Classification of Hyperspectral Image using Ensemble Learning methods:A comparative study

Pathak, Diganta Kumar; Kalita, Sanjib Kumar; Bhattacharya, D.

doi:10.1109/indicon49873.2020.9342213

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…Three most popular ensemble learning methods are random forest(RF) [26], adaboost [27] and eXtreme gradient boost-ing(XGBoost) [28]. D.K Pathak et al presented a review of various ensemble learning methods for HSI classification in [29].…”

Section: Introductionmentioning

confidence: 99%

HCEC: An effective Hybrid CNN-Ensemble classifier forHyperspectral Image classification

Pathak,

Kalita,

Bhattacharya

2023

Preprint

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This article proposes a hybrid classifier for hyperspectral image(HSI) integrating the merits of two prominent classifiers: convolution neural network(CNN) and ensemble learning method. Both of them have evidence of efficient recognition capability of finding patterns from data. The ensemble model performs recognition using the salient features extracted by CNN. A modified version of Alexnet compatible with HSI datacube has been adopted as the CNN model. Three ensemble methods have been applied for recognition: adaptive boosting(adaboost), random forest(RF), and extreme gradient boosting(XGBoost). Two base learners have been used with adaboost: decision tree and support vector machine(SVM). Experiments have been performed on three benchmark datasets: Indian Pines,University of Pavia and Kennedy Space Centre, which mostly include land cover of agriculture, forest, soil, rural, and urban area. Experimental results establish the superiority of CNN with adaboosthybrid method where SVM is used as the base learner.

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Section: Introductionmentioning

confidence: 99%

HCEC: An effective Hybrid CNN-Ensemble classifier forHyperspectral Image classification

Pathak,

Kalita,

Bhattacharya

2023

Preprint

Self Cite

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“…The primary objective of those systems is to increase performance by aggregating the findings of multiple weak classifiers. These systems employ a voting technique amongst all the weak classifiers to obtain the ultimate classification result [ 11 ]. A decision tree (DT) [ 12 ] is considered to be the most preliminary bagging technique.…”

Section: Introductionmentioning

confidence: 99%

Computational Intelligence for Observation and Monitoring: A Case Study of Imbalanced Hyperspectral Image Data Classification

Datta

Mallick

Shafi

et al. 2022

Computational Intelligence and Neuroscience

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Imbalance in hyperspectral images creates a crisis in its analysis and classification operation. Resampling techniques are utilized to minimize the data imbalance. Although only a limited number of resampling methods were explored in the previous research, a small quantity of work has been done. In this study, we propose a novel illustrative study of the performance of the existing resampling techniques, viz. oversampling, undersampling, and hybrid sampling, for removing the imbalance from the minor samples of the hyperspectral dataset. The balanced dataset is classified in the next step, using the tree-based ensemble classifiers by including the spectral and spatial features. Finally, the comparative study is performed based on the statistical analysis of the outcome obtained from those classifiers that are discussed in the results section. In addition, we applied a new ensemble hybrid classifier named random rotation forest to our dataset. Three benchmark hyperspectral datasets: Indian Pines, Salinas Valley, and Pavia University, are applied for performing the experiments. We have taken precision, recall, F score, Cohen kappa, and overall accuracy as assessment metrics to evaluate our model. The obtained result shows that SMOTE, Tomek Links, and their combinations stand out to be the more optimized resampling strategies. Moreover, the ensemble classifiers such as rotation forest and random rotation ensemble provide more accuracy than others of their kind.

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Enhancing Image Classification: A Metaheuristic-Driven Approach

Hemashree,

Rohan,

Kalanithi

et al. 2024

Lecture Notes in Networks and Systems

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Classification of Hyperspectral Image using Ensemble Learning methods:A comparative study

Cited by 7 publications

References 17 publications

HCEC: An effective Hybrid CNN-Ensemble classifier forHyperspectral Image classification

HCEC: An effective Hybrid CNN-Ensemble classifier forHyperspectral Image classification

Computational Intelligence for Observation and Monitoring: A Case Study of Imbalanced Hyperspectral Image Data Classification

Enhancing Image Classification: A Metaheuristic-Driven Approach

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