Multiscale Spectral-Spatial Unmixing Network With Boltzmann-Inspired Adaptive Temperature

Hyperspectral unmixing aims to decompose mixed pixels into endmembers with corresponding abundances. However, while several existing convolutional autoencoder methods usually use fixed convolutional kernels, making it difficult to capture the global context. In addition, due to the huge solution spa...

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Vydané v:	IEEE journal of selected topics in applied earth observations and remote sensing Ročník 18; s. 20085 - 20097
Hlavní autori:	Wang, Zhixiang, Xu, Jindong, Wei, Guangyi, Wang, Jie, Yan, Yu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Autoencoders Boltzmann distribution Constraints convolutional autoencoder (AE) Convolutional codes Decoding Distance Energy distribution Euclidean geometry Feature extraction Hyperspectral imaging hyperspectral unmixing (HU) Image reconstruction Kernel multiscale spectral–spatial attention Particle energy Pixels Representation learning Solution space Sparsity Spectral bands Temperature distribution Temperature effects temperature matrix
ISSN:	1939-1404, 2151-1535
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