Hyperspectral image analysis
11 papers with code • 1 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Hyperspectral image analysis
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Most implemented papers
A Tutorial on Modeling and Inference in Undirected Graphical Models for Hyperspectral Image Analysis
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
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
Endmember Extraction is a critical step in hyperspectral image analysis and classification.
Unsupervised Segmentation of Hyperspectral Images Using 3D Convolutional Autoencoders
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
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
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
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
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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
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
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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.
