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Computer Science, Information Systems
Sandeep Kumar Ladi et al.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Shaoguang Huang et al.
Summary: This article proposes a tensor-based subspace clustering model for hyperspectral band selection, aiming to address the issues of existing methods by encoding the multimode correlations of HSI through Tucker decomposition. Additionally, well-motivated heterogeneous regularizations are applied to factor matrices, taking into account the important local and global properties of HSI along three dimensions. Instead of learning correlations in the original domain, the model naturally learns band correlations in a low-dimensional latent feature space, leading to a computationally efficient and unified framework.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Changda Xing et al.
Summary: This article presents a novel deep network, DIKS, for the classification of hyperspectral images. By using irregular convolutional kernels and self-expressive property, the network can adaptively compute feature maps to describe the characteristics of different object classes. The introduced self-expression theory helps produce more discriminative features through clustering. Experimental results show that this method outperforms state-of-the-art algorithms in classification performance.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Geography, Physical
Rashedul Islam et al.
Summary: This study proposes a band grouping technique called BgMNF, which utilizes Normalized Mutual Information (NMI) for feature extraction from hyperspectral images (HSI). The technique combines kernel Support Vector Machine (SVM) for feature selection and analysis. Experimental results demonstrate significant improvement in classification accuracy and computational cost compared to existing methods.
JOURNAL OF SPATIAL SCIENCE
(2022)
Article
Remote Sensing
Menna M. Elkholy et al.
Summary: This paper proposes a new unsupervised band selection approach that leverages deep learning frameworks, with two consecutive phases to select the optimal band subset. Experimental results show that the proposed approach outperforms several state-of-the-art counterparts in terms of accuracy.
INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION
(2022)
Article
Environmental Sciences
Shahram Sharifi Hashjin et al.
Summary: In this study, a new method is proposed to improve the performance of target detection algorithms based on principal component analysis (PCA) feature space. Experimental results demonstrate that this method achieves better performance in terms of false alarm rate compared to other methods.
GEOCARTO INTERNATIONAL
(2022)
Article
Environmental Sciences
Arati Paul et al.
Summary: Hyperspectral image contains redundant information, and existing methods for dimensionality reduction rely on user input. This research proposes a supervised data-driven method that automatically selects the required number of bands. Experimental results show improved classification accuracy compared to other state-of-the-art methods.
GEOCARTO INTERNATIONAL
(2022)
Article
Geochemistry & Geophysics
Reza Aghaee et al.
Summary: This article proposes an improved approach using a Levy flight-based version of the genetic algorithm (GA) to overcome the challenges of selecting appropriate bands in hyperspectral remote sensing images. The method increases the number of training samples based on spatial adjacency and spectral similarity, resulting in improved classification accuracies. The proposed semisupervised method shows significant accuracy improvements, even with a small number of training samples.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Soronzonbold Otgonbaatar et al.
Summary: This study explores programming and evaluating a parameterized quantum circuit (PQC) for classifying Earth observation (EO) satellite images, comparing it with a classic deep learning classifier. By reducing the dimensionality of input images, a PQC exhibits high accuracy in classifying images, providing insights for programming future gate-based quantum computers for practical problems in EO.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Akrem Sellami et al.
Summary: The study introduces a novel methodology for hyperspectral image classification using multi-view deep neural networks that combines spectral and spatial features to enhance classification performance with limited labeled samples.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Information Systems
Xinxin Wang et al.
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)
Review
Chemistry, Analytical
Anton Terentev et al.
Summary: This review presents the modern advances in early plant disease detection using hyperspectral remote sensing. It identifies the current gaps in experimental methodologies, suggests further directions for methodological development, and provides a systematic table of disease detection results for different plants using hyperspectral remote sensing.
Article
Computer Science, Artificial Intelligence
Arati Paul et al.
Summary: This paper proposes an unsupervised dimensionality reduction method based on particle swarm optimization, which uses spectral divergence and spatial gradient information for band selection. With the application of a noise filter and clustering, the computation performance and classification accuracy are improved.
Article
Multidisciplinary Sciences
Qu Shenming et al.
Summary: This paper proposes a new method for hyperspectral image classification, which combines two-dimensional Gabor filter with random patch convolution feature extraction to enhance classification accuracy. Experimental results demonstrate the superiority of this method over other comparison methods on widely used datasets.
SCIENTIFIC REPORTS
(2022)
Article
Environmental Sciences
Dorijan Radocaj et al.
Summary: This research aims to integrate agronomic and spatial components in precision fertilization by applying both conventional and modern prediction methods. Through evaluating specific conventional and modern machine learning methods, as well as providing an overview of remote sensing methods, it confirms the superior prediction accuracy and local heterogeneity of the modern approach in precision fertilization.
Article
Computer Science, Artificial Intelligence
Jun Wang et al.
Summary: A novel band selection method based on region-aware latent features fusion was proposed, which utilized superpixel segmentation and Laplacian matrix construction to preserve spatial information and enhance separability among bands in hyperspectral images. The method achieved superior performance in comparison with other state-of-the-art methods through k-means clustering algorithm to obtain the index of selected bands.
INFORMATION FUSION
(2022)
Article
Food Science & Technology
Huihui Wang et al.
Summary: This study demonstrated that the hyperspectral imaging technique can nondestructively and accurately detect the carp damage area during the descaling process. The decision tree model was determined to be the optimal damage recognition model. The combination of principal component analysis and pixel values statistical analysis was used to reduce the dimension of hyperspectral images, and the resulting visualization maps provided efficient and precise damage-area recognition.
JOURNAL OF FOOD SCIENCE
(2022)
Article
Geography, Physical
Ayasha Siddiqa et al.
Summary: This paper introduces a dimensionality reduction method for hyperspectral images (HSI) using feature extraction and feature selection. It proposes a segmented MNF based on nCCRE and employs an nCCRE-based feature selection method. The efficiency of the extracted subsets is evaluated using support vector machines (SVM) classifier.
JOURNAL OF SPATIAL SCIENCE
(2022)
Article
Computer Science, Information Systems
Li Li et al.
Summary: Hyperspectral images are characterized by multi band, high spatial resolution, and information redundancy, which are mainly caused by high-dimensional data. Feature extraction is a research hotspot in this field, as spectral information alone cannot fully describe the intrinsic geometric structure of hyperspectral images. Therefore, spatial information needs to be mined. In this study, an effective feature extraction method (SSF_HM) is proposed using the harmonic mean and spectral-spatial filter. The SSF_HM method combines spectral and spatial information, expanding the range of feature extraction from spectral space to spectral-spatial fusion space. Experimental results on real-world hyperspectral image data sets show the better performance of SSF_HM compared to other feature extraction methods in small sample size situations using the maximum likelihood classifier (MLC).
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Review
Environmental Sciences
Huiwei Jiang et al.
Summary: This paper provides a review of the latest progress and challenges in deep learning-based change detection algorithms using high-resolution remote sensing images, and suggests promising directions for future research.
Article
Environmental Sciences
Yang-Lang Chang et al.
Summary: The performance of hyperspectral image classification is influenced by spatial and spectral information, but affected by factors like data redundancy and insufficient spatial resolution. While 3D-CNN-based methods perform well, they come with high computational complexity. To address these issues, this study proposes a consolidated C-CNN method that combines 3D-CNN and 2D-CNN. PCA is used to reduce spectral redundancy, and image augmentation techniques are employed to increase training samples and prevent overfitting. The proposed C-CNN model, named C-CNN-Aug, achieves optimal trade-off between accuracy and computational time compared to other methods.
Article
Environmental Sciences
Nannan Liang et al.
Summary: This paper proposes a multi-view structural feature extraction method that considers the correlation and dependencies of different regions to provide a complete characterization of the spectral-spatial structures of objects. Experimental results show that this method outperforms other state-of-the-art classification methods in terms of visual performance and objective results, especially with limited training set.
Article
Ecology
Jeannine Cavender-Bares et al.
Summary: This Perspective discusses the importance of integrating remote sensing with field-based ecology and evolution to fully understand and preserve Earth's biodiversity. The inclusive integration of data collected through different methods can benefit conservation efforts and advance biodiversity science.
NATURE ECOLOGY & EVOLUTION
(2022)
Article
Computer Science, Artificial Intelligence
Chunlin He et al.
Summary: In this article, an unsupervised multitask artificial bee colony (ABC) BS algorithm based on variable-size clustering (MBBS-VC) is proposed to simultaneously obtain multiple optimal band subsets with different sizes. Several new strategies are designed to improve the algorithm's performance, and experimental results verify the superiority of the proposed BS algorithm.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
He Sun et al.
Summary: Due to its strong feature representation ability, the deep learning-based method is preferred for the unsupervised band selection task of hyperspectral image. However, the current methods have not investigated the nonlinear relationship between spectral bands, calling for a more robust model and effective loss function. In this paper, a novel stochastic gate-based autoencoder is proposed, which directly obtains the desired band subset with learnable parameters. The inclusion of a nonlinear regularization term and an early stopping criteria further improve the performance of the method.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Hardware & Architecture
Songlin Jin et al.
Summary: By proposing a spatial-spectral feature extraction method to identify seeds, our research demonstrates that this method can classify hyperspectral images quickly, accurately, and nondestructively, achieving higher classification accuracy compared to other methods.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Remote Sensing
Shrutika S. Sawant et al.
Summary: This article introduces a novel unsupervised band selection approach called MOMVOBS, which uses a multi-objective multi-verse optimizer to optimize the information richness, redundancy, and number of selected bands simultaneously. The experimental results demonstrate that the proposed approach outperforms other methods in selecting highly informative bands and achieves higher classification accuracy with a small number of retained bands.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2022)
Article
Environmental Sciences
Yiqun Shang et al.
Summary: The study aims to propose a new filter-wrapper (F-W) framework to optimize the support vector machine (SVM) with ten Swarm Intelligence and Evolutionary Algorithms (SIEAs) in hyperspectral image (HSI) classification, showing significant performance.
Article
Environmental Sciences
Shuzhu Shi et al.
Summary: This study presents a novel model for assessing food security in Egypt using remote sensing techniques. By extracting image features, calculating net primary production, and predicting grain yield, food security in Egypt is evaluated based on multiple dimensions. The results show that food security in Egypt is declining.
Article
Multidisciplinary Sciences
Zhiwei Ye et al.
Summary: This study proposes a modified Hybrid Rice Optimization (MHRO) method based on opposition-based-learning and differential evolution operators for band selection in hyperspectral image (HSI) analysis. Experimental results show that the proposed method outperforms other algorithms in terms of classification accuracy and selected number of bands.
Proceedings Paper
Computer Science, Artificial Intelligence
Mete Ahishali et al.
Summary: A novel framework for band selection in hyperspectral image data processing is proposed, utilizing the Self-Representation Learning with Sparse 1D-Operational Autoencoder approach. The proposed method outperforms competing methods in terms of land cover classification accuracies on Indian Pines and Salinas-A datasets.
2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP
(2022)
Proceedings Paper
Acoustics
Yongshan Zhang et al.
Summary: This paper proposes a graph learning based autoencoder (GLAE) method to achieve unsupervised hyperspectral band selection. GLAE constructs the initial graph using the relationships among pair-wise pixels within HSIs and adjusts it to adapt the band selection process. Experiments show that GLAE achieves better results on three HSI datasets compared to existing methods.
2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
(2022)
Article
Geochemistry & Geophysics
Hang Fu et al.
Summary: This study proposes a novel dimensionality reduction method that integrates band selection and spatial noise reduction to extract low-dimensional spectral-spatial features of hyperspectral images (HSI). The method uses a neighborhood grouping normalized matched filter for band selection and an enhanced 2-D singular spectrum analysis method for spatial context extraction. Experimental results demonstrate that the proposed method outperforms state-of-the-art dimensionality reduction methods in HSI classification.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Imaging Science & Photographic Technology
Junzhe Zhang
Summary: This paper introduces a new hybrid changing-weight classification method with a filter feature selection (HCW-SSC) that improves the classification accuracy of hyperspectral images by selecting representative features and computing appropriate similarity measures weights.
JOURNAL OF IMAGING
(2022)
Article
Geochemistry & Geophysics
Yufei Liu et al.
Summary: Band selection (BS) is an effective method to address the issues of spectral redundancy and Hughes phenomenon in hyperspectral images (HSIs). However, existing BS methods lack consideration of the representativeness, redundancy, and information content of selected bands simultaneously, and do not account for the nonlinear relationship between bands. In this letter, a novel unsupervised BS framework called RRI is proposed, which comprehensively considers the band representativeness, redundancy, and information content. The proposed method utilizes a convolutional autoencoder to estimate band representativeness by capturing the inherent nonlinear relationship between bands and leveraging the spatial information of the HSI. Experimental results demonstrate that the RRI method outperforms competitors in terms of classification accuracy and is robust to noisy bands.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Imaging Science & Photographic Technology
Raju Anand et al.
Summary: In this research, a new metaheuristic algorithm called Moth-Flame Optimization (MFO) is studied for hyperspectral band selection. The research found that MFO outperformed other state-of-the-art band selection algorithms in terms of classification accuracy on three benchmark hyperspectral datasets.
JOURNAL OF IMAGING
(2022)
Article
Geochemistry & Geophysics
Xianfeng Ou et al.
Summary: This paper proposes a CNN framework for hyperspectral image change detection, which involves slow-fast band selection and feature fusion grouping to improve detection accuracy.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Diganta Kumar Pathak et al.
Summary: Recently, algorithms based on spectral-spatial information have gained attention for their robustness, accuracy, and efficiency. This paper proposes an SVM-based classification method that utilizes both spectral and spatial information to encode pixel data, outperforming other classification algorithms such as K nearest neighbors, linear discriminant analysis, Naive Bayes, and decision tree.
EVOLUTIONARY INTELLIGENCE
(2022)
Article
Environmental Sciences
Divyesh Varade et al.
Summary: Information on snow cover distribution is important in hydrological processes and climate models. Hyperspectral remote sensing provides opportunities in land cover assessment, but is limited in snow-covered alpine regions due to large dimensionality. A band selection technique based on mutual information is proposed to improve efficiency and accuracy in selecting informative bands.
GEOCARTO INTERNATIONAL
(2021)
Article
Computer Science, Information Systems
Shrutika Sawant et al.
Summary: This paper proposes a new hybrid global optimization algorithm based on Wind Driven Optimization (WDO) and Cuckoo Search (CS) to solve hyperspectral band selection problems. By dividing the population into two subgroups and utilizing the strengths of WDO and CS independently, the algorithm avoids premature convergence and achieves the best optimal solution.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Geochemistry & Geophysics
Sen Jia et al.
Summary: This study proposes a framework, FG-SuULDA, for extracting informative and discriminating features from hyperspectral images for material classification. By designing flexible Gabor filters and introducing the SuULDA method, the classification capability and peculiarity of features are enhanced. Experimental results demonstrate the superiority of the proposed method in both classification performance and computational efficiency.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Agricultural Engineering
Domenico Cautela et al.
Summary: The study found that the depletion of olfactory qualities in bergamot essential oil is closely related to the combination of heat waves and droughts, posing a threat to the bergamot industry.
INDUSTRIAL CROPS AND PRODUCTS
(2021)
Article
Instruments & Instrumentation
Qiyou Jiang et al.
Summary: Anthracnose and gray mold, two devastating diseases of strawberries, can cause large-scale yield losses globally. Early identification of these diseases is challenging but crucial for managing strawberry production. This study developed machine learning-aided methods based on spectral fingerprint features to achieve early detection of anthracnose and gray mold in strawberries.
INFRARED PHYSICS & TECHNOLOGY
(2021)
Article
Computer Science, Information Systems
Ali Can Karaca et al.
Summary: The study introduces a novel GAN-based method for compressing multitemporal MS images, which achieves efficiency by learning to transform the reference image to the target image.
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
(2021)
Article
Optics
Muhammad Ahmad et al.
Summary: The study introduces a compact hybrid CNN model that improves hyperspectral image classification performance by extracting spatial-spectral features between 3D and 2D layers. Intensive preprocessing has been carried out to achieve better classification results and reduce computational time.
Article
Environmental Sciences
Anca Dabija et al.
Summary: Land cover information is crucial in European Union spatial management, with the development of the new version CLC+ in progress. Various methods and algorithms are being tested in Catalonia, Poland, and Romania to provide insights and guidance for development.
Article
Computer Science, Artificial Intelligence
Zhang Yong et al.
Summary: A multi-objective artificial bee colony approach is proposed for band selection problem in hyperspectral images, which uses a new multi-objective unsupervised band selection model and several new operators to enhance algorithm performance. Experimental results indicate that the proposed algorithm performs well in addressing hyperspectral band selection problem.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Construction & Building Technology
Yuan Zhou et al.
Summary: The study analyzed the impact of urbanization on land use carbon emissions in the Beijing-Tianjin-Hebei urban agglomeration in China. It found that while there was a general increase in built-up land and city-level carbon emissions, Beijing showed a decline in emissions despite significant urban expansion. The relationship between urbanization and LUCEs can be categorized into three modes: “high urbanization low emissions”, “middle urbanization high emissions”, and “low urbanization low emissions”.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Article
Construction & Building Technology
Bijay Halder et al.
Summary: Urban heat island effect can be triggered by climate change and rapid urbanization, leading to higher temperatures in urban areas compared to rural areas. The study in Kolkata municipality explored land use changes, land surface temperature analysis, and correlations between various factors like LST, LULC, NDVI, and NDBI. Proper planning is needed to address future urban expansion and environmental degradation for better livelihoods and protection of the environment.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Article
Remote Sensing
Boggavarapu L. N. Phaneendra Kumar et al.
Summary: A band selection method based on whale optimization was proposed in the study, retrieving informative bands and extracting spatial features through a search mechanism similar to hunting behavior of humpback whales to achieve high-quality classification maps. The overall accuracy of the method on three benchmark datasets is very high, outperforming other methods in effectiveness.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2021)
Article
Environmental Sciences
Mukesh Singh Boori et al.
Summary: The article introduces a technique for ecological vulnerability analysis based on remote sensing data and PCA method, analyzing the changes of four land surface parameters in the Samara region of Russia to reveal the trends in ecological conditions from 2010 to 2020. It suggests that this technology could be utilized for ecological conditions mapping, monitoring, decision-making, and management.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2021)
Article
Microbiology
Janie Zhang et al.
Summary: The rhizosphere microbiome consists of various microorganisms that interact with crop plants, potentially influencing plant growth. Studying the rhizosphere microbiome has the potential to increase crop yield and quality to meet the growing demand for food.
MICROBIOLOGICAL RESEARCH
(2021)
Article
Geochemistry & Geophysics
Qi Wang et al.
Summary: Hyperspectral images provide rich information but also come with increased data complexity and redundancy among adjacent bands. Current band selection methods focus more on the number of selected bands and less on context information from the whole spectral bands. A new fast neighborhood grouping method is proposed to select relevant and informative bands based on local density and information entropy, achieving satisfactory performance compared to state-of-the-art algorithms.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Computer Science, Hardware & Architecture
R. Tamilarasi et al.
Summary: Hyperspectral imagery is useful for determining urban-related characteristics such as roads, trees, buildings, and structures. Researchers are currently focusing on deep learning and machine learning methods for image classification. This research proposes a new technique for dimensionality reduction and classification, combining ICA, PCA, FCN, and SVM models, to extract road and building features with high accuracy from hyperspectral images. Experimental results show better accuracy compared to existing machine learning approaches.
JOURNAL OF SUPERCOMPUTING
(2021)
Article
Environmental Sciences
Clara Cruz-Ramos et al.
Summary: The proposed method reduces data dimension by extracting Gabor texture features and utilizing LDA, followed by classification using ANN to build a data matrix for analysis. It achieves high classification rates but with longer training time compared to non-reduced features.
Proceedings Paper
Computer Science, Artificial Intelligence
Zhiwei Ye et al.
Summary: This paper proposes a band selection method for hyperspectral image based on binary coded hybrid rice optimization algorithm, solving the band selection problem by defining an objective function. Experimental results demonstrate that the proposed method achieves satisfactory results in terms of performance and execution time.
PROCEEDINGS OF THE THE 11TH IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT DATA ACQUISITION AND ADVANCED COMPUTING SYSTEMS: TECHNOLOGY AND APPLICATIONS (IDAACS'2021), VOL 1
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Engineering, Electrical & Electronic
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Summary: This article introduces a method called folded Linear Discriminant Analysis (F-LDA) for dimensionality reduction of remotely sensed HSI data in small sample size scenarios. The F-LDA allows selecting more discriminant features and significantly improves accuracy in pixel classification, outperforming conventional LDA in terms of classification accuracy, computational complexity, and memory requirements.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Computer Science, Artificial Intelligence
Arati Paul et al.
Summary: Band selection in hyperspectral imagery is crucial for dimensionality reduction and improving processing efficiency. A genetic algorithm based method was proposed in this paper, using a weighted combination of signal entropy and image spatial information. Spatial dimension was reduced using discrete wavelet transformation to enhance time efficiency without compromising output quality. The method was evaluated by classifying hyperspectral images and comparing with other state-of-the-art methods, showing its efficiency in selecting information-rich bands.
EVOLUTIONARY INTELLIGENCE
(2021)
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NEURAL COMPUTING & APPLICATIONS
(2020)
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SIGNAL IMAGE AND VIDEO PROCESSING
(2020)
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Beibei Jia et al.
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(2020)
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Geochemistry & Geophysics
Guangzhe Zhao et al.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
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INTERNATIONAL JOURNAL OF REMOTE SENSING
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