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Journal articles on the topic 'Indian Pines'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Karadaş, Cağfer. "Kelam Atomculuğunun Kaynağı Sorunu." Marife 2, no. 2 (2002): 81–100. https://doi.org/10.5281/zenodo.3344693.

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<b>Kalâm Atomism</b>Kalâm atomism is one of the central subjects in the Mutakallimûn's doctrine of universe. The origin of this subject still remains to be explored. Moses b. Maymonides claims that kalâm atomism was affected by Greek atomism. This claim also has been taken into account by various orientalists. In the nineteenth Century, Schmölders and Mabilleu claimed that kalam atomism was affected by Indian atomism. Shlomo Pines in his Beitrage zur 6slamischen Atomenlehre has acknowledged a resemblance between the certain aspects of Greek and Kalâm atomism. Pines thought that the difference

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Goswami, Tilottama, Kandi Navya Shruthi, Sindhu Chokkarapu, Raghavendra Kune, and Mukesh Kumar Tripathi. "SWT-PCA-CNN: hyperspectral image classification with multi-stage feature extraction and parameter tuning." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 1 (2024): 59. http://dx.doi.org/10.11591/ijeecs.v34.i1.pp59-68.

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Hyperspectral imaging is an increasingly popular technique in remote sensing, offering a wealth of spectral information for a range of applications. This paper presents a comparative study of hyperspectral image classification techniques using three different datasets: Indian Pines, Salinas, and Pavia University. The study employs a combination of three methods, namely stationary wavelet transforms (SWT), principal component analysis (PCA), and convolutional neural network (CNN), to develop a model for hyperspectral image classification. The proposed approach combines SWT and PCA for spatial f

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Goswami, Tilottama, Kandi Navya Shruthi, Sindhu Chokkarapu, Raghavendra Kune, and Mukesh Kumar Tripathi. "SWT-PCA-CNN: hyperspectral image classification with multi-stage feature extraction and parameter tuning." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 1 (2024): 59–68. https://doi.org/10.11591/ijeecs.v34.i1.pp59-68.

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Hyperspectral imaging is an increasingly popular technique in remote sensing, offering a wealth of spectral information for a range of applications. This paper presents a comparative study of hyperspectral image classification techniques using three different datasets: Indian Pines, Salinas, and Pavia University. The study employs a combination of three methods, namely stationary wavelet transforms (SWT), principal component analysis (PCA), and convolutional neural network (CNN), to develop a model for hyperspectral image classification. The proposed approach combines SWT and PCA for spatial f

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Jamali, A., M. Mahdianpari, and A. Abdul Rahman. "HYPERSPECTRAL IMAGE CLASSIFICATION USING MULTI-LAYER PERCEPTRON MIXER (MLP-MIXER)." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W6-2022 (February 7, 2023): 179–82. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w6-2022-179-2023.

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Abstract. The classifying of hyperspectral images (HSI) is a difficult task given the high dimensionality of the space, the huge number of spectral bands, and the small number of labeled data. As such, we offer a unique hyperspectral image classification methodology to address these issues based on sophisticated Multi-Layer Perceptron (MLP) algorithms. In this paper, we propose using MLP-Mixer to classify HSI data in three data benchmarks of Pavia, Salinas, and Indian Pines. Based on the results, the proposed MLP-Mixer achieved a high level of classification accuracy and produced noise-free an

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Wu, Jee Cheng, Bo Kai Lin, and Gwo Chyang Tsuei. "Comparison of Processing Chains Based on Support Vector Machine Classifier for Hyperspectral Image Classification." Advanced Materials Research 433-440 (January 2012): 646–49. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.646.

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In this research, we compared the performance of different processing chains resulting from combinations of spatial denoising filters, unsupervised feature transformation methods, and a support vector machine classifier. Two different training and test sample scenarios were investigated and conducted on an AVIRIS image over the Indian Pines region in Indiana, USA. The results showed that by using the process chain (adaptive enhanced Lee filter, maximum noise fraction, and support vector machine) the classification accuracies of the kappa coefficient were better than those of the best previousl

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Tan, Yulei, Jingtao Gu, Laijun Lu, et al. "Hyperspectral Band Selection for Crop Identification and Mapping of Agriculture." Remote Sensing 17, no. 4 (2025): 663. https://doi.org/10.3390/rs17040663.

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Different crops, as well as the same crop at different growth stages, display distinct spectral and spatial characteristics in hyperspectral images (HSIs) due to variations in their chemical composition and structural features. However, the narrow bandwidth and closely spaced spectral channels of HSIs result in significant data redundancy, posing challenges to crop identification and classification. Therefore, the dimensionality reduction in HSIs is crucial. Band selection as a widely used method for reducing dimensionality has been extensively applied in research on crop identification and ma

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Meenu. "Contribution of Indian English Poets towards Indian Freedom Movement." Research Review Journal of Social Science 3, no. 02 (2023): 38–41. http://dx.doi.org/10.31305/rrjss.2023.v03.n02.006.

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The idea of independence has always captivated the minds of the poets. Poets all around the world have always supported the cause of freedom of different countries. They have even served in wars for the same purpose. English Romantic poets were inspired by the French Revolution. There were war poets who served in the world wars and filled the hearts of people with patriotism. Some fought for the liberation of Greece and Spain. India had also been colonized by the Britishers for over a century. Freedom for India was brought about by the supreme sacrifice of many brave souls. The contribution of

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Sinha, Bhaskar. "Pines in the Himalayas: Past, Present and Future Scenario." Energy & Environment 13, no. 6 (2002): 873–81. http://dx.doi.org/10.1260/095830502762231322.

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The Himalayas have a great environmental influence in the Indian subcontinent. But Himalayan ecosystems with their rich biodiversity are particularly fragile. In the recent past, India has been serially affected by several natural calamities like droughts, floods and even earthquakes. Scientists all over the world have warned of various contributing factors. But, the major cause has been attributed to one important matrix – the constant degeneration of the Himalayan ecology. Human activities have resulted in arrested succession, leading to exhaustion of the germplasm of larger shrubs and trees

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Munishamaiaha, Kavitha, Gayathri Rajagopal, Dhilip Kumar Venkatesan, et al. "Robust Spatial–Spectral Squeeze–Excitation AdaBound Dense Network (SE-AB-Densenet) for Hyperspectral Image Classification." Sensors 22, no. 9 (2022): 3229. http://dx.doi.org/10.3390/s22093229.

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Increasing importance in the field of artificial intelligence has led to huge progress in remote sensing. Deep learning approaches have made tremendous progress in hyperspectral image (HSI) classification. However, the complexity in classifying the HSI data using a common convolutional neural network is still a challenge. Further, the network architecture becomes more complex when different spatial–spectral feature information is extracted. Usually, CNN has a large number of trainable parameters, which increases the computational complexity of HSI data. In this paper, an optimized squeeze–exci

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Mohan, Divya, Aravinth J, and Sankaran Rajendran. "Hyperspectral Image Denoising and Compression Using Optimized Bidirectional Gated Recurrent Unit." Remote Sensing 16, no. 17 (2024): 3258. http://dx.doi.org/10.3390/rs16173258.

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The availability of a higher resolution fine spectral bandwidth in hyperspectral images (HSI) makes it easier to identify objects of interest in them. The inclusion of noise into the resulting collection of images is a limitation of HSI and has an adverse effect on post-processing and data interpretation. Denoising HSI data is thus necessary for the effective execution of post-processing activities like image categorization and spectral unmixing. Most of the existing models cannot handle many forms of noise simultaneously. When it comes to compression, available compression models face the pro

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Firozjaei, Mohammad, Iman Daryaei, Amir Sedighi, Qihao Weng, and Seyed Alavipanah. "Homogeneity Distance Classification Algorithm (HDCA): A Novel Algorithm for Satellite Image Classification." Remote Sensing 11, no. 5 (2019): 546. http://dx.doi.org/10.3390/rs11050546.

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Image classification is one of the most common methods of information extraction from satellite images. In this paper, a novel algorithm for image classification based on gravity theory was developed, which was called “homogeneity distance classification algorithm (HDCA)”. The proposed HDCA used texture and spectral information for classifying images in two iterative supplementary computing stages: (1) merging, (2) traveling and escaping operators. The HDCA was equipped by a new concept of distance, the weighted Manhattan distance (WMD). Moreover, an improved gravitational search algorithm (IG

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Lin, ZX, YH Jiang, CM Wu, JG Wang, and S. Yan. "An Accurate Subspace Clustering Model for Unsupervised Noise-laden Hyperspectral Image Segmentation." Journal of Physics: Conference Series 2476, no. 1 (2023): 012023. http://dx.doi.org/10.1088/1742-6596/2476/1/012023.

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Abstract The hyperspectral image acquisition in realistic situations is accompanied by a lot of noise, which dramatically interferes with the discriminative power of the clustering model. In this paper, a superpixel segmentation 3D regularized subspace clustering model (SS3D-SSC) is provided, which uses a superpixel segmentation method to obtain the most representative pixel in each tiny region and use it as a representative to replace other pixels in the same area, and then use sparse subspace clustering to perform the subsequent image segmentation, the proposed method in this paper is used i

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Kuzmina, Margarita Georgievna. "Multilayered autoencoders in problems of hyperspectral image analysis and processing." Keldysh Institute Preprints, no. 28 (2021): 1–21. http://dx.doi.org/10.20948/prepr-2021-28.

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A model of five-layered autoencoder (stacked autoencoder, SAE) is suggested for deep image features extraction and deriving compressed hyperspectral data set specifying the image. Spectral cost function, dependent on spectral curve forms of hyperspectral image, has been used for the autoencoder tuning. At the first step the autoencoder capabilities will be tested based on using pure spectral information contained in image data. The images from well known and widely used hyperspectral databases (Indian Pines, Pavia University и KSC) are planned to be used for the model testing.

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Shah, Syed Taimoor Hussain, Shahzad Ahmad Qureshi, Aziz ul Rehman, et al. "A Novel Hybrid Learning System Using Modified Breaking Ties Algorithm and Multinomial Logistic Regression for Classification and Segmentation of Hyperspectral Images." Applied Sciences 11, no. 16 (2021): 7614. http://dx.doi.org/10.3390/app11167614.

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A new methodology, the hybrid learning system (HLS), based upon semi-supervised learning is proposed. HLS categorizes hyperspectral images into segmented regions with discriminative features using reduced training size. The technique utilizes the modified breaking ties (MBT) algorithm for active learning and unsupervised learning-based regressors, viz. multinomial logistic regression, for hyperspectral image categorization. The probabilities estimated by multinomial logistic regression for each sample helps towards improved segregation. The high dimensionality leads to a curse of dimensionalit

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Agilandeeswari, Loganathan, Manoharan Prabukumar, Vaddi Radhesyam, Kumar L. N. Boggavarapu Phaneendra, and Alenizi Farhan. "Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images." Applied Sciences 12, no. 3 (2022): 1670. http://dx.doi.org/10.3390/app12031670.

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Hyperspectral imaging (HSI), measuring the reflectance over visible (VIS), near-infrared (NIR), and shortwave infrared wavelengths (SWIR), has empowered the task of classification and can be useful in a variety of application areas like agriculture, even at a minor level. Band selection (BS) refers to the process of selecting the most relevant bands from a hyperspectral image, which is a necessary and important step for classification in HSI. Though numerous successful methods are available for selecting informative bands, reflectance properties are not taken into account, which is crucial for

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Ashraf, Mahmood, Raed Alharthi, Lihui Chen, Muhammad Umer, Shtwai Alsubai, and Ala Abdulmajid Eshmawi. "Attention 3D central difference convolutional dense network for hyperspectral image classification." PLOS ONE 19, no. 4 (2024): e0300013. http://dx.doi.org/10.1371/journal.pone.0300013.

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Hyperspectral Images (HSI) classification is a challenging task due to a large number of spatial-spectral bands of images with high inter-similarity, extra variability classes, and complex region relationships, including overlapping and nested regions. Classification becomes a complex problem in remote sensing images like HSIs. Convolutional Neural Networks (CNNs) have gained popularity in addressing this challenge by focusing on HSI data classification. However, the performance of 2D-CNN methods heavily relies on spatial information, while 3D-CNN methods offer an alternative approach by consi

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Hsieh, Tien-Heng, and Jean-Fu Kiang. "Comparison of CNN Algorithms on Hyperspectral Image Classification in Agricultural Lands." Sensors 20, no. 6 (2020): 1734. http://dx.doi.org/10.3390/s20061734.

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Several versions of convolutional neural network (CNN) were developed to classify hyperspectral images (HSIs) of agricultural lands, including 1D-CNN with pixelwise spectral data, 1D-CNN with selected bands, 1D-CNN with spectral-spatial features and 2D-CNN with principal components. The HSI data of a crop agriculture in Salinas Valley and a mixed vegetation agriculture in Indian Pines were used to compare the performance of these CNN algorithms. The highest overall accuracy on these two cases are 99.8% and 98.1%, respectively, achieved by applying 1D-CNN with augmented input vectors, which con

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Liu, Jinxiang, Tiejun Wang, Andrew Skidmore, Yaqin Sun, Peng Jia, and Kefei Zhang. "Integrated 1D, 2D, and 3D CNNs Enable Robust and Efficient Land Cover Classification from Hyperspectral Imagery." Remote Sensing 15, no. 19 (2023): 4797. http://dx.doi.org/10.3390/rs15194797.

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Convolutional neural networks (CNNs) have recently been demonstrated to be able to substantially improve the land cover classification accuracy of hyperspectral images. Meanwhile, the rapidly developing capacity for satellite and airborne image spectroscopy as well as the enormous archives of spectral data have imposed increasing demands on the computational efficiency of CNNs. Here, we propose a novel CNN framework that integrates one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) CNNs to obtain highly accurate and fast land cover classification from airborne hyperspectral

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Gao, Luyao, Shulin Xiao, Changhong Hu, and Yang Yan. "Hyperspectral Image Classification Based on Fusion of Convolutional Neural Network and Graph Network." Applied Sciences 13, no. 12 (2023): 7143. http://dx.doi.org/10.3390/app13127143.

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Convolutional neural networks (CNNs) have attracted significant attention as a commonly used method for hyperspectral image (HSI) classification in recent years; however, CNNs can only be applied to Euclidean data and have difficulties in dealing with relationships due to their limitations of local feature extraction. Each pixel of a hyperspectral image contains a set of spectral bands that are correlated and interact with each other, and the methods used to process Euclidean data cannot effectively obtain these correlations. In contrast, the graph convolutional network (GCN) can be used in no

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Wolfmayr, Monika. "Parameter Optimization for Low-Rank Matrix Recovery in Hyperspectral Imaging." Applied Sciences 13, no. 16 (2023): 9373. http://dx.doi.org/10.3390/app13169373.

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An approach to parameter optimization for the low-rank matrix recovery method in hyperspectral imaging is discussed. We formulate an optimization problem with respect to the initial parameters of the low-rank matrix recovery method. The performance for different parameter settings is compared in terms of computational times and memory. The results are evaluated by computing the peak signal-to-noise ratio as a quantitative measure. The potential improvement in the performance of the noise reduction method is discussed when optimizing the choice of the initial values. The optimization method is

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Anand, R., S. Veni, and J. Aravinth. "Robust Classification Technique for Hyperspectral Images Based on 3D-Discrete Wavelet Transform." Remote Sensing 13, no. 7 (2021): 1255. http://dx.doi.org/10.3390/rs13071255.

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Hyperspectral image classification is an emerging and interesting research area that has attracted several researchers to contribute to this field. Hyperspectral images have multiple narrow bands for a single image that enable the development of algorithms to extract diverse features. Three-dimensional discrete wavelet transform (3D-DWT) has the advantage of extracting the spatial and spectral information simultaneously. Decomposing an image into a set of spatial–spectral components is an important characteristic of 3D-DWT. It has motivated us to perform the proposed research work. The novelty

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Sildir, Hasan, Erdal Aydin, and Taskin Kavzoglu. "Design of Feedforward Neural Networks in the Classification of Hyperspectral Imagery Using Superstructural Optimization." Remote Sensing 12, no. 6 (2020): 956. http://dx.doi.org/10.3390/rs12060956.

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Artificial Neural Networks (ANNs) have been used in a wide range of applications for complex datasets with their flexible mathematical architecture. The flexibility is favored by the introduction of a higher number of connections and variables, in general. However, over-parameterization of the ANN equations and the existence of redundant input variables usually result in poor test performance. This paper proposes a superstructure-based mixed-integer nonlinear programming method for optimal structural design including neuron number selection, pruning, and input selection for multilayer perceptr

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Wang, Aili, Meixin Li, and Haibin Wu. "A Novel Classification Framework for Hyperspectral Image Data by Improved Multilayer Perceptron Combined with Residual Network." Symmetry 14, no. 3 (2022): 611. http://dx.doi.org/10.3390/sym14030611.

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Convolutional neural networks (CNNs) have attracted extensive attention in the field of modern remote sensing image processing and show outstanding performance in hyperspectral image (HSI) classification. Nevertheless, some hyperspectral images have fixed position priors and parameter sharing between different positions, so the common convolution layer may ignore some important fine and useful information and cannot guarantee to effectively capture the optimal image features. This paper proposes an improved multilayer perceptron (IMLP) and IMLP combined with ResNet (IMLP-ResNet) two models for

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Zhu, Xiufang, Nan Li, and Yaozhong Pan. "Optimization Performance Comparison of Three Different Group Intelligence Algorithms on a SVM for Hyperspectral Imagery Classification." Remote Sensing 11, no. 6 (2019): 734. http://dx.doi.org/10.3390/rs11060734.

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Group intelligence algorithms have been widely used in support vector machine (SVM) parameter optimization due to their obvious characteristics of strong parallel processing ability, fast optimization, and global optimization. However, few studies have made optimization performance comparisons of different group intelligence algorithms on SVMs, especially in terms of their application to hyperspectral remote sensing classification. In this paper, we compare the optimization performance of three different group intelligence algorithms that were run on a SVM in terms of five aspects by using thr

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Vidya, Mohanty, Kumar Behera Dayal, Ranjan Panda Amiya, and Swetanisha Subhra. "Comparative Analysis of Machine Learning and Deep Learning Models for LULC Classification using Remote Sensing Data." Indian Journal of Science and Technology 18, no. 18 (2025): 1397–409. https://doi.org/10.17485/IJST/v18i18.104.

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Abstract <strong>Objectives:</strong> The primary objective of this study is to evaluate and compare the performance of machine learning and deep learning models for Land Use and Land Cover (LULC) classification using remote sensing data. Specifically, it assesses Support Vector Machine (SVM), XGBoost, an ensemble model (SVM + XGBoost), and a Deep Neural Network (DNN) on pre-processed hyperspectral datasets (Pavia University, Indian Pines) and raw satellite imagery from the Twin Cities of Odisha, India.<strong> Method:</strong> The study follows a systematic workflow, including

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Ravi Kondal, Easala, and Soubhagya Sankar Barpanda. "Hyperspectral image classification using Hyb-3D convolution neural network spectral partitioning." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 1 (2022): 295. http://dx.doi.org/10.11591/ijeecs.v29.i1.pp295-303.

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Hyperspectral image classification (HSIC) on remote sensing imaging has brought immersive achievement using artificial intelligence technology. In deep learning convolution neural networks (CNN), 2D-CNN, and 3D-CNN methods are widely used to classify the spectral-spatial bands of hyperspectral images (HSI). The proposed Hybrid 3D-CNN (H3D-CNN) model framework for deeper features extraction predicts classification accuracy in supervised learning. The model reduces the narrow gap between supervised and unsupervised learning and the complexity and cost of the previous models. The HSI classificati

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Kondal, Easala Ravi, and Soubhagya Sankar Barpanda. "Hyperspectral image classification using Hyb-3D convolution neural network spectral partitioning." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 1 (2023): 295–303. https://doi.org/10.11591/ijeecs.v29.i1.pp295-303.

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Hyperspectral image classification (HSIC) on remote sensing imaging has brought immersive achievement using artificial intelligence technology. In deep learning convolution neural networks (CNN), 2D-CNN, and 3D-CNN methods are widely used to classify the spectral-spatial bands of hyperspectral images (HSI). The proposed Hybrid 3D-CNN (H3D-CNN) model framework for deeper features extraction predicts classification accuracy in supervised learning. The model reduces the narrow gap between supervised and unsupervised learning and the complexity and cost of the previous models. The HSI classificati

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Yang, Weiwei, and Haifeng Song. "Spectral-Spatial Classification of Hyperspectral Image Based on Support Vector Machine." International Journal of Information Technology and Web Engineering 16, no. 1 (2021): 56–74. http://dx.doi.org/10.4018/ijitwe.2021010103.

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Recent research has shown that integration of spatial information has emerged as a powerful tool in improving the classification accuracy of hyperspectral image (HSI). However, partitioning homogeneous regions of the HSI remains a challenging task. This paper proposes a novel spectral-spatial classification method inspired by the support vector machine (SVM). The model consists of spectral-spatial feature extraction channel (SSC) and SVM classifier. SSC is mainly used to extract spatial-spectral features of HSI. SVM is mainly used to classify the extracted features. The model can automatically

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Beirami, Behnam Asghari, and Mehdi Mokhtarzade. "Superpixel-Based Minimum Noise Fraction Feature Extraction for Classification of Hyperspectral Images." Traitement du Signal 37, no. 5 (2020): 812–22. http://dx.doi.org/10.18280/ts.370514.

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In this paper, a novel feature extraction technique called SuperMNF is proposed, which is an extension of the minimum noise fraction (MNF) transformation. In SuperMNF, each superpixel has its own transformation matrix and MNF transformation is performed on each superpixel individually. The basic idea behind the SuperMNF is that each superpixel contains its specific signal and noise covariance matrices which are different from the adjacent superpixels. The extracted features, owning spatial-spectral content and provided in the lower dimension, are classified by maximum likelihood classifier and

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Zhao, Xurui. "Joint Spatial-Spectral Convolutional Neural Network Enhanced with Attention Mechanism for Optimized Hyperspectral Image Classification." Applied and Computational Engineering 115, no. 1 (2024): 123–31. https://doi.org/10.54254/2755-2721/2025.18515.

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Hyperspectral imagery (HSI) classification is essential for remote sensing analysis, utilizing various image bands. Convolutional Neural Networks (CNNs) are prevalent in deep learning for visual data processing, with recent applications in HSI classification primarily employing 2D and 3D CNNs. However, 3D CNNs demand significant computational resources due to their complexity. This paper introduces a two-branch spatial-spectral joint convolutional neural network (SSDB) leveraging an attention mechanism for HSI classification. SSDB effectively extracts spectral and spatial information while red

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Chakrabarty, Ankush, Olivia Choudhury, Pallab Sarkar, Avishek Paul, and Debarghya Sarkar. "Hyperspectral image classification incorporating bacterial foraging-optimized spectral weighting." Artificial Intelligence Research 1, no. 1 (2012): 63. http://dx.doi.org/10.5430/air.v1n1p63.

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The present paper describes the development of a hyperspectral image classification scheme using support vector machines (SVM) with spectrally weighted kernels. The kernels are designed during the training phase of the SVM using optimal spectral weights estimated using the Bacterial Foraging Optimization (BFO) algorithm, a popular modern stochastic optimization algorithm. The optimized kernel functions are then in the SVM paradigm for bi-classification of pixels in hyperspectral images. The effectiveness of the proposed approach is demonstrated by implementing it on three widely used benchmark

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Wu, Yuyang, Qian Weng, Jiawen Lin, and Cairen Jian. "RA-ViT:Patch-wise Radially-Accumulate Module for ViT in Hyperspectral Image Classification." Journal of Physics: Conference Series 2278, no. 1 (2022): 012009. http://dx.doi.org/10.1088/1742-6596/2278/1/012009.

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Abstract Hyperspectral Images(HSIs) are data containing abundant spatial and spectral information, which is collected by advanced remote sensors. HSI Classification is a pixel-wise classification task that has broad prospects in the era of science and technology. In recent years, the widely used convolutional neural networks (CNNs) have come to the leading place in HSI Classification. However, the lack of utilization of spatial information limits its further application. To solve this issue, we considered the recently proposed Vision Transformer(ViT), which is modularized structures that are e

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Huang, Hong, Meili Chen, and Yule Duan. "Dimensionality Reduction of Hyperspectral Image Using Spatial-Spectral Regularized Sparse Hypergraph Embedding." Remote Sensing 11, no. 9 (2019): 1039. http://dx.doi.org/10.3390/rs11091039.

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Many graph embedding methods are developed for dimensionality reduction (DR) of hyperspectral image (HSI), which only use spectral features to reflect a point-to-point intrinsic relation and ignore complex spatial-spectral structure in HSI. A new DR method termed spatial-spectral regularized sparse hypergraph embedding (SSRHE) is proposed for the HSI classification. SSRHE explores sparse coefficients to adaptively select neighbors for constructing the dual sparse hypergraph. Based on the spatial coherence property of HSI, a local spatial neighborhood scatter is computed to preserve local struc

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CHANG, Peipei. "Hyperspectral Image Classification Based on the Lightweight G-GhostNets Network." Acta Interdisciplinary Science 2, no. 1 (2025): 48–57. https://doi.org/10.48014/ais.20250314002.

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Hyperspectral image classification (HSIC) has been widely applied in remote sensing image analysis. However, deploying deep learning models on embedded devices for HSIC tasks faces challenges due to excessive parameters and high computational complexity. To address these issues, this paper proposes the lightweight G-GhostNets network optimized for GPU devices. G-GhostNets effectively integrates both spectral and spatial features of hyperspectral images. By introducing the G-Ghost Stage, which aggregates intermediate features, the model achieves high-precision classification while maintaining c

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Fairweather, M. L., and B. W. Geils. "First Report of the White Pine Blister Rust Pathogen, Cronartium ribicola, in Arizona." Plant Disease 95, no. 4 (2011): 494. http://dx.doi.org/10.1094/pdis-10-10-0699.

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White pine blister rust, caused by Cronartium ribicola J.C. Fisch., was found on southwestern white pine (Pinus flexilis James var. reflexa Engelm., synonym P. strobiformis Engelm.) near Hawley Lake, Arizona (Apache County, White Mountains, 34.024°N, 109.776°W, elevation 2,357 m) in April 2009. Although white pines in the Southwest (Arizona and New Mexico) have been repeatedly surveyed for blister rust since its discovery in the Sacramento Mountains of southern New Mexico in 1990 (1,2), this was the first confirmation of C. ribicola in Arizona. Numerous blister rust cankers were sporulating on

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Chen, Guang Yi. "Multiscale filter-based hyperspectral image classification with PCA and SVM." Journal of Electrical Engineering 72, no. 1 (2021): 40–45. http://dx.doi.org/10.2478/jee-2021-0006.

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Abstract Hyperspectral imagery can offer images with high spectral resolution and provide a unique ability to distinguish the subtle spectral signatures of different land covers. In this paper, we develop a new algorithm for hyperspectral image classification by using principal component analysis (PCA) and support vector machines (SVM). We use PCA to reduce the dimensionality of an HSI data cube, and then perform spatial convolution with three different filters on the PCA output cube. We feed all three convolved output cubes to SVM to classify every pixel. Finally, we perform fusion on the thr

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Darvishnezhad, M., H. Ghassemian, and M. Imani. "LOCAL BINARY GRAPH FEATURE REDUCTION FOR THREE-DIMENSIONAL GABOR FILTER BASED HYPERSPECTRAL IMAGE CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 285–91. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-285-2019.

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Abstract. One of the challenges of the hyperspectral image classification is the fusing spectral and spatial features. There are several methods for fusing features in hyperspectral image classification. Three-Dimensional Gabor Filters are the best method to extract spectral and spatial features simultaneously. However, one of the problems with using the 3D Gabor filter is the high number of extracted features. In this paper, to reducing extracted features from 3D-Gabor filters and increasing the classification accuracy in hyperspectral images, a novel method named Local Binary Graph (LBG) is

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Bai, Jing, Jiawei Lu, Zhu Xiao, Zheng Chen, and Licheng Jiao. "Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification." Remote Sensing 14, no. 14 (2022): 3426. http://dx.doi.org/10.3390/rs14143426.

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Nowadays, HSI classification can reach a high classification accuracy when given sufficient labeled samples as training set. However, the performances of existing methods decrease sharply when trained on few labeled samples. Existing methods in few-shot problems usually require another dataset in order to improve the classification accuracy. However, the cross-domain problem exists in these methods because of the significant spectral shift between target domain and source domain. Considering above issues, we propose a new method without requiring external dataset through combining a Generative

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Ye, Chengming, Hongfu Li, Chunming Li, et al. "A Building Roof Identification CNN Based on Interior-Edge-Adjacency Features Using Hyperspectral Imagery." Remote Sensing 13, no. 15 (2021): 2927. http://dx.doi.org/10.3390/rs13152927.

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Hyperspectral remote sensing can obtain both spatial and spectral information of ground objects. It is an important prerequisite for a hyperspectral remote sensing application to make good use of spectral and image features. Therefore, we improved the convolutional Neural Network (CNN) model by extracting interior-edge-adjacency features of building roof and proposed a new CNN model with a flexible structure: Building Roof Identification CNN (BRI-CNN). Our experimental results demonstrated that the BRI-CNN can not only extract interior-edge-adjacency features of building roof, but also change

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Grewal, Reaya, Geeta Kasana, and Singara Singh Kasana. "A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features." Applied Sciences 14, no. 11 (2024): 4909. http://dx.doi.org/10.3390/app14114909.

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This research presents an innovative technique for semantic segmentation of Hyperspectral Image (HSI) while focusing on its dimensionality reduction. A unique technique is applied to three distinct HSI landcover datasets, Indian Pines, Pavia University, and Salinas Valley, acquired from diverse sensors. HSIs are inherently multi-view structures, causing redundancy and computation overload due to their high dimensionality. The technique utilizes Canonical Correlation Analysis (CCA) variants, Pairwise CCA (PCCA) and Multiple Set CCA (MCCA), to extract features from multiple views of the input im

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Yao, Wei, Cheng Lian, and Lorenzo Bruzzone. "A CNN Ensemble Based on a Spectral Feature Refining Module for Hyperspectral Image Classification." Remote Sensing 14, no. 19 (2022): 4982. http://dx.doi.org/10.3390/rs14194982.

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In the study of hyperspectral image classification based on machine learning theory and techniques, the problems related to the high dimensionality of the images and the scarcity of training samples are widely discussed as two main issues that limit the performance of the data-driven classifiers. These two issues are closely interrelated, but are usually addressed separately. In our study, we try to kill two birds with one stone by constructing an ensemble of lightweight base models embedded with spectral feature refining modules. The spectral feature refining module is a technique based on th

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Tinega, Haron, Enqing Chen, Long Ma, Richard M. Mariita, and Divinah Nyasaka. "Hyperspectral Image Classification Using Deep Genome Graph-Based Approach." Sensors 21, no. 19 (2021): 6467. http://dx.doi.org/10.3390/s21196467.

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Recently developed hybrid models that stack 3D with 2D CNN in their structure have enjoyed high popularity due to their appealing performance in hyperspectral image classification tasks. On the other hand, biological genome graphs have demonstrated their effectiveness in enhancing the scalability and accuracy of genomic analysis. We propose an innovative deep genome graph-based network (GGBN) for hyperspectral image classification to tap the potential of hybrid models and genome graphs. The GGBN model utilizes 3D-CNN at the bottom layers and 2D-CNNs at the top layers to process spectral–spatia

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Li, Chenming, Simon X. Yang, Yao Yang, et al. "Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network." Sensors 18, no. 10 (2018): 3587. http://dx.doi.org/10.3390/s18103587.

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In a traditional convolutional neural network structure, pooling layers generally use an average pooling method: a non-overlapping pooling. However, this condition results in similarities in the extracted image features, especially for the hyperspectral images of a continuous spectrum, which makes it more difficult to extract image features with differences, and image detail features are easily lost. This result seriously affects the accuracy of image classification. Thus, a new overlapping pooling method is proposed, where maximum pooling is used in an improved convolutional neural network to

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Osman, Hassan Abdalla Abdelkarim, and Norsinnira Zainul Azlan. "Generating images for Supervised Hyperspectral Image Classification with Generative Adversarial Nets." Journal of Integrated and Advanced Engineering (JIAE) 2, no. 2 (2022): 107–12. http://dx.doi.org/10.51662/jiae.v2i2.80.

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With the advancement of remote sensing technologies, hyperspectral imagery has garnered significant interest in the remote sensing community. These developments have inspired improvement in various hyperspectral images (HSI) classification applications, such as land cover mapping, amongst other earth observation applications. Deep Neural Networks have revolutionized image classification tasks in areas of computer vision. However, in the domain of hyperspectral images, insufficient training samples have been earmarked as a significant bottleneck for supervised HSI classification. Moreover, acqu

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HUANG, HONG, and HAILIANG FENG. "LOCALITY AND GLOBALITY DISCRIMINANT FEATURE AND ITS APPLICATION IN HYPERSPECTRAL IMAGE CLASSIFICATION." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 04 (2013): 1350010. http://dx.doi.org/10.1142/s0218001413500109.

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Feature selection has attracted a huge amount of interest in both research and application communities of hyperspectral image (HSI) classification. Generally, supervised feature selection methods are superior to unsupervised ones without label information. However, in classification of HSI, the labeled samples are often difficult, expensive or time-consuming to obtain. In this paper, we proposed a novel semi-supervised feature selection method, called Locality and Globality Discriminant Feature (LGDF), for HSI classification. This method combines Fisher's criteria and Graph Laplacian, which ma

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Luo, Huiwu, Yuan Yan Tang, and Lina Yang. "Subspace Learning via Local Probability Distribution for Hyperspectral Image Classification." Mathematical Problems in Engineering 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/145136.

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The computational procedure of hyperspectral image (HSI) is extremely complex, not only due to the high dimensional information, but also due to the highly correlated data structure. The need of effective processing and analyzing of HSI has met many difficulties. It has been evidenced that dimensionality reduction has been found to be a powerful tool for high dimensional data analysis. Local Fisher’s liner discriminant analysis (LFDA) is an effective method to treat HSI processing. In this paper, a novel approach, called PD-LFDA, is proposed to overcome the weakness of LFDA. PD-LFDA emphasizes

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Dong, Chao, and Lianfang Tian. "Accelerating Relevance-Vector-Machine-Based Classification of Hyperspectral Image with Parallel Computing." Mathematical Problems in Engineering 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/252979.

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Benefiting from the kernel skill and the sparse property, the relevance vector machine (RVM) could acquire a sparse solution, with an equivalent generalization ability compared with the support vector machine. The sparse property requires much less time in the prediction, making RVM potential in classifying the large-scale hyperspectral image. However, RVM is not widespread influenced by its slow training procedure. To solve the problem, the classification of the hyperspectral image using RVM is accelerated by the parallel computing technique in this paper. The parallelization is revealed from

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Luo, Huiwu, Yuan Yan Tang, Chunli Li, and Lina Yang. "Local and Global Geometric Structure Preserving and Application to Hyperspectral Image Classification." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/917259.

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Locality Preserving Projection (LPP) has shown great efficiency in feature extraction. LPP captures the locality by theK-nearest neighborhoods. However, recent progress has demonstrated the importance of global geometric structure in discriminant analysis. Thus, both the locality and global geometric structure are critical for dimension reduction. In this paper, a novel linear supervised dimensionality reduction algorithm, calledLocality and Global Geometric Structure Preserving(LGGSP) projection, is proposed for dimension reduction. LGGSP encodes not only the local structure information into

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Amit Kumar Jha, Ram Krishna Maharjan, and Nanda Bikram Adhikari. "Hyperspectral Image Analysis using LSTM and 2D CNN and its Application in Remote Sensing." Journal of Innovative Image Processing 5, no. 4 (2023): 358–78. http://dx.doi.org/10.36548/jiip.2023.4.002.

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The land cover classification in urban areas is described in this research work. The use of hyperspectral image analysis is growing in popularity because it performs better than conventional machine learning techniques. Hypercubes, a type of three-dimensional dataset with two spatial dimensions and one spectral dimension, make up the Hyperspectral imaging (HSI). An overview of HSI's uses in remote sensing applications and the methods for classifying it are given in this research. In the field of HSI, numerous experiments are conducted with various deep learning methods for analysis and classif

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Nyabuga, Douglas Omwenga, та Godfrey Nyariki. "Learning high-level spectral-spatial features for hyperspectral image classiﬁcation with insufficient labeled samples". IAES International Journal of Artificial Intelligence (IJ-AI) 14, № 2 (2025): 1211. https://doi.org/10.11591/ijai.v14.i2.pp1211-1219.

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Hyperspectral image (HSI) classification research is a hot area, with a mass of new methods being developed to improve performance for specific applications that use spatial and spectral image material. However, the main obstacle for scientists is determining how to identify HSIs effectively. These obstacles include an increased presence of redundant spectral information, high dimensionality in observed data, and limited spatial features in a classification model. To this end, we, therefore, proposed a novel approach for learning high-level spectral-spatial features for HSI classiﬁcation with

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