A Survey on Hyperspectral Image Classification and Object Detection Techniques

Authors

  • Parul Bhanarkar  Department of Computer Science & Engineering, Babasaheb Naik College of Engineering, Pusad, Maharashtra, India.
  • Dr. Salim Y. Amdani  Department of Computer Science & Engineering, Babasaheb Naik College of Engineering, Pusad, Maharashtra, India.

Keywords:

Machine Learning, Deep Learning, Image classification, Hyperspectral images, Object detection

Abstract

Machine Learning is vast field which finds its application in almost every field. The image classification is one of the important application of Supervised Machine learning algorithms. Image classification is basically concerned with identifying the objects in the images. The complexity of this task is dependent on the image features and type of images. For the research work here, the hyperspectral images are considered for deep learning based image classification. The object detection in the Hyperspectral images have applications in various areas including defense, precision agriculture, atmospheric analysis, environmental analysis, anomaly detection, fraud detection , etc. The work presented here is divided into broad survey of image classification methods using machine learning and deep learning methods. Continuing with this work, the further work presents object detection methods in ML and DL. The later work presents the deep review of the research articles over Hyperspectral image classification using Machine Learning and Deep Learning Algorithms. A lot of challenges are present to solve the object detection problems in Hyperspectral images. The later section of this work describes the object detection based on Hyperspectral images survey in detail highlighting the major developments.

References

  1. B. Borasca, L. Bruzzone, L. Carlin and M. Zusi, "A fuzzy-input fuzzy-output SVM technique for classification of hyperspectral remote sensing images," Proceedings of the 7th Nordic Signal Processing Symposium - NORSIG 2006, 2006, pp. 2-5, doi: 10.1109/NORSIG.2006.275261.
  2. F. Melgani and L. Bruzzone, "Classification of hyperspectral remote sensing images with support vector machines," in IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 8, pp. 1778-1790, Aug. 2004, doi: 10.1109/TGRS.2004.831865.
  3. X. Wang and Y. Feng, "New Method Based on Support Vector Machine in Classification for Hyperspectral Data," 2008 International Symposium on Computational Intelligence and Design, 2008, pp. 76-80, doi: 10.1109/ISCID.2008.61.
  4. N. Alajlan, Y. Bazi, H. AlHichri and E. Othman, "Robust classification of hyperspectral images based on the combination of supervised and unsupervised learning paradigms," 2012 IEEE International Geoscience and Remote Sensing Symposium, 2012, pp. 1417-1420, doi: 10.1109/IGARSS.2012.6351270.
  5. A. Villa, J. Chanussot, J.A. Benediktsson, C. Jutten, R. Dambreville, “Unsupervised methods for the classification of hyperspectral images with low spatial resolution,” Pattern Recognition, Volume 46, Issue 6, 2013, Pages 1556-1568, ISSN 0031-3203,doi.org/10.1016/j.patcog.2012.10.030.
  6. A. Samat, P. Du, S. Liu, J. Li and L. Cheng, "E2LMs : Ensemble Extreme Learning Machines for Hyperspectral Image Classification," in IEEE Journal of Selected Topics in Applied Earth observations and Remote Sensing, vol. 7, no. 4, pp. 1060-1069, April 2014, doi: 10.1109/JSTARS.2014.2301775.
  7. Yanfeng Gu, Huan Liu, “Sample-screening MKL method via boosting strategy for hyperspectral image classification”, Neurocomputing, Volume 173, Part 3, 2016, Pages 1630-1639, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2015.09.035.
  8. Maryam Imani, Hassan Ghassemian,"An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges", Information Fusion, Volume 59, 2020, Pages 59-83, ISSN 1566-2535, doi:10.1016/j.inffus.2020.01.007.
  9. Gizem Ortac, Giyasettin Ozcan,”Comparative study of hyperspectral image classification by multidimensional Convolutional Neural Network approaches to improve accuracy”, Expert Systems with Applications, Volume 182, 2021, 115280, ISSN 0957-4174, doi:10.1016/j.eswa.2021.115280.
  10. Sezer Kutluk, Koray Kayabol, Aydin Akan,”A new CNN training approach with application to hyperspectral image classification”, Digital Signal Processing, Volume 113, 2021, 103016, ISSN 1051-2004, doi:10.1016/j.dsp.2021.103016.
  11. Park, KS., Cho, S.H., Hong, S. et al. Real-Time Target Detection Architecture Based on Reduced Complexity Hyperspectral Processing. EURASIP J. Adv. Signal Process. 2008, 438051 (2008). Doi:10.1155/2008/438051.
  12. Zhang, B., Yang, W., Gao, L. et al. Real-time target detection in hyperspectral images based on spatial-spectral information extraction. EURASIP J. Adv. Signal Process. 2012, 142 (2012). https://doi.org/10.1186/1687-6180-2012-142
  13. J. Liu, Z. Wu, Z. Xiao and J. Yang, "Region-Based Relaxed Multiple Kernel Collaborative Representation for Hyperspectral Image Classification," in IEEE Access, vol. 5, pp. 20921-20933, 2017, doi: 10.1109/ACCESS.2017.2758168.
  14. N. Imamoglu et al., "Hyperspectral Image Dataset for Benchmarking on Salient Object Detection," 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX), 2018, pp. 1-3, doi: 10.1109/QoMEX.2018.8463428.
  15. Ma, L., Lu, G., Wang, D. et al. Adaptive deep learning for head and neck cancer detection using hyperspectral imaging. Vis. Comput. Ind. Biomed. Art 2, 18 (2019). https://doi.org/10.1186/s42492-019-0023-8
  16. X. Sun, H. Zhang, F. Xu, Y. Zhu and X. Fu, "Constrained-Target Band Selection with Subspace Partition for Hyperspectral Target Detection," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2021.3109455.
  17. Y. Liang, P. P. Markopoulos and E. S. Saber, "Subpixel target detection in hyperspectral images with local matched filtering in SLIC superpixels," 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2016, pp. 1-5, doi: 10.1109/WHISPERS.2016.8071719.
  18. J. Hao, J. Li, C. Pan, C. Huang, T. Xu and H. Cheng, "Leveraging Global and Background Contrast for Salient Object Detection in Hyperspectral Images," 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), 2020, pp. 2186-2190, doi: 10.1109/ICMCCE51767.2020.00474.

Downloads

Published

2022-01-30

Issue

Section

Research Articles

How to Cite

[1]
Parul Bhanarkar, Dr. Salim Y. Amdani, " A Survey on Hyperspectral Image Classification and Object Detection Techniques , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 1, pp.236-249, January-February-2022.