Exploring ELM-based spatial–spectral classification of hyperspectral images
Abstract
Among the different computational intelligence techniques avalaible for hyperspectral data classification, support vector machines (SVMs) have played a dominant role. Recently, a new learning algorithm for single-layer feedforward neural networks called the extreme learning machine (ELM) was proposed. This technique is competitive with SVMs in terms of accuracy, learning speed, and computational scalability. In this article, we propose and evaluate the use of ELM for land-cover classification from hyperspectral images. In addition, we consider two ELM-based techniques integrating spectral and spatial information of the image. The first is a scheme that uses a majority vote approach in order to combine the results of a pixel-wise spectral classification by ELM and a segmentation map obtained by a watershed algorithm. The second introduces spatial information from a small spatial neighbourhood after the classification by ELM. We show the usefulness of spatial–spectral ELM-based classification techniques in hyperspectral imaging. The results are compared to those obtained by similar SVM-based techniques and show improved classification results and much lower execution time. These simple and computationally cheap methods can be combined with others traditionally applied to hyperspectral images.
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The most recent ways involving classification using hyperspectral data include extreme learning machine (ELM), partial least squares discriminant analysis (PLS-DA), and various deep learning algorithms.
“The most recent ways involving classification using hyperspectral data include extreme learning machine (ELM) [7–9], partial least squares discriminant analysis (PLS-DA) [10,11], and various deep learning algorithms [12,13].”
From Development of online classification system for construction waste based on industrial camera and hyperspectral camera (2019) · cites this paper as [8]
Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070 · see citing works
Extreme learning machine: Theory and applications
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.126 · Cited by 13,285 · see citing works
Image analysis and mathematical morphology
(1982) · Computer Graphics and Image Processing · doi:10.1016/0146-664x(82)90078-8 · Cited by 8,152 · see citing works
Neural networks: A comprehensive foundation
Tadeusiewicz, R. (1995) · Control Engineering Practice · doi:10.1016/0967-0661(95)90080-2 · Cited by 7,217 · see citing works
Extreme Learning Machine for Regression and Multiclass Classification
Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2011.2168604 · Cited by 5,564 · see citing works
Classification of hyperspectral remote sensing images with support vector machines
Melgani, F., & Bruzzone, L. (2004) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2004.831865 · Cited by 4,426 · see citing works
Extreme learning machine: a new learning scheme of feedforward neural networks
Huang, G.-B., Zhu, Q.-Y., & Siew, C. (2005) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2004.1380068 · Cited by 4,248 · see citing works
Image Analysis and Mathematical Morphology.
Diggle, P., & Serra, J. (1983) · Biometrics · doi:10.2307/2531038 · Cited by 3,910 · see citing works
Support vector machines in remote sensing: a review
Mountrakis, G., Im, J., & Ogole, C. (2011) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2010.11.001 · Cited by 3,473 · see citing works
Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes
Huang, G.-B., Chen, L., & Siew, C. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.875977 · Cited by 2,614 · see citing works
Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment
Pontius, R. G., & Millones, M. (2011) · International Journal of Remote Sensing · doi:10.1080/01431161.2011.552923 · Cited by 2,085 · see citing works
Extreme learning machines: a survey
Huang, G.-B., Wang, D. H., & Lan, Y. (2011) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-011-0019-y · Cited by 1,903 · see citing works
Recent advances in techniques for hyperspectral image processing
Plaza, A., Benediktsson, J. A., Boardman, J. W., Brazile, J., Bruzzone, L., Camps‐Valls, G., Chanussot, J., Fauvel, M., Gamba, P. E., Gualtieri, A. G., et al. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2007.07.028 · Cited by 1,705 · see citing works
Remote Sensing Digital Image Analysis
Richards, J. A., & Jia, X. (1999) · Springer eBooks · doi:10.1007/978-3-662-03978-6 · Cited by 1,538 · see citing works
Classification of hyperspectral data from urban areas based on extended morphological profiles
Benediktsson, J. A., Palmason, J. A., & Sveinsson, J. R. (2005) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2004.842478 · Cited by 1,399 · see citing works
Advances in Spectral-Spatial Classification of Hyperspectral Images
Fauvel, M., Tarabalka, Y., Benediktsson, J. A., Chanussot, J., & Tilton, J. C. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2197589 · Cited by 1,288 · see citing works
Convex incremental extreme learning machine
Huang, G.-B., & Chen, L. (2007) · Neurocomputing · doi:10.1016/j.neucom.2007.02.009 · Cited by 1,140 · see citing works
Composite Kernels for Hyperspectral Image Classification
Camps‐Valls, G., Gómez‐Chova, L., Muñoz-Marı́, J., Vila‐Francés, J., & Calpe‐Maravilla, J. (2006) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2005.857031 · Cited by 1,069 · see citing works
Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles
Fauvel, M., Benediktsson, J. A., Chanussot, J., & Sveinsson, J. R. (2008) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2008.922034 · Cited by 1,009 · see citing works
Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles
Fauvel, M., Chanussot, J., Benediktsson, J. A., & Sveinsson, J. R. (2007) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2007.4423943 · Cited by 910 · see citing works
Enhanced random search based incremental extreme learning machine
Huang, G.-B., & Chen, L. (2007) · Neurocomputing · doi:10.1016/j.neucom.2007.10.008 · Cited by 891 · see citing works
Support Vector Machines for Pattern Classification
Abe, S. (2005) · Springer eBooks · doi:10.1007/1-84628-219-5 · Cited by 853 · see citing works
Evolutionary extreme learning machine
Zhu, Q., Qin, A. K., Suganthan, P. N., & Huang, G.-B. (2005) · Pattern Recognition · doi:10.1016/j.patcog.2005.03.028 · Cited by 817 · see citing works
SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images
Tarabalka, Y., Fauvel, M., Chanussot, J., & Benediktsson, J. A. (2010) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2010.2047711 · Cited by 749 · see citing works
Comparison of airborne hyperspectral data and eo-1 hyperion for mineral mapping
Kruse, F. A., Boardman, J. W., & Huntington, J. F. (2003) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2003.812908 · Cited by 744 · see citing works
Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques
Tarabalka, Y., Benediktsson, J. A., & Chanussot, J. (2009) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2009.2016214 · Cited by 680 · see citing works
Support Vector Machines for Pattern Classification
Abe, S. (2010) · Advances in computer vision and pattern recognition · doi:10.1007/978-1-84996-098-4 · Cited by 583 · see citing works
Segmentation and classification of hyperspectral images using watershed transformation
Tarabalka, Y., Chanussot, J., & Benediktsson, J. A. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.01.016 · Cited by 562 · see citing works
A Geodesic Framework for Fast Interactive Image and Video Segmentation and Matting
Bai, X., & Sapiro, G. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408931 · Cited by 438 · see citing works
A fast pruned-extreme learning machine for classification problem
Rong, H.-J., Ong, Y.-S., Tan, A., & Zhu, Z. (2008) · Neurocomputing · doi:10.1016/j.neucom.2008.01.005 · Cited by 359 · see citing works
Voting based extreme learning machine
Cao, J., Lin, Z., Huang, G.-B., & Liu, N. (2011) · Information Sciences · doi:10.1016/j.ins.2011.09.015 · Cited by 356 · see citing works
Nemmour, H., & Chibani, Y. (2006) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2006.09.004 · Cited by 317 · see citing works
A spatial–spectral kernel-based approach for the classification of remote-sensing images
Fauvel, M., Chanussot, J., & Benediktsson, J. A. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.03.035 · Cited by 281 · see citing works
Tarabalka, Y., Chanussot, J., & Benediktsson, J. A. (2010) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2009.2037132 · Cited by 275 · see citing works
A study on effectiveness of extreme learning machine
Wang, Y. G., Cao, F., & Yuan, Y. (2011) · Neurocomputing · doi:10.1016/j.neucom.2010.11.030 · Cited by 271 · see citing works
Incremental extreme learning machine with fully complex hidden nodes
Huang, G.-B., Li, M.-B., Chen, L., & Siew, C. (2007) · Neurocomputing · doi:10.1016/j.neucom.2007.07.025 · Cited by 259 · see citing works
Kernel-based extreme learning machine for remote-sensing image classification
Pal, M., Maxwell, A. E., & Warner, T. A. (2013) · Remote Sensing Letters · doi:10.1080/2150704x.2013.805279 · Cited by 202 · see citing works
Fast Computation of Moore-Penrose Inverse MatricesPreprint
Courrieu, P. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0804.4809 · Cited by 196 · see citing works
A morphological gradient approach to color edge detection
Evans, A. N., & Xueshuan, L. I. U. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.864164 · Cited by 178 · see citing works
GPU-accelerated and parallelized ELM ensembles for large-scale regression
van Heeswijk, M., Miché, Y., Erkki Oja, A., & Lendasse, A. (2011) · Neurocomputing · doi:10.1016/j.neucom.2010.11.034 · Cited by 163 · see citing works
Evaluation of Kernels for Multiclass Classification of Hyperspectral Remote Sensing Data
Fauvel, M., Chanussot, J., & Benediktsson, J. A. (2006) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2006.1660467 · Cited by 118 · see citing works
van der Linden, S., Janz, A., Waske, B., Eiden, M. J., & Hostert, P. (2007) · Journal of Applied Remote Sensing · doi:10.1117/1.2813466 · Cited by 97 · see citing works
Extreme‐learning‐machine‐based land cover classification
Pal, M. (2009) · International Journal of Remote Sensing · doi:10.1080/01431160902788636 · Cited by 82 · see citing works
An automatic method for burn scar mapping using support vector machines
Cao, X., Chen, J., Matsushita, B., Imura, H., & Wang, L. (2009) · International Journal of Remote Sensing · doi:10.1080/01431160802220219 · Cited by 70 · see citing works
Composite Function Wavelet Neural Networks with Differential Evolution and Extreme Learning Machine
Cao, J., Lin, Z., & Huang, G.-B. (2011) · Neural Processing Letters · doi:10.1007/s11063-011-9176-y · Cited by 39 · see citing works
Efficient segmentation of hyperspectral images on commodity GPUs
Pablo, Q.-B., Francisco, A., & Heras, D. B. (2012) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-61499-105-2-2130 · Cited by 13 · see citing works
Self-citationKing, I., Wang, J., Wang, D., & Chan, L. (2006) · Lecture notes in computer science · doi:10.1007/11893028 · Cited by 13 · see citing works
Flagged first, then by citations
1 of the 50 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Extreme Learning Machine With Composite Kernels for Hyperspectral Image Classification
Zhou, Y., Peng, J., & Chen, C. L. P. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2359965 · Cited by 181 · see citing works
Deep Multiview Learning for Hyperspectral Image Classification
Liu, B., Yu, A., Yu, X., Wang, R., Gao, K., & Guo, W. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3034133 · Cited by 139 · see citing works
Zhao, C., Wan, X., Zhao, G., Cui, B., Liu, W., & Qi, B. (2017) · European Journal of Remote Sensing · doi:10.1080/22797254.2017.1274566 · Cited by 78 · see citing works
Zhu, M., Fan, J., Yang, Q., & Chen, T. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3131152 · Cited by 61 · see citing works
Lopez-Fandino, J., Quesada-Barriuso, P., Heras, D. B., & Arguello, F. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2384133 · Cited by 57 · see citing works
Self-citationHaut, J. M., Paoletti, M. E., Plaza, J., & Plaza, A. (2018) · Journal of Real-Time Image Processing · doi:10.1007/s11554-018-0793-9 · Cited by 51 · see citing works
Liu, Y., Xie, H., Chen, Y., Tan, K., Wang, L., & Xie, W. (2016) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2016.07.009 · Cited by 45 · see citing works
Cao, F., Yang, Z., Ren, J., Ling, W.-K., Zhao, H., Sun, M., & Benediktsson, J. A. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2828601 · Cited by 44 · see citing works
Liu, B., Guo, W., Chen, X., Gao, K., Zuo, X., Wang, R., & Yu, A. (2020) · IEEE Access · doi:10.1109/access.2020.3004968 · Cited by 44 · see citing works
Soil type classification and mapping using hyperspectral remote sensing data
Vibhute, A. D., Kale, K. V., Dhumal, R. K., & Mehrotra, S. C. (2015) · International Conference on Man and Machine Interfacing (MAMI) · doi:10.1109/mami.2015.7456607 · Cited by 43 · see citing works
Apple Variety Identification Based on Dielectric Spectra and Chemometric Methods
Shang, L., Guo, W., & Nelson, S. O. (2014) · Food Analytical Methods · doi:10.1007/s12161-014-9985-5 · Cited by 40 · see citing works
Rapid Identification of Genetically Modified Maize Using Laser-Induced Breakdown Spectroscopy
Liu, X., Feng, X., Liu, F., Peng, J., & He, Y. (2018) · Food and Bioprocess Technology · doi:10.1007/s11947-018-2216-0 · Cited by 35 · see citing works
Men, H., Yuan, H., Shi, Y. H., Liu, M., Wang, Q., & Liu, J. (2021) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2021.120155 · Cited by 34 · see citing works
Optimizing area under the ROC curve via extreme learning machines
Yang, Z., Zhang, T., Lu, J., Zhang, D., & Kalui, D. M. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.05.013 · Cited by 31 · see citing works
S3L: Spectrum Transformer for Self-Supervised Learning in Hyperspectral Image Classification
Guo, H., & Liu, W. (2024) · Remote Sensing · doi:10.3390/rs16060970 · Cited by 28 · see citing works
Xiao, W., Yang, J., Fang, H., Zhuang, J., & Ku, Y. (2019) · PLOS ONE · doi:10.1371/journal.pone.0208706 · Cited by 27 · see citing works
Argüello, F., & Heras, D. B. (2015) · International Journal of Remote Sensing · doi:10.1080/01431161.2014.999882 · Cited by 27 · see citing works
Self-citationLiu, L., Shi, Z., Pan, B., Zhang, N., Luo, H., & Lan, X. (2020) · Remote Sensing · doi:10.3390/rs12020280 · Cited by 27 · see citing works
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Liu, Y., Wu, T., Yang, J., Tan, K., & Wang, S. (2018) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2018.12.002 · Cited by 25 · see citing works
Pan, B., Shi, Z., & Xu, X. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2853268 · Cited by 23 · see citing works
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Li, H., Tu, B., Liu, B., Li, J., & Plaza, A. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3516742 · Cited by 22 · see citing works
Sun, Y., Liu, B., Yu, X., Yu, A., Xue, Z., & Gao, K. (2022) · International Journal of Remote Sensing · doi:10.1080/01431161.2022.2054299 · Cited by 22 · see citing works
Chen, X., Cheng, G., Li, H., & Li, Y. (2016) · Strojniški vestnik – Journal of Mechanical Engineering · doi:10.5545/sv-jme.2016.3811 · Cited by 18 · see citing works
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Duan, W., Shutao Li, A., & Fang, L. (2014) · Communications in computer and information science · doi:10.1007/978-3-662-45646-0_17 · Cited by 17 · see citing works
Liu, Y., Chen, Y., Tan, K., Xie, H., Wang, L., Yan, X., Xie, W., & Xu, Z. X. (2016) · Measurement Science and Technology · doi:10.1088/0957-0233/27/12/125501 · Cited by 17 · see citing works
Classification of complex environments using pixel level fusion of satellite data
Vibhute, A. D., Kale, K. V., Gaikwad, S. V., Dhumal, R. K., Nagne, A. D., Varpe, A. B., Nalawade, D. B., & Mehrotra, S. C. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08978-4 · Cited by 16 · see citing works
Decentralized Federated GAN for Hyperspectral Change Detection in Edge Computing
Xie, W., Xu, X., & Li, Y. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3389641 · Cited by 15 · see citing works
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Self-citationYan, D., Chu, Y., Li, L., & Liu, D. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4494-3 · Cited by 12 · see citing works
Zhao, C., Wan, X., Zhao, G., & Yan, Y. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.016033 · Cited by 11 · see citing works
A new supervised classifier exploiting spectral-spatial information in the Bayesian framework
Barca, E., Castrignanò, A., Ruggieri, S., & Rinaldi, M. (2019) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2019.101990 · Cited by 10 · see citing works
ELM-based hyperspectral imagery processor for onboard real-time classification
Basterretxea, K., Martinez-Corral, U., Finker, R., & del Campo, I. (2016) · Conference on Design and Architectures for Signal and Image Processing · doi:10.1109/dasip.2016.7853795 · Cited by 10 · see citing works
GPU Projection of ECAS-II Segmenter for Hyperspectral Images Based on Cellular Automata
López-Fandiño, J., Priego, B., Heras, D. B., & Argüello, F. (2016) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2016.2588530 · Cited by 8 · see citing works
Self-citationHypeRvieW: an open source desktop application for hyperspectral remote-sensing data processing
Garea, A. S., Ordóñez, Á., Heras, D. B., & Argüello, F. (2016) · International Journal of Remote Sensing · doi:10.1080/01431161.2016.1244363 · Cited by 8 · see citing works
Self-citationAkbari, D. (2017) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-016-0652-8 · Cited by 7 · see citing works
Xie, F., Dongcui, H., Li, F., Yang, J., & Liu, D. (2018) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi7070284 · Cited by 6 · see citing works
Shen, Y., Yu, Y., Wei, Y., & Guo, H. (2022) · Infrared Physics & Technology · doi:10.1016/j.infrared.2022.104415 · Cited by 6 · see citing works
Quesada-Barriuso, P., Heras, D. B., & Argüello, F. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2240854 · Cited by 6 · see citing works
Self-citationWu, X., Wu, X., Huang, H., Zhang, F., & Wen, Y. (2023) · Analytical Letters · doi:10.1080/00032719.2023.2210708 · Cited by 4 · see citing works
Vibhute, A. D., Gaikwad, S. V., Dhumal, R. K., Nagne, A. D., Varpe, A. B., Nalawade, D. B., Kale, K. V., & Mehrotra, S. C. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-9181-1_31 · Cited by 4 · see citing works
Vibhute, A. D., Kale, K. V., Dhumal, R. K., Nagne, A. D., Mehrotra, S. C., Varpe, A. B., Surase, R. R., Nalawade, D. B., & Gaikwad, S. V. (2018) · Lecture notes in electrical engineering · doi:10.1007/978-981-13-1906-8_54 · Cited by 4 · see citing works
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Singh, S., & Kasana, S. S. (2019) · Amity International Conference on Artificial Intelligence (AICAI) · doi:10.1109/aicai.2019.8701243 · Cited by 4 · see citing works
Compressed sampling reconstruction of hyperspectral images based on spectral prediction
Wang, L., & Feng, Y. (2015) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.22189 · Cited by 4 · see citing works
Pradhan, M. K., Minz, S., & Shrivastava, V. K. (2020) · Current Science · doi:10.18520/cs/v119/i6/934-943 · Cited by 3 · see citing works
Wang, L., Feng, Y., & Wang, Z. (2017) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.22482 · Cited by 3 · see citing works
Liu, Y., Qiao, F., Wang, S., Wang, R., & Xu, L. (2021) · RSC Advances · doi:10.1039/d1ra03664e · Cited by 3 · see citing works
AN EXTENDED SPECTRAL–SPATIAL CLASSIFICATION APPROACH FOR HYPERSPECTRAL DATA
Akbari, D. (2017) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iv-4-w4-37-2017 · Cited by 2 · see citing works
Akbari, D., Safari, A., Homayouni, S., & Khazai, S. (2015) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsarchives-xl-1-w5-37-2015 · Cited by 1 · see citing works
An open source desktop application for classification of remote sensing data
Garea, A. S., Heras, D. B., & Argüello, F. (2015) · IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) · doi:10.1109/idaacs.2015.7340751 · Cited by 1 · see citing works
Self-citationJiafeng, W., Xuebin, S., Hanjin, X., Guohu, X., Bo, W., Wei, L., & Siqi, W. (2022) · Power System and Green Energy Conference (PSGEC) · doi:10.1109/psgec54663.2022.9881131 · Cited by 1 · see citing works
Semi-Supervised FCM and SVM in Co-Training Framework for the Classification of Hyperspectral Images
Aydav, P. S. S., & Minz, S. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-47952-1_10
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Liu, J., Zhang, S., Sun, H., & Wu, Z. (2019) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-06155-5_11
Hyperspectral Image Classification Based on Stacked Extreme Learning Machine
Fu, Q., Qin, J., & Li, L. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-6963-7_3
Zhang, Q., You, Y., Wang, Y., Bai, X., & Zhang, W. (2026) · Journal of Food Composition and Analysis · doi:10.1016/j.jfca.2026.109144
- Authors
- Heras, D. B., Argüello, F., & Quesada-Barriuso, P.
- Affiliations
- CITIUS (Centro Singular de Investigación en Tecnoloxías da Información), Rúa de Jenaro de la Fuente Domínguez, 15782 Santiago de Compostela, Spain
- Corresponding author
- Heras, Dora B.
- Venue
- International Journal of Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 2014-01-02
- Volume
- 35
- Issue
- 2
- Pages
- 401-423
- ISSN
- 0143-1161 (ISSN-L)
- 1366-5901
- OpenAlex
- W2011731909
- Cited by
- 54
- References
- 50
- Citations by year
- 2026: 1 · 2024: 3 · 2023: 1 · 2022: 4 · 2021: 3 · 2020: 5 · 2019: 5 · 2018: 7 · 2017: 7 · 2016: 9 · 2015: 6 · 2014: 3
- FWCI
- 12.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Remote Sensing (publishedVersion)
- Topics
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Keywords
- extreme learning machine, hyperspectral image classification, land cover classification, watershed segmentation, majority voting, single-layer feedforward neural network, support vector machine, computational efficiency
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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