Hyperspectral Image Classification With Rotation Random Forest ...

In this paper, we propose a novel ensemble approach, namely rotation random forest via kernel principal component analysis (RoRF-KPCA). In ...

Hyperspectral Image Classification With Rotation. Random Forest Via KPCA. View Document. 10. Paper. Citations. 427. Full. Text Views. Related Articles.

Hyperspectral Image Classification with Rotation Random Forest Via ...

Hyperspectral images offer a good separability among land cover classes, owing to their advantages to provide spectral bands with narrow wavelength intervals.

Hyperspectral Image Classification With Rotation Random Forest ...

A novel ensemble approach, namely rotation random forest via kernel principal component analysis (RoRF-KPCA), in which the original feature space is first ...

Hyperspectral Image Classification Based on Improved Rotation ...

In this paper, an efficient hyperspectral image classification method based on improved Rotation Forest (ROF) is proposed. It is named ROF-KELM. Firstly, Non- ...

[PDF] Hyperspectral Remote Sensing Image Classification Based on ...

Experimental results revealed that Rotation Forest, especially with PCA transformation, could produce more accurate results than bagging, AdaBoost, ...

Hyperspectral Remote Sensing Image Classification Based on ...

Experimental results revealed that Rotation. Forest, especially with PCA transformation, could produce more accurate results than bagging, AdaBoost, and Random ...

(PDF) Hyperspectral Image Classification Based on Semi ...

Rotation forest (RoF), combining feature extraction and classifier ensembles, has been successfully applied to hyperspectral (HS) image ...

Hyperspectral Remote Sensing Image Classification Based ... - HAL

Experimental results revealed that Rotation Forest, especially with PCA transformation, could produce more accurate results than bagging, AdaBoost, and Random ...

Hyperspectral Image Classification Based on Semi-Supervised ...

Rotation forest (RoF), combining feature extraction and classifier ensembles, has been successfully applied to hyperspectral (HS) image classification by ...

Spectral–Spatial Classification for Hyperspectral Data Using ...

Spectral–Spatial Classification for Hyperspectral Data Using Rotation Forests With Local Feature Extraction and Markov Random Fields · Attached ...

Random Forest and Rotation Forest for fully polarized SAR image ...

Inspired by the complementarity between spectral and spatial features bringing remarkable improvements in optical image classification, the complementary ...

Class-Separation-Based Rotation Forest for Hyperspectral Image ...

... rotation forest (RoF) method for the pixelwise classification of hyperspectral images. RoF, which is an ensemble of decision tree classifiers, uses random ...

Semi-supervised rotation forest based on ensemble margin theory ...

In this paper, an adaptive semi-supervised rotation forest (SSRoF) algorithm is proposed for the classification of hyperspectral images with limited training ...

Full article: Two hidden layer neural network-based rotation forest ...

Decision tree-based Rotation Forest could generate satisfactory but lower classification accuracy for a given training sample set and image data, ...

Two hidden layer neural network-based rotation forest ensemble for ...

Decision tree-based Rotation Forest could generate satisfactory but lower classification accuracy for a given training sample set and image data, owing to the ...

Random Forest Ensembles and Extended Multiextinction Profiles for ...

Classification techniques for hyperspectral images based on random forest (RF) ensembles and extended multiextinction profiles (EMEPs) are proposed as a ...

Hyperspectral Image Classification With Canonical Correlation Forests

Two well-known ensemble learning classifiers for hyperspectral data are random forest (RF) and rotation forest (RoF). In this paper, we proposed ...

Spectral–Spatial Classification for Hyperspectral Data Using ...

Index Terms—Feature extraction, hyperspectral image classifi- cation, Markov random fields (MRFs), rotation forests. I. INTRODUCTION. OVER the past 20 years, ...

Hyperspectral Image Classification: A Hybrid Approach Integrating ...

This paper provides a unique hybrid methodology for hyperspectral image (HSI) analysis that integrates Random Forest (RF) feature selection with Convolutional ...