Deep autoencoder for hyperspectral unmixing via global-local smoothing
Hyperspectral unmixing is to decompose the mixed pixels into pure spectral signatures
(endmembers) and their proportions (abundances). Recently, deep learning-based methods …
(endmembers) and their proportions (abundances). Recently, deep learning-based methods …
Optimal segmentation and improved abundance estimation for superpixel-based Hyperspectral Unmixing
Q Guan, T Xu, S Feng, F Yu, K Song - European Journal of Remote …, 2022 - Taylor & Francis
Superpixel-based hyperspectral unmixing (HU) can effectively reduce spectral variability's
influence on unmixing performance. In the superpixel-based HU method, this study …
influence on unmixing performance. In the superpixel-based HU method, this study …
An Enhanced and Unsupervised Siamese Network with Superpixel-Guided Learning for Change Detection in Heterogeneous Remote Sensing Images
In this article, we consider the issue of change detection (CD) for heterogeneous remote
sensing images. Existing deep learning-based methods for CD usually utilize square …
sensing images. Existing deep learning-based methods for CD usually utilize square …
Using band subset selection for dimensionality reduction in superpixel segmentation of hyperspectral imagery
This paper explores the use of unsupervised band subset selection (BSS) methods as a
dimensionality reduction preprocessing stage in SLIC superpixel segmentation (BSSSLIC) …
dimensionality reduction preprocessing stage in SLIC superpixel segmentation (BSSSLIC) …
Evaluating the effect of band subset selection in SLIC superpixel segmentation
P Pochamreddy, MQ Alkhatib… - … , and Applications for …, 2020 - spiedigitallibrary.org
The Simple Linear Iterative Clustering (SLIC) algorithm is widely used for superpixel
segmentation in hyperspectral image processing. In this paper, we study the effect of band …
segmentation in hyperspectral image processing. In this paper, we study the effect of band …