Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers
Authors
Keywords
-
Journal
Remote Sensing
Volume 11, Issue 23, Pages 2788
Publisher
MDPI AG
Online
2019-11-26
DOI
10.3390/rs11232788
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Tensor-Based Classification Models for Hyperspectral Data Analysis
- (2018) Konstantinos Makantasis et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- The use of airborne hyperspectral data for tree species classification in a species-rich Central European forest area
- (2016) Ronny Richter et al. International Journal of Applied Earth Observation and Geoinformation
- Spectral–spatial hyperspectral image ensemble classification via joint sparse representation
- (2016) Erlei Zhang et al. PATTERN RECOGNITION
- Review of studies on tree species classification from remotely sensed data
- (2016) Fabian Ewald Fassnacht et al. REMOTE SENSING OF ENVIRONMENT
- Semi-supervised SVM for individual tree crown species classification
- (2015) Michele Dalponte et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Spatially regularized semisupervised Ensembles of Extreme Learning Machines for hyperspectral image segmentation
- (2015) Borja Ayerdi et al. NEUROCOMPUTING
- Comparison of Feature Reduction Algorithms for Classifying Tree Species With Hyperspectral Data on Three Central European Test Sites
- (2014) Fabian E. Fassnacht et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Forecasting potential bark beetle outbreaks based on spruce forest vitality using hyperspectral remote-sensing techniques at different scales
- (2013) A. Lausch et al. FOREST ECOLOGY AND MANAGEMENT
- Hyperspectral Remote Sensing Image Classification Based on Rotation Forest
- (2013) Junshi Xia et al. IEEE Geoscience and Remote Sensing Letters
- Spectral–Spatial Classification of Hyperspectral Images Based on Hidden Markov Random Fields
- (2013) Pedram Ghamisi et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Hyperspectral Remote Sensing Data Analysis and Future Challenges
- (2013) Jose M. Bioucas-Dias et al. IEEE Geoscience and Remote Sensing Magazine
- Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
- (2012) José M. Bioucas-Dias et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Semisupervised Local Discriminant Analysis for Feature Extraction in Hyperspectral Images
- (2012) Wenzhi Liao et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Tree Species Discrimination in Tropical Forests Using Airborne Imaging Spectroscopy
- (2012) Jean-Baptiste Feret et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data
- (2012) Michele Dalponte et al. REMOTE SENSING OF ENVIRONMENT
- Spectral–Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields
- (2011) Jun Li et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- A review of advanced techniques for detecting plant diseases
- (2010) Sindhuja Sankaran et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Variation in foliar water content and hyperspectral reflectance ofPinus patulatrees infested bySirex noctilio
- (2010) O Mutanga et al. Southern Forests
- A systematic analysis of performance measures for classification tasks
- (2009) Marina Sokolova et al. INFORMATION PROCESSING & MANAGEMENT
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started