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Earth remote sensing imagery classification using a multi-sensor super-resolution fusion algorithm
A.M. Belov 1, A.Y. Denisova 1

Samara National Research University, 34, Moskovskoye shosse, Samara, 443086, Russia

 PDF, 1143 kB

DOI: 10.18287/2412-6179-CO-735

Pages: 627-635.

Full text of article: Russian language.

Earth remote sensing data fusion is intended to produce images of higher quality than the original ones. However, the fusion impact on further thematic processing remains an open question because fusion methods are mostly used to improve the visual data representation. This article addresses an issue of the effect of fusion with increasing spatial and spectral resolution of data on thematic classification of images using various state-of-the-art classifiers and features extraction methods. In this paper, we use our own algorithm to perform multi-frame image fusion over optical remote sensing images with different spatial and spectral resolutions. For classification, we applied support vector machines and Random Forest algorithms. For features, we used spectral channels, extended attribute profiles and local feature attribute profiles. An experimental study was carried out using model images of four imaging systems. The resulting image had a spatial resolution of 2, 3, 4 and 5 times better than for the original images of each imaging system, respectively. As a result of our studies, it was revealed that for the support vector machines method, fusion was inexpedient since excessive spatial details had a negative effect on the classification. For the Random Forest algorithm, the classification results of a fused image were more accurate than for the original low-resolution images in 90% of cases. For example, for images with the smallest difference in spatial resolution (2 times) from the fusion result, the classification accuracy of the fused image was on average 4% higher. In addition, the results obtained for the Random Forest algorithm with fusion were better than the results for the support vector machines method without fusion. Additionally, it was shown that the classification accuracy of a fused image using the Random Forest method could be increased by an average of 9% due to the use of extended attribute profiles as features. Thus, when using data fusion, it is better to use the Random Forest classifier, whereas using fusion with the support vector machines method is not recommended.

image classification, data fusion, super-resolution, SVM, RF, EAP, LFAP.

Belov AM, Denisova AY. Earth remote sensing imagery classification using multi-sensor super-resolution algorithm. Computer Optics 2020; 44(4): 627-635. DOI: 10.18287/2412-6179-CO-735.

The work was partly funded by the Russian Foundation for Basic Research under project #18-07-00748.


  1. Belov AM, Denisova AY. Spectral and spatial super-resolution method for Earth remote sensing image fusion. Computer Optics 2018; 42(5): 855-863. DOI: 10.18287/2412-6179-2018-42-5-855-863.
  2. Tuia D, Volpi M, Dalla Mura M, Rakotomamonjy A, Flamary R. Automatic feature learning for spatio-spectral image classification with sparse SVM. IEEE Trans Geosci Remote Sens 2014; 52(10): 6062-6074.
  3. Belgiu M, Drăguţ L. Random forest in remote sensing: A review of applications and future directions. ISPRS J Photogramm Remote Sens 2016; 114: 24-31.
  4. Li M, Ma L, Blaschke T, Cheng L, Tiede D. A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments. Int J Appl Earth Obs Geoinf 2016; 49: 87-98.
  5. Khatami R, Mountrakis G, Stehman SV. A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote Sens Environ 2016; 177: 89-100.
  6. García MA, Moutahir H, Casady GM, Bautista S, Rodríguez F. Using hidden markov models for land surface phenology: An evaluation across a range of land cover types in southeast spain. Remote Sens 2019; 11(5): 507.
  7. Liao W, Dalla Mura M, Chanussot J, Pižurica A. Fusion of spectral and spatial information for classification of hyperspectral remote-sensed imagery by local graph. IEEE J Sel Top Appl Earth Obs Remote Sens 2015; 9(2): 583-594.
  8. Pham MT, Lefèvre S, Aptoula E. Local feature-based attribute profiles for optical remote sensing image classification. IEEE Trans Geosci Remote Sens 2017; 56(2): 1199-1212.
  9. Pham MT, Aptoula E, Lefèvre S. Classification of remote sensing images using attribute profiles and feature profiles from different trees: a comparative study. IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium 2018: 4511-4514.
  10. Pham M-T, Lefèvre S, Aptoula E, Bruzzone L. Recent developments from attribute profiles for remote sensing image classification. Source: <https://arxiv.org/abs/1803.10036>.
  11. Hong D, Wu X, Ghamisi P, Chanussot J, Yokoya N, Zhu XX. Invariant attribute profiles: A spatial-frequency joint feature extractor for hyperspectral image classification. IEEE Trans Geosci Remote Sens 2019: 1-18. DOI: 10.1109/TGRS.2019.2957251.
  12. Farsiu S, Robinson MD, Elad M, Milanfar P. Fast and robust multiframe super resolution. IEEE Trans Image Process 2004; 13(10): 1327-1344. DOI: 10.1109/TIP.2004.834669.
  13. Farsiu S, Robinson MD, Elad M, Milanfar P. Fast and robust super-resolution. Proceedings of the 2003 International Conference on Image Processing 2003; 3: 291-294. – DOI: 10.1109/ICIP.2003.1246674.
  14. Hyperspectral remote sensing scenes. Source: <http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes>.
  15. Marpu PR, Pedergnana M., Dalla Mura M; Benediktsson JA, Bruzzone L. Automatic generation of standard deviation attribute profiles for spectral-spatial classification of remote sensing data. IEEE Geosci Remote Sens Lett 2013: 10(2): 293-297.
  16. Li J, Huang X, Gamba P, Bioucas-Dias JM, Zhang L, Benediktsson JA, Plaza A. Multiple Feature Learning for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing 2015; 53(3): 1592-1606.

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