Exponential Weighted Random Forest for Hyperspectral Image Classification
Authors:
- 1 July 2019
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 3297-3300
Abstract
Hyperspectral image (HSI) classification is a challenging task due to the problem of a few labeled samples versus the high dimensional features. Random forest (RF) have been applied to the HSI problem in the past. For RF based HSI classification methods, equal weights/votes have been considered to all decision trees in the past. However, this may not be true always for varying test cases. Hence, this paper proposes an Exponentially Weighted Random Forest (EWRF) method which uses dynamic association with trained trees. Thus, we try to capture the relationship between trained trees and test case in EWRF. The performance of the proposed method has been compared with state-of-the-art methods over two well-known benchmark datasets. The experimental results show that EWRF can provide competitive solutions for hyperspectral image classification.Keywords
This publication has 14 references indexed in Scilit:
- Random forest in remote sensing: A review of applications and future directionsISPRS Journal of Photogrammetry and Remote Sensing, 2016
- Low-rank group inspired dictionary learning for hyperspectral image classificationSignal Processing, 2016
- Weighted Hybrid Decision Tree Model for Random Forest ClassifierJournal of The Institution of Engineers (India): Series B, 2015
- Multiple Kernel Learning via Low-Rank Nonnegative Matrix Factorization for Classification of Hyperspectral ImageryIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014
- Spectral-Spatial Constraint Hyperspectral Image ClassificationIEEE Transactions on Geoscience and Remote Sensing, 2013
- Generalized Composite Kernel Framework for Hyperspectral Image ClassificationIEEE Transactions on Geoscience and Remote Sensing, 2013
- Composite Kernels for Hyperspectral Image ClassificationIEEE Geoscience and Remote Sensing Letters, 2006
- Investigation of the random forest framework for classification of hyperspectral dataIEEE Transactions on Geoscience and Remote Sensing, 2005
- Classification of hyperspectral remote sensing images with support vector machinesIEEE Transactions on Geoscience and Remote Sensing, 2004
- Random ForestsMachine Learning, 2001