Abstract
Main approaches to object edge detection in hyperspectral images are considered. Algorithms are presented for the edge detection in spectral–selective objects based on spatial–spectral correlation and interspectral difference of gradients. The proposed algorithms are shown to be efficient in the processing of real hyperspectral images with additive Gaussian noise.
Similar content being viewed by others
REFERENCES
A. N. Vinogradov, V. V. Egorov, A. P. Kalinin, A. I. Rodionov, and I. D. Rodionov, ‘‘A line of aviation hyperspectrometers in the UV, visible, and near-IR ranges,’’ J. Opt. Technol. 88, 237–243 (2016). https://doi.org/10.1364/JOT.83.000237
V. E. Pozhar, A. A. Balashov, and M. F. Bulatov, ‘‘Modern spectral optical instruments developed in Scientific Technological Center of Unique Instrumentation of Russian Academy of Sciences,’’ Nauchn. Priborostr. 28 (4), 49–57 (2018). https://doi.org/10.18358/np-28-4-i4957
P. M. Yukhno, S. M. Ogreb, and M. V. Tishaninov, ‘‘Statistical synthesis of a hyperspectral detector,’’ Optoelectron., Instrum. Data Process. 51, 264–271 (2015). https://doi.org/10.3103/S8756699015030085
L. A. Demidova, R. V. Tishkin, and S. V. Trukhanov, ‘‘Algorithms for identification of hyperspectral characteristics of objects in problems of Earth’s remote probing,’’ Tsifrovaya Obrab. Signalov, No. 3, pp. 30–37 (2014).
A. N. Vinogradov, V. V. Egorov, A. P. Kalinin, A. I. Rodionov, I. D. Rodionova, and I. P. Rodionova, ‘‘Studying the capabilities of hyperspectral detection for monitoring the state of water objects,’’ Sovrem. Probl. Distantsionnogo Zondirov. Zemli Kosmosa 14 (2), 125–134 (2017).https://doi.org/10.21046/2070-7401-2017-14-2-125-134
S. M. Borzov and O. I. Potaturkin, ‘‘Spectral-spatial methods for hyperspectral image classification. Review,’’ Optoelectron., Instrum. Data Process. 54, 582–599 (2018). https://doi.org/10.3103/S8756699018060079
R. C. Gonzalez and R. E. Woods, Digital Image Processing (Prentice Hall, 2002).
N. V. Kim, Processing and Analysis of Images in Technical Vision Systems: Handbook (Mosk. Aviats. Inst., Moscow, 2014).
Image Processing in Aviation Technical Vision Systems, Ed. by L. N. Kostyashkin and M. B. Nikiforov (Fizmatlit, Moscow, 2016).
R. A. Shovengerdt, Remote Sensing: Models and Methods for Image Processing (Tekhnosfera, Moscow, 2013).
Modern Technologies of Data Processing for Remote Sensing of the Earth, Ed. by V. V. Eremeev (Fizmatlit, Moscow, 2015).
Perspective Information Technologies of Remote Sensing of the Earth, Ed. by V. A. Soifer (Novaya Tekhnika, Samara, 2015).
T. A. Sheremet’eva, G. N. Filippov, and A. M. Malov, ‘‘Using the target-visualization method to process hyperspectral images,’’ J. Opt. Technol. 82, 24–27 (2015). https://doi.org/10.1364/JOT.82.000024
V. V. Shipko, ‘‘Noise filtration in hyperspectral images,’’ Optoelectron., Instrum. Data Process. 56, 19–27 (2020). https://doi.org/10.3103/S8756699020010033
V. E. Pozhar, A. S. Machikhin, M. I. Gaponov, S. V. Shirokov, M. M. Mikhailov, and A. E. Sheryshev, ‘‘Hyperspectrometer based on restructured acustooptic filters for UAVs,’’ Svetotekhnika, No. 4, 47–50 (2018).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
The authors declare that they have no conflicts of interest.
Additional information
Translated by V. Arutyunyan
About this article
Cite this article
Shipko, V.V., Samoilin, E.A., Pozhar, V.E. et al. Edge Detection in Hyperspectral Images. Optoelectron.Instrument.Proc. 57, 618–625 (2021). https://doi.org/10.3103/S8756699021060145
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.3103/S8756699021060145