Active learning-based hyperspectral image classification: a reinforcement learning approach

Patel, Usha; Patel, Vibha

doi:10.1007/s11227-023-05568-7

Cited by 9 publications

(2 citation statements)

References 30 publications

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“…Reinforcement learning (RL), such as deep Q-learning (DQL), offers a different approach to HS image classification [27][28][29][30][31]. The classification of HS images through DQL represents a significant leap in the analytical capabilities of remote sensing (RS).…”

Section: Related Workmentioning

confidence: 99%

Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization

Shoeibi,

Nevisi,

Salehi

et al. 2024

CMC

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Hyperspectral (HS) image classification plays a crucial role in numerous areas including remote sensing (RS), agriculture, and the monitoring of the environment. Optimal band selection in HS images is crucial for improving the efficiency and accuracy of image classification. This process involves selecting the most informative spectral bands, which leads to a reduction in data volume. Focusing on these key bands also enhances the accuracy of classification algorithms, as redundant or irrelevant bands, which can introduce noise and lower model performance, are excluded. In this paper, we propose an approach for HS image classification using deep Q learning (DQL) and a novel multi-objective binary grey wolf optimizer (MOBGWO). We investigate the MOBGWO for optimal band selection to further enhance the accuracy of HS image classification. In the suggested MOBGWO, a new sigmoid function is introduced as a transfer function to modify the wolves' position. The primary objective of this classification is to reduce the number of bands while maximizing classification accuracy. To evaluate the effectiveness of our approach, we conducted experiments on publicly available HS image datasets, including Pavia University, Washington Mall, and Indian Pines datasets. We compared the performance of our proposed method with several state-of-the-art deep learning (DL) and machine learning (ML) algorithms, including long short-term memory (LSTM), deep neural network (DNN), recurrent neural network (RNN), support vector machine (SVM), and random forest (RF). Our experimental results demonstrate that the Hybrid MOBGWO-DQL significantly improves classification accuracy compared to traditional optimization and DL techniques. MOBGWO-DQL shows greater accuracy in classifying most categories in both datasets used. For the Indian Pine dataset, the MOBGWO-DQL architecture achieved a kappa coefficient (KC) of 97.68% and an overall accuracy (OA) of 94.32%. This was accompanied by the lowest root mean square error (RMSE) of 0.94, indicating very precise predictions with minimal error. In the case of the Pavia University dataset, the MOBGWO-DQL model demonstrated outstanding performance with the highest KC of 98.72% and an impressive OA of 96.01%. It also recorded the lowest RMSE at 0.63, reinforcing its accuracy in predictions. The results clearly demonstrate that the proposed MOBGWO-DQL architecture not only reaches a highly accurate model more quickly but also maintains superior performance throughout the training process.

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Section: Related Workmentioning

confidence: 99%

Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization

Shoeibi,

Nevisi,

Salehi

et al. 2024

CMC

View full text Add to dashboard Cite

show abstract

“…In many natural scenes, we often pay more attention to minority categories, such as disease diagnosis [2], intrusion detection [3], image classification [4], natural language processing [5], and other fields. In these areas, model misjudgment or omission of a few category samples may cause severe consequences and increase social costs and risks.…”

Section: Introductionmentioning

confidence: 99%

Constructing small sample datasets with game mixed sampling and improved genetic algorithm

Zhu,

Wang,

Fan

2024

J Supercomput

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Graph convolutional and random Fourier feature mapping for hyperspectral image clustering

Li,

Liu

2024

J Supercomput

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Active learning-based hyperspectral image classification: a reinforcement learning approach

Cited by 9 publications

References 30 publications

Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization

Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization

Constructing small sample datasets with game mixed sampling and improved genetic algorithm

Graph convolutional and random Fourier feature mapping for hyperspectral image clustering

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