Abstract
Most hyperspectral images (HSI) have important spectral features in specific combination of wave numbers or channels. Noise in these specific channels or bands can easily overwhelm these relevant spectral features. Maximum Noise Fraction (MNF) by Green et al. [1] has been extensively studied for noise removal in HSI data. The MNF transform maximizes the Signal to Noise Ratio (SNR) in feature space, thereby explicitly requiring an estimation of the HSI noise. We present two simple and efficient Noise Covariance Matrix (NCM) estimation methods as required for the MNF transform. Our NCM estimations improve the performance of HSI classification, even when ground objects are mixed. Both techniques rely on a superpixel based clustering of HSI data in the spatial domain. The novelty of our NCM’s comes from their reduced sensitivity to HSI noise distributions and interference patterns. Experiments with both simulated and real HSI data show that our methods significantly outperforms the NCM estimation in the classical MNF transform, as well as against more recent state of the art NCM estimation methods. We quantify this improvement in terms of HSI classification accuracy and superior recovery of spectral features.
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Notes
- 1.
Code available at: http://ivrl.epfl.ch/research/superpixels.
- 2.
Data available at http://aviris.jpl.nasa.gov/html/aviris.freedata.html.
- 3.
P is the number of pixels grouped in a superpixel.
References
Green, A.A., Berman, M., Switzer, P., Craig, M.D.: A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans. Geosci. Remote Sens. 26(1), 65–74 (1988)
Chang, C.-I.: Hyperspectral Imaging: Techniques for Spectral Detection and Classification, vol. 1. Springer, New York (2003). https://doi.org/10.1007/978-1-4419-9170-6
Shippert, P.: Why use hyperspectral imagery? Photogram. Eng. Remote Sens. 70(4), 377–396 (2004)
Resonon: hyperspectral imaging applications, May 2017. https://www.resonon.com/applications_main.html
Corner, B., Narayanan, R., Reichenbach, S.: Noise estimation in remote sensing imagery using data masking. Int. J. Remote Sens. 24(4), 689–702 (2003)
Lee, J.B., Woodyatt, A.S., Berman, M.: Enhancement of high spectral resolution remote-sensing data by a noise-adjusted principal components transform. IEEE Trans. Geosci. Remote Sens. 28(3), 295–304 (1990)
Switzer, P., Green, A.A.: Min/max autocorrelation factors for multivariate spatial imagery. Comput. Sci. Stat. 16, 13–16 (1984)
Nielsen, A.A.: Analysis of regularly and irregularly sampled spatial, multivariate, and multi-temporal data. Science 21(4), 555–567 (1994)
Liu, X., Zhang, B., Gao, L., Chen, D.: A maximum noise fraction transform with improved noise estimation for hyperspectral images. Sci. Chin. Ser. F: Inf. Sci. 52(9), 1578–1587 (2009)
Gao, L., Zhang, B., Sun, X., Li, S., Du, Q., Wu, C.: Optimized maximum noise fraction for dimensionality reduction of Chinese HJ-1A hyperspectral data. EURASIP J. Adv. Sig. Process. 2013(1), 65 (2013)
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Roger, R.: Principal components transform with simple, automatic noise adjustment. Int. J. Remote Sens. 17(14), 2719–2727 (1996)
Greco, M., Diani, M., Corsini, G.: Analysis of the classification accuracy of a new mnf based feature extraction algorithm. In: Remote Sensing, p. 63 650V. International Society for Optics and Photonics (2006)
Chartered Institute of Taxation. Jet Propulsion Lab: AVIRIS data - ordering free AVIRIS standard data products, July 2017. http://aviris.jpl.nasa.gov/html/aviris.freedata.html
Zhang, L., Zhang, L., Bovik, A.C.: A feature-enriched completely blind image quality evaluator. IEEE Trans. Image Process. 24(8), 2579–2591 (2015)
Acknowledgment
This research was supported in part by National Institute of Health (NIH) grants R01 GM117594 and R41 GM116300.
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Gupta, S., Bajaj, C. (2018). Efficient Clustering-Based Noise Covariance Estimation for Maximum Noise Fraction. In: Rameshan, R., Arora, C., Dutta Roy, S. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-0020-2_21
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DOI: https://doi.org/10.1007/978-981-13-0020-2_21
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