Hierarchical Feature Fusion and Selection for Hyperspectral Image Classification

Feng, Zhixi; Liu, Xuehu; Yang, Shuyuan; Zhang, Kai; Jiao, Licheng

doi:10.1109/lgrs.2023.3236672

Cited by 11 publications

(8 citation statements)

References 14 publications

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“…PLSR is particularly suitable for high-dimensional datasets and situations where multicollinearity problems exist. It reduces the dimensionality of the data by finding the combination of independent variables that has the highest correlation with the dependent variable, which better captures the structure of the data and builds the regression model [ 74 , 83 ]. MLR is a statistical method widely employed to build regression models to analyze and predict the relationship between the dependent variable and one or more independent variables.…”

Section: Hyperspectral Information Analysis Methods For Tea Fresh Lea...mentioning

confidence: 99%

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Tang,

Luo,

Yang

et al. 2023

Foods

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As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.

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Section: Hyperspectral Information Analysis Methods For Tea Fresh Lea...mentioning

confidence: 99%

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Tang,

Luo,

Yang

et al. 2023

Foods

View full text Add to dashboard Cite

show abstract

“…These often yield superior performance but at the cost of increased computational complexity levels. Recent advances include the use of machine learning models, particularly deep learning 32 – 34 , for feature selection. Methods like auto-encoders and neural attention mechanisms have shown promise but require substantial computational resources and are not interpretable, limiting their application in certain domains.…”

Section: In-depth Review Of Existing Machine Learning Models Used For...mentioning

confidence: 99%

Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Potharlanka,

2024

Sci Rep

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Feature selection is an indispensable aspect of modern machine learning, especially for high-dimensional datasets where overfitting and computational inefficiencies are common concerns. Traditional methods often employ either filter, wrapper, or embedded approaches, which have limitations in terms of robustness, computational load, or capability to capture complex interactions among features. Despite the utility of metaheuristic algorithms like Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Whale Optimization (WOA) in feature selection, there still exists a gap in efficiently incorporating feature importance feedback into these processes. This paper presents a novel approach that integrates the strengths of PSO, FA, and WOA algorithms into an ensemble model and further enhances its performance by incorporating a Deep Q-Learning framework for relevance feedbacks. The Deep Q-Learning module intelligently updates feature importance based on model performance, thereby fine-tuning the selection process iteratively. Our ensemble model demonstrates substantial gains in effectiveness over traditional and individual metaheuristic approaches. Specifically, the proposed model achieved a 9.5% higher precision, an 8.5% higher accuracy, an 8.3% higher recall, a 4.9% higher AUC, and a 5.9% higher specificity across multiple software bug prediction datasets and samples. By resolving some of the key issues in existing feature selection methods and achieving superior performance metrics, this work paves the way for more robust and efficient machine learning models in various applications, from healthcare to natural language processing scenarios. This research provides an innovative framework for feature selection that promises not only superior performance but also offers a flexible architecture that can be adapted for a variety of machine learning challenges.

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“…To solve this problem, Zhang et al [10] proposed a mutual guidance attention module, which highlights key task-based information and suppresses useless noise information by establishing mutual supervision between different modal information flows. In order to more fully mine the consistency information among multimodal data, Song et al [11] developed a cross-modal attention fusion module, which learns the global dependency relationship between multimodal data by establishing deep interaction between them, and then uses this dependency relationship to improve the model's ability to capture task-related information. However, optimization of attention mechanisms in the above methods often relies on establishing statistical associations between multimodal data and labels, thus minimizing the empirical risk of the model on the training data.…”

Section: Of 22mentioning

confidence: 99%

Causal Meta-Reinforcement Learning for Multimodal Remote Sensing Data Classification

Zhang,

Wang,

Wang

et al. 2024

Preprint

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Multimodal remote sensing data classification can enhance the model’s ability to distinguish land features through multimodal data fusion. In this context, how to help models understand the relationship between multimodal data and target tasks is the focus of researchers. Inspired by human feedback learning mechanisms, causal reasoning mechanisms, and knowledge induction mechanisms, this paper integrates causal learning, reinforcement learning, and meta learning into a unified remote sensing data classification framework and proposed the causal meta-reinforcement learning (CMRL). First, based on feedback learning mechanisms, we have overcome the limitations of traditional implicit optimization of fusion features and customized a reinforcement learning environment for multimodal remote sensing data classification tasks. Through feedback interactive learning between agents and the environment, we help them understand the complex relationships between multimodal data and labels, thereby achieving full mining of multimodal complementary information. Second, based on the causal inference mechanism we designed causal distribution prediction action, classification reward, and causal intervention reward, capturing pure causal factors in multimodal data and cutting off false statistical associations between non-causal factors and class labels. Finally, based on the knowledge induction mechanism, we designed a bi-layer optimization mechanism based on meta-learning. By constructing a meta training task and meta validation task simulation model in the generalization scenario of unseen data, we helped the model induce cross-task shared knowledge, thereby improving its generalization ability for unseen multimodal data. The experimental results on multiple sets of multimodal datasets show that the proposed method achieved state-of-the-art performance in multimodal remote sensing data classification tasks.

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Hierarchical Feature Fusion and Selection for Hyperspectral Image Classification

Cited by 11 publications

References 14 publications

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Causal Meta-Reinforcement Learning for Multimodal Remote Sensing Data Classification

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