Hyperspectral image analysis

12 papers with code • 1 benchmarks • 2 datasets

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Most implemented papers

A Tutorial on Modeling and Inference in Undirected Graphical Models for Hyperspectral Image Analysis

UBGewali/tutorial-UGM-hyperspectral 25 Jan 2018

However, graphical models have not been easily accessible to the larger remote sensing community as they are not discussed in standard remote sensing textbooks and not included in the popular remote sensing software and toolboxes.

AeroRIT: A New Scene for Hyperspectral Image Analysis

aneesh3108/AeroRIT 17 Dec 2019

We investigate applying convolutional neural network (CNN) architecture to facilitate aerial hyperspectral scene understanding and present a new hyperspectral dataset-AeroRIT-that is large enough for CNN training.

Comparison of VCA and GAEE algorithms for Endmember Extraction

douglaswinstonr/GAEE 27 May 2018

Endmember Extraction is a critical step in hyperspectral image analysis and classification.

Unsupervised Segmentation of Hyperspectral Images Using 3D Convolutional Autoencoders

jnalepa/3d-cae 20 Jul 2019

Hyperspectral image analysis has become an important topic widely researched by the remote sensing community.

Hierarchical Sparse Subspace Clustering (HESSC): An Automatic Approach for Hyperspectral Image Analysis

Kasra2020/HESSC 28 Jul 2020

In addition, in order to have a comparison with conventional clustering algorithms, HESSC’s performance is compared with K-means and FCM.

Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification

lcmou/DRL4BS 15 Mar 2021

To this end, we frame the problem of unsupervised band selection as a Markov decision process, propose an effective method to parameterize it, and finally solve the problem by deep reinforcement learning.

A distribution-dependent Mumford-Shah model for unsupervised hyperspectral image segmentation

berkels/msiplib 28 Mar 2022

We equipped the MS functional with a novel robust distribution-dependent indicator function designed to handle the characteristic challenges of hyperspectral data.

Exploring the Relationship between Center and Neighborhoods: Central Vector oriented Self-Similarity Network for Hyperspectral Image Classification

lms-07/CVSSN IEEE Transactions on Circuits and Systems for Video Technology 2022

Specifically, based on two similarity measures, we firstly design an adaptive weight addition based spectral vector self-similarity module (AWA-SVSS) in input space and a Euclidean distance based feature vector self-similarity module (ED-FVSS) in feature space to fully mine the central vector oriented spatial relationships.

Measuring complex refractive index through deeplearning-enabled optical reflectometry

tigerwang3133/ReflectoNet 2D Materials 2023

Optical spectroscopy is indispensable for research and development in nanoscience and nanotechnology, microelectronics, energy, and advanced manufacturing.

Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning with Distance Covariance Representation for Hyperspectral Image Classification

lms-07/AMS-M2ESL IEEE Transactions on Geoscience and Remote Sensing 2023

Subsequently, based on distance covariance descriptor, a dual channel distance covariance representation (DC-DCR) module is proposed for modeling unified spectral-spatial feature representations and exploring spectral-spatial relationships, especially linear and nonlinear interdependence in spectral domain.