Classification

Hyperspectral imagery with high spatial resolution (H<sup>2</sup>) imagery can synchronously obtain the spectral and spatial features of objects, thus providing richer information. However, the exacerbated spatial&#x2013;spectral heterogeneity poses new challenges for classification....

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Bibliographic Details
Main Authors: Shuai Zhang, Yonghua Jiang, Chengjun Wang, Meilin Tan, Bin Du, Feng Tian
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
High spatial resolution (H<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$_2$</tex-math> </inline-formula> </named-content>) imagery
Houston datasets
interactive learning
semantic gap
spatial–spectral heterogeneity
WHU-Hi datasets
Online Access:https://ieeexplore.ieee.org/document/10684570/
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