Deep interpretable fully CNN structure for sparse hyperspectral unmixing via model-driven and data-driven integration
Hyperspectral unmixing (HSU), which aims to identify constituent materials and estimate the
corresponding proportions in a scene, is an essential research topic in remote sensing. Most …
corresponding proportions in a scene, is an essential research topic in remote sensing. Most …
Endmember Abundance Prediction in Hyperspectral Unmixing: The Impact of Endmember Extraction Algorithms and Self-Attention in Autoencoders
H Wickramathilaka, R Ratnayake… - 2023 IEEE 17th …, 2023 - ieeexplore.ieee.org
Recently, deep learning techniques have gained popularity in hyperspectral unmixing (HU).
The autoencoder framework, which optimizes the pixel reconstruction loss, is the most …
The autoencoder framework, which optimizes the pixel reconstruction loss, is the most …
[HTML][HTML] Benchmark for Hyperspectral Unmixing Algorithm Evaluation
V Paura, V Marcinkevičius - Informatica, 2023 - content.iospress.com
Over the past decades, many methods have been proposed to solve the linear or nonlinear
mixing of spectra inside the hyperspectral data. Due to a relatively low spatial resolution of …
mixing of spectra inside the hyperspectral data. Due to a relatively low spatial resolution of …
[引用][C] Virtual Spectral Feature Reconstruction and Adaptive Spatial Feature Extraction Based on Convolutional Neural Networks to Classify Hyperspectral Images …
M HAMOUDA, MS Bouhlel - Available at SSRN 4167474