Article
Computer Science, Information Systems
Ansheng Ye, Xiangbing Zhou, Fang Miao
Summary: This study proposes an innovative classification method IPCEHRIC that combines the advantages of enhanced PSO algorithm, CNN, and ELM to effectively extract features and improve classification accuracy for HRSIs. Experimental results show that IPCEHRIC demonstrates stronger generalization, faster learning ability, and higher accuracy in classifying HRSIs.
Article
Computer Science, Artificial Intelligence
Qurrat Ul Ain, Harith Al-Sahaf, Bing Xue, Mengjie Zhang
Summary: This study analyzes GP-based approaches to skin image classification, which improve the performance of machine learning classification algorithms by constructing features, thereby enhancing diagnostic efficiency and assisting dermatologists in diagnosis.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Dalal Al-Alimi, Zhihua Cai, Mohammed A. A. Al-Qaness, Abdelghani Dahou, Eman Ahmed Alawamy, Sakinatu Issaka
Summary: In hyperspectral image processing, dimensionality reduction methods are crucial for reducing complexity and improving classification accuracy. The newly introduced Compression and Reinforced Variation (CRV) method shows promising results in reducing data dimension while improving classification accuracy.
APPLIED SOFT COMPUTING
(2022)
Article
Chemistry, Analytical
Nanlan Wang, Xiaoyong Zeng, Yanjun Duan, Bin Deng, Yan Mo, Zhuojun Xie, Puhong Duan
Summary: A novel multi-scale superpixel-guided structural profile method is proposed for hyperspectral image classification, which can produce outstanding classification effects in the case of limited samples compared to other advanced methods.
Article
Computer Science, Artificial Intelligence
Meenakshi Garg, Gaurav Dhiman
Summary: This paper introduces a content-based image retrieval technique which focuses on extraction and reduction in multiple features. Utilizing methods such as discrete wavelet transformation and local binary patterns, it achieves better results in texture image classification.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Lianxi Wang, Shengyi Jiang, Siyu Jiang
Summary: The study introduces a novel feature selection algorithm that selects relevant and interactive features using a maximum criterion, leading to improved classification accuracy. Experimental results show that the algorithm efficiently selects features and enhances classifiers to achieve better or comparable classification accuracy compared to ten representative competing feature selection algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Jinxin Zhang, Wei Li, Weidong Sun, Yuxiang Zhang, Ran Tao
Summary: Domain adaptation is a widely used technique for cross-domain hyperspectral image classification. However, some methods neglect the local manifold structure and the negative influences of abnormal features. To address these problems, we propose a Locality Robust Domain Adaptation (LRDA) method, which reduces domain discrepancy through statistical alignment and learns a robust projection matrix using row-sparsity constraint and discriminant regularization. Additionally, a manifold regularization term is introduced to explore local neighbor information between domains.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Engineering, Multidisciplinary
M. Shaheen, N. Naheed, A. Ahsan
Summary: Big data analytics uncovers hidden patterns through classification, prediction and reinforcement of big datasets. Relevant, important and informative features are selected using different filtration techniques. A new feature selection technique called Relevance-diversity algorithm and a new supervised classification algorithm based on Naive Bayes classification are proposed. The performance of these techniques is evaluated using various datasets, and the results show improvements in terms of feature selection, accuracy, and time complexity.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Geochemistry & Geophysics
Zhixi Feng, Xuehu Liu, Shuyuan Yang, Kai Zhang, Licheng Jiao
Summary: Most existing classification methods for hyperspectral images (HSIs) rely on complicated and large deep neural network (DNN) models, which suffer from limited training samples and high computational costs in real scenarios. To address these issues, we propose a simple spectral hierarchical feature fusion and selection network (HFFSNet) that utilizes 1-D grouped convolution for dimensionality reduction and multilevel feature extraction. The multilevel features are fused using the soft attention mechanism to assist adaptive feature selection, and the selected features are further fused to enhance the overall feature representation. Extensive experimental results on three hyperspectral datasets demonstrate the effectiveness of our proposed network.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Computer Science, Information Systems
Xinxin Wang, Zhenyu Wang, Yongshan Zhang, Xinwei Jiang, Zhihua Cal
Summary: Feature selection is crucial in hyperspectral image analysis to reduce noise, irrelevant and redundant information, and autoencoder can learn latent representations to aid in feature selection.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Automation & Control Systems
Yunfei Zhang, Yuelong Zhu, Hexuan Hu, Hongyan Wang
Summary: A novel specific two-dimensional-three-dimensional fusion strategy is proposed in this paper, using a spatial-spectral feature fusion network based on two-dimensional convolution and three-dimensional convolution to extract rich features while keeping spatial and spectral information intact. Experimental results show that the proposed method outperforms existing methods in cases of small training sets.
INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION
(2021)
Article
Environmental Sciences
Giorgio Morales, John W. Sheppard, Riley D. Logan, Joseph A. Shaw
Summary: The study introduces a dimensionality reduction technique combining filter-based and wrapper-based methods to select useful bands by analyzing collinearity and information entropy among spectral bands, aiming to reduce redundancy and improve processing efficiency of hyperspectral images. Experimental results demonstrate that the proposed method achieves good performance in handling hyperspectral images.
Article
Geochemistry & Geophysics
Xinyu Zhang, Yantao Wei, Weijia Cao, Huang Yao, Jiangtao Peng, Yicong Zhou
Summary: The article proposes a spatial-spectral feature representation method based on local correntropy matrix for hyperspectral image (HSI) classification. By performing dimension reduction and constructing local correntropy matrix, the proposed method achieves competitive performance in HSI classification.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Dalal AL-Alimi, Mohammed A. Al-qaness, Zhihua Cai, Eman Ahmed Alawamy
Summary: This study introduces a novel feature reduction method called improving distribution analysis (IDA) to enhance data distribution, reduce complexity, and accelerate performance in hyperspectral images (HSIs). The experimental results demonstrate that IDA performs admirably in achieving these goals.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Bing Liu, Yifan Sun, Anzhu Yu, Zhixiang Xue, Xibing Zuo
Summary: This study introduces an optical flow technique to track the variation of the spectrum in hyperspectral images, and extracts more distinguishable spectral flow features. By combining the extracted spectral flow with the original spectral features, the classification accuracy using a support vector machine classifier is higher than traditional methods and the latest deep-learning-based methods.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
