Accelerated Adaptive Feature Balance Technique Based on TEMD for Hyperspectral Classification
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2022.3193503
Abstract: Tridimensional empirical mode decomposition (TEMD) has been successfully employed as a powerful tool for hyperspectral classification. It achieves good performance to analyze nonlinear/nonstationary data and capture abstract features of hyperspectral images (HSIs). However, the major… read more here.
Keywords: adaptive feature; hyperspectral classification; accelerated adaptive; balance ... See more keywords
Hyperspectral Classification Through Unmixing Abundance Maps Addressing Spectral Variability
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2019.2892903
Abstract: Climate change and anthropogenic pressure are causing an indisputable decline in biodiversity; therefore, the need of environmental knowledge is important to develop the appropriate management plans. In this context, remote sensing and, specifically, hyperspectral imagery… read more here.
Keywords: classification unmixing; spectral variability; variability; hyperspectral classification ... See more keywords
Spectral–Spatial Fractal Residual Convolutional Neural Network With Data Balance Augmentation for Hyperspectral Classification
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2020.3046840
Abstract: The development of deep learning has brought new prospects into the field of hyperspectral classification, and the classification ability of this method for the classification of hyperspectral images (HSIs) has been continuously improved. However, there… read more here.
Keywords: network data; data balance; hyperspectral classification; spectral spatial ... See more keywords
Spatial–spectral hyperspectral classification using local binary patterns and Markov random fields
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Applied Remote Sensing"
DOI: 10.1117/1.jrs.11.035002
Abstract: Abstract. Local binary patterns (LBPs) have been extensively used to yield spatial features for the classification of general imagery, and a few recent works have applied these patterns to the classification of hyperspectral imagery. Although… read more here.
Keywords: binary patterns; hyperspectral classification; classification; markov random ... See more keywords