Estudio Comparativo de Técnicas de Clasificación de Imágenes Hiperespectrales

Mercedes Eugenia Paoletti, Juan Mario Haut, Javier Plaza, Antonio Plaza

Resumen

Las imágenes hiperespectrales constituyen el núcleo de varios programas de observación remota de la Tierra. La cantidad de información que contienen estas imágenes, formadas por cientos de canales espectrales estrechos y casi continuos, resulta de gran utilidad en aplicaciones en las que la caracterización de los materiales observados en la superficie terrestre resulta de gran relevancia. Esto se debe a la posibilidad de caracterizar de forma inequívoca cada material a través de su firma espectral. Algunas de estas aplicaciones son la agricultura de precisión, la planificación de espacios urbanos, o la prevención y seguimiento de desastres naturales. Sin embargo, la gran dimensión de las imágenes hiperespectrales supone un reto en su tratamiento, almacenamiento y procesamiento, debido a la gran variabilidad espectral y la correlación existente en los datos. En la literatura se han desarrollado múltiples algoritmos de análisis de imágenes hiperespectrales. En este artículo revisamos los algoritmos más utilizados para la clasificación de este tipo de imágenes, realizando experimentos con tres imágenes públicas y presentando una comparativa entre los métodos más ampliamente utilizados en este campo.

Palabras clave

Inteligencia artificial; aplicaciones satelitales; análisis de escenas; ingeniería informática; aplicaciones informáticas

Clasificación por materias

90: Visión por Computador;110: Inteligencia computacional y técnicas de supervisión y detección de fallos; 80: Filtrado, estimación y análisis y tratamiento de señales e imágenes

Texto completo:

PDF

Referencias

Acito, N., Corsini, G., Diani, M., 2003. An unsupervised algorithm for hyperspectral image segmentation based on the gaussian mixture model. In: Geoscience and Remote Sensing Symposium, 2003. IGARSS’03. Proceedings. 2003 IEEE International. Vol. 6. IEEE, pp. 3745–3747.

Atkinson, P. M., Tatnall, A. R. L., 1997. Introduction Neural networks in remote sensing. International Journal of Remote Sensing 18 (4), 699–709. https://doi.org/10.1080/014311697218700

Banerjee, A., Burlina, P., Diehl, C., 2006. A Support Vector Method for Anomaly Detection in Hyperspectral Imagery. IEEE Transactions on Geoscience and Remote Sensing 44 (8), 2282–2291. https://doi.org/10.1109/TGRS.2006.873019

Bannari, A., Pacheco, A., Staenz, K., McNairn, H., Omari, K., 2006. Estimating and mapping crop residues cover on agricultural lands using hyperspectral and IKONOS data. Remote Sensing of Environment 104 (4), 447 – 459. https://doi.org/10.1016/j.rse.2006.05.018

Belgiu, M., Dragut¸, L., 2016. Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011

Benediktsson, J. A., Ghamisi, P., 2015. Spectral-Spatial Classification of Hyperspectral Remote Sensing Images. Artech House.

Camps-Valls, G., Marsheva, T. V. B., Zhou, D., 2007. Semi-supervised graph-based hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 45 (10), 3044–3054. https://doi.org/10.1109/tgrs.2007.895416

Chan, J. C.-W., Paelinckx, D., 2008. Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sensing of Environment 112 (6), 2999–3011.

Chang, C.-I., 2003. Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Springer US. https://doi.org/10.1007/978-1-4419-9170-6

Chang, N.-B., Bai, K., Imen, S., Chen, C.-F., Gao, W., 2016. Multisensor Satellite Image Fusion and Networking for All-Weather Environmental Monitoring. IEEE Systems Journal PP (99), 1–17. https://doi.org/10.1109/JSYST.2016.2565900

Chi, M., Bruzzone, L., 2007. Semisupervised classification of hyperspectral images by svms optimized in the primal. IEEE Transactions on Geoscience and Remote Sensing 45 (6), 1870–1880. https://doi.org/10.1109/tgrs.2007.894550

Chiang, S.-S., Chang, C.-I., Ginsberg, I. W., 2000. Unsupervised hyperspectral image analysis using independent component analysis. In: Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International. Vol. 7. IEEE, pp. 3136–3138. https://doi.org/10.1109/igarss.2000.860361

Chutia, D., Bhattacharyya, D. K., Sarma, K. K., Kalita, R., Sudhakar, S., 2016. Hyperspectral Remote Sensing Classifications: A Perspective Survey. Transactions in GIS 20 (4), 463–490. https://doi.org/10.1111/tgis.12164

Dixit, V. S., Bhatia, S. K., 2013. Cross Project Validation for Refined Clusters Using Machine Learning Techniques. In: Computational Science and Its Applications - ICCSA. Springer, Berlin, Heidelberg, pp. 498–512. https://doi.org/10.1007/978-3-642-39643-4 36

Eismann, M. T., 2012. Hyperspectral Remote Sensing. SPIE. https://doi.org/10.1117/3.899758

Green, R. O., Eastwood, M. L., Sarture, C. M., Chrien, T. G., Aronsson, M., Chippendale, B. J., Faust, J. A., Pavri, B. E., Chovit, C. J., Solis, M., Olah, M. R., Williams, O., 1998. Imaging spectroscopy and the Airborne Visible /Infrared Imaging Spectrometer (AVIRIS). Remote Sensing of Environment 65 (3), 227–248. https://doi.org/10.1016/S0034-4257(98)00064-9

Ham, J., Chen, Y., Crawford, M. M., Ghosh, J., 2005. Investigation of the random forest framework for classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 43 (3), 492–501. https://doi.org/10.1109/tgrs.2004.842481

Haut, J. M., Bernabé, S., Paoletti, M. E., Fernandez-Beltran, R., Plaza, J., Plaza, A., Pla, F., 2018a. Low-high power consumption architectures for deep learning models applied to hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/LGRS.2018.2881045

Haut, J. M., Paoletti, M., Plaza, J., Plaza, A., 2016. Evaluación del rendimiento de una implementación Cloud para un clasificador neuronal aplicado a imágenes hiperespectrales. Actas Jornadas Sarteco, 127–134.

Haut, J. M., Paoletti, M., Plaza, J., Plaza, A., Jan 2017. Cloud implementation of the k-means algorithm for hyperspectral image analysis. The Journal of Supercomputing 73 (1), 514–529. https://doi.org/10.1007/s11227-016-1896-3

Haut, J. M., Paoletti, M. E., Plaza, J., Li, J., Plaza, A., Nov 2018b. Active learning with convolutional neural networks for hyperspectral image classification using a new bayesian approach. IEEE Transactions on Geoscience and Remote Sensing 56 (11), 6440–6461. https://doi.org/10.1109/TGRS.2018.2838665

Haut, J. M., Paoletti, M. E., Plaza, J., Plaza, A., 2018c. Fast dimensionality reduction and classification of hyperspectral images with extreme learning machines. Journal of Real-Time Image Processing, 1–24. https://doi.org/10.1007/s11554-018-0793-9

He, N., Paoletti, M. E., Fang, L., Li, S., Plaza, A., Plaza, J., et al., 2018. Feature extraction with multiscale covariance maps for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 1–15. https://doi.org/10.1109/tgrs.2018.2860464

Ifarraguerri, A., Chang, C.-I., 2000. Unsupervised hyperspectral image analysis with projection pursuit. IEEE Transactions on Geoscience and Remote Sensing 38 (6), 2529–2538. https://doi.org/10.1109/36.885200

Khodadadzadeh, M., Li, J., Plaza, A., Bioucas-Dias, J. M., 2014. A subspace-based multinomial logistic regression for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 11 (12), 2105–2109. https://doi.org/10.1109/lgrs.2014.2320258

Kingma, D. P., Ba, J., 2014. Adam: A method for stochastic optimization. ArXiv preprint arXiv:1412.6980.

Kokaly, R. F., Hoefen, T. M., Graham, G. E., Kelley, K. D., Johnson, M. R., Hubbard B. E., Goldfarb, R. J., Buchhorn, M., Prakash, A., 2016. Mineral information at micron to kilometer scales: Laboratory, field, and remote sensing imaging spectrometer data from the orange hill porphyry copper deposit, Alaska, USA. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). pp. 5418–5421. https://doi.org/10.1109/IGARSS.2016.7730411

Kunkel, B., Blechinger, F., Lutz, R., Doerffer, R., van der Piepen, H., 1988. ROSIS (Reflective Optics System Imaging Spectrometer) - A candidate instrument for polar platform missions. In: Seeley, J., Bowyer, S. (Eds.), Optoelectronic technologies for remote sensing from space. pp. 134–141. https://doi.org/10.1117/12.943611

Lawrence, R. L., Wood, S. D., Sheley, R. L., 2006. Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (randomForest). Remote Sensing of Environment 100 (3), 356 – 362. https://doi.org/10.1016/j.rse.2005.10.014

Lee, C. A., Gasster, S. D., Plaza, A., Chang, C. I., Huang, B., 2011. Recent developments in high performance computing for remote sensing: A review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4 (3), 508–527. https://doi.org/10.1109/JSTARS.2011.2162643

Li, J., Bioucas-Dias, J. M., Plaza, A., 2010. Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48 (11), 4085–4098. https://doi.org/10.1109/tgrs.2010.2060550

Liang, S., 2008. Advances in land remote sensing: system, modeling, inversion and application. Springer Science & Business Media.

Ma, W., Gong, C., Hu, Y., Meng, P., Xu, F., 2013. The Hughes phenomenon in hyperspectral classification based on the ground spectrum of grasslands in the region around Qinghai Lake. In: Proc. SPIE 8910, International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications, 89101G. pp. 89101G–89101G–11. https://doi.org/10.1117/12.2034457

Melgani, F., Bruzzone, L., 2004. Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on geoscience and remote sensing 42 (8), 1778–1790. https://doi.org/10.1109/tgrs.2004.831865

Mercier, G., Lennon, M., 2003. Support vector machines for hyperspectral image classification with spectral-based kernels. In: Geoscience and Remote Sensing Symposium, 2003. IGARSS’03. Proceedings. 2003 IEEE International. Vol. 1. IEEE, pp. 288–290. https://doi.org/10.1109/igarss.2003.1293752

Mountrakis, G., Im, J., Ogole, C., 2011. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing 66 (3), 247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001

Naidoo, L., Cho, M. A., Mathieu, R., Asner, G., 2012. Classification of savanna tree species, in the greater kruger national park region, by integrating hyperspectral and lidar data in a random forest data mining environment. ISPRS journal of Photogrammetry and Remote Sensing 69, 167–179. https://doi.org/10.1016/j.isprsjprs.2012.03.005

Nocedal, J., 1980. Updating Quasi-Newton Matrices With Limited Storage. Math. of Computation 35 (151), 773–782. https://doi.org/10.1090/s0025-5718-1980-0572855-7

Pal, M., Mather, P. M., 2004. Assessment of the effectiveness of support vector machines for hyperspectral data. Future Generation Computer Systems 20 (7), 1215–1225. https://doi.org/10.1016/j.future.2003.11.011

Paola, J. D., Schowengerdt, R. A., 1995. A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification. IEEE Transactions on Geoscience and Remote Sensing 33 (4), 981–996. https://doi.org/10.1109/36.406684

Paoletti, M., Haut, J., Plaza, J., Plaza, A., 2018. A new deep convolutional neural network for fast hyperspectral image classification. ISPRS Journal of Photogrammetry and Remote Sensing 145, 120 – 147, deep Learning RS Data. https://doi.org/10.1016/j.isprsjprs.2017.11.021

Plaza, A., Plaza, J., Paz, A., Sanchez, S., 2011. Parallel Hyperspectral Image and Signal Processing. IEEE Signal Processing Magazine 28 (3), 119–126. https://doi.org/10.1109/msp.2011.940409

Pour, A. B., Hashim, M., 2014. ASTER, ALI and Hyperion sensors data for lithological mapping and ore minerals exploration. SpringerPlus 3 (1), 130. https://doi.org/10.1186/2193-1801-3-130

Rodarmel, C., Shan, J., 2002. Principal component analysis for hyperspectral image classification. Surveying and Land Information Science 62 (2), 115–122.

Rumelhart, D. E., Hinton, G. E.,Williams, R. J., 1986. Learning representations by back-propagating errors. Nature 323 (6088), 533–536. https://doi.org/10.1038/323533a0

Schölkopf, B., Smola, A. J., Bach, F., et al., 2002. Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press. https://doi.org/10.7551/mitpress/4175.001.0001

Serpico, S. B., Bruzzone, L., Roli, F., 1996. An experimental comparison of neural and statistical non-parametric algorithms for supervised classification of remote-sensing images. Pattern Recognition Letters 17 (13), 1331 – 1341. https://doi.org/10.1016/S0167-8655(96)00090-6

Silverman, B., Jones, M. C., 1989. E. Fix and J.L. Hodges (1951): an important contribution to nonparametric discriminant analysis and density estimation. International Statistical Review 57 (3), 233–247. https://doi.org/10.2307/1403796

Theiler, J. P., Gisler, G., 1997. Contiguity-enhanced k-means clustering algorithm for unsupervised multispectral image segmentation. In: Algorithms, Devices, and Systems for Optical Information Processing. Vol. 3159. International Society for Optics and Photonics, pp. 108–119. https://doi.org/10.1117/12.279444

Vane, G., Evans, D. L., Kahle, A. B., 1989. Recent Advances In Airborne Terrestrial Remote Sensing With The Nasa Airborne Visible/infrared Imaging Spectrometer (aviris), Airborne Synthetic Aperture Radar (sar), And Thermal Infrared Multispectral Scanner (tims). In: 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium. pp. 942–943. https://doi.org/10.1109/IGARSS.1989.579044

Wang, F., 1990. Fuzzy classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing 28 (2), 194–201. https://doi.org/10.1109/36.46698

Yang, H., Du, Q., Chen, G., 2011. Unsupervised hyperspectral band selection using graphics processing units. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4 (3), 660–668. https://doi.org/10.1109/jstars.2011.2120598

Zhu, H., Basir, O., 2005. An adaptive fuzzy evidential nearest neighbor formulation for classifying remote sensing images. IEEE Transactions on Geoscience and Remote Sensing 43 (8), 1874–1889. https://doi.org/10.1109/TGRS.2005.848706

Abstract Views

1814
Metrics Loading ...

Metrics powered by PLOS ALM


 

Citado por (artículos incluidos en Crossref)

This journal is a Crossref Cited-by Linking member. This list shows the references that citing the article automatically, if there are. For more information about the system please visit Crossref site

1. A Deep Learning Approach for Fusing Sensor Data from Screw Compressors
Serafín Alonso, Daniel Pérez, Antonio Morán, Juan José Fuertes, Ignacio Díaz, Manuel Domínguez
Sensors  vol: 19  num.: 13  primera página: 2868  año: 2019  
doi: 10.3390/s19132868



Licencia Creative Commons

Esta revista se publica bajo unaLicencia Creative Commons Atribución 4.0 Internacional.

Universitat Politècnica de València     https://doi.org/10.4995/riai

e-ISSN: 1697-7920     ISSN: 1697-7912