4.7 Article

Joint Posterior Probability Active Learning for Hyperspectral Image Classification

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

Dual-Stage Approach Toward Hyperspectral Image Super-Resolution

Qiang Li et al.

Summary: This paper proposes a new structure for hyperspectral image super-resolution, called DualSR, which consists of a coarse stage and a fine stage. In the coarse stage, high similarity bands are processed band-by-band to achieve super-resolution. In the fine stage, an enhanced back-projection method is used to improve spatial-spectral consistency. Extensive experiments demonstrate the effectiveness of the proposed approach.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2022)

Article Geochemistry & Geophysics

An Adaptive Multiview Active Learning Approach for Spectral&x2013;Spatial Classification of Hyperspectral Images

Zhou Zhang et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2020)

Article Geochemistry & Geophysics

Subpixel-Pixel-Superpixel-Based Multiview Active Learning for Hyperspectral Images Classification

Yu Li et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2020)

Article Environmental Sciences

Fully Dense Multiscale Fusion Network for Hyperspectral Image Classification

Zhe Meng et al.

REMOTE SENSING (2019)

Article Geochemistry & Geophysics

Multiview Intensity-Based Active Learning for Hyperspectral Image Classification

Xiang Xu et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2018)

Article Geochemistry & Geophysics

Learning and Transferring Deep Joint Spectral-Spatial Features for Hyperspectral Classification

Jingxiang Yang et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2017)

Article Engineering, Electrical & Electronic

Wavelet-Domain Multiview Active Learning for Spatial-Spectral Hyperspectral Image Classification

Xiong Zhou et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2016)

Article Engineering, Electrical & Electronic

An MRF Model-Based Active Learning Framework for the Spectral-Spatial Classification of Hyperspectral Imagery

Shujin Sun et al.

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING (2015)

Article Engineering, Electrical & Electronic

A Support Vector Conditional Random Fields Classifier With a Mahalanobis Distance Boundary Constraint for High Spatial Resolution Remote Sensing Imagery

Yanfei Zhong et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2014)

Article Geochemistry & Geophysics

Spectral-Spatial Classification of Hyperspectral Data Using Loopy Belief Propagation and Active Learning

Jun Li et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2013)

Article Engineering, Electrical & Electronic

Active Learning: Any Value for Classification of Remotely Sensed Data?

Melba M. Crawford et al.

PROCEEDINGS OF THE IEEE (2013)

Article Geochemistry & Geophysics

Simplified Conditional Random Fields With Class Boundary Constraint for Spectral-Spatial Based Remote Sensing Image Classification

Guangyun Zhang et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2012)

Article Engineering, Electrical & Electronic

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Devis Tuia et al.

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING (2011)

Article Geochemistry & Geophysics

Hyperspectral Image Segmentation Using a New Bayesian Approach With Active Learning

Jun Li et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2011)

Article Computer Science, Artificial Intelligence

Learning Conditional Random Fields for Classification of Hyperspectral Images

Ping Zhong et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2010)

Article Geochemistry & Geophysics

Active Learning Methods for Remote Sensing Image Classification

Devis Tuia et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2009)

Article Computer Science, Artificial Intelligence

Segmentation of multispectral remote sensing images using active support vector machines

P Mitra et al.

PATTERN RECOGNITION LETTERS (2004)