Densely connected deep random forest for hyperspectral imagery classification
Sign Up to like & getrecommendations! Published in 2018 at "International Journal of Remote Sensing"
DOI: 10.1080/01431161.2018.1547932
Abstract: ABSTRACT In very recent years, deep learning based methods have been widely introduced for the classification of hyperspectral images (HSI). However, these deep models need lots of training samples to tune abundant parameters which induce… read more here.
Keywords: classification; connected deep; random forest; deep random ... See more keywords
Exploiting Spectral–Spatial Information Using Deep Random Forest for Hyperspectral Imagery Classification
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2021.3112198
Abstract: In recent years, deep learning methods have been widely applied to hyperspectral image (HSI) classification. Besides convolutional neural network (CNN)-based deep learning, deep random forest (RF)-based method, such as densely connected deep RF (DCDRF), was… read more here.
Keywords: spectral spatial; deep random; classification; random forest ... See more keywords
Ensemble Deep Random Vector Functional Link Neural Network for Regression
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Systems, Man, and Cybernetics: Systems"
DOI: 10.1109/tsmc.2022.3213628
Abstract: Inspired by the ensemble strategy of machine learning, deep random vector functional link (dRVFL), and ensemble dRVFL (edRVFL) has shown state-of-the-art results on different datasets. Our present work first fills the gap of dRVFL and… read more here.
Keywords: regression; edrvfl; random; deep random ... See more keywords