Ensembles of spectral-spatial convolutional neural network models for classifying soil types in hyperspectral images
The paper presents a study of various approaches to the classification of soil covers based on neural network algorithms using hyperspectral remote and proximal sensing of the Earth. The spectral distributions were recorded in the laboratory using an Offner imaging scanning hyperspectrometer. Spectr...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Samara National Research University
2023-10-01
|
| Series: | Компьютерная оптика |
| Subjects: | |
| Online Access: | https://www.computeroptics.ru/eng/KO/Annot/KO47-5/470514e.html |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832590921971007488 |
|---|---|
| author | N.A. Firsov V.V. Podlipnov N.A. Ivliev D.D. Ryskova A.V. Pirogov A.A. Muzyka A.R. Makarov V.E. Lobanov V.I. Platonov A.N. Babichev V.A. Monastyrskiy V.I. Olgarenko D.P. Nikolaev R.V. Skidanov A.V. Nikonorov N.L. Kazanskiy V.A. Soyfer |
| author_facet | N.A. Firsov V.V. Podlipnov N.A. Ivliev D.D. Ryskova A.V. Pirogov A.A. Muzyka A.R. Makarov V.E. Lobanov V.I. Platonov A.N. Babichev V.A. Monastyrskiy V.I. Olgarenko D.P. Nikolaev R.V. Skidanov A.V. Nikonorov N.L. Kazanskiy V.A. Soyfer |
| author_sort | N.A. Firsov |
| collection | DOAJ |
| description | The paper presents a study of various approaches to the classification of soil covers based on neural network algorithms using hyperspectral remote and proximal sensing of the Earth. The spectral distributions were recorded in the laboratory using an Offner imaging scanning hyperspectrometer. Spectral-spatial characteristics of nine soil samples from various parts of a farming land in the Samara region were experimentally studied. Using a method of energy dispersion microanalysis, the correspondence between the hyperspectral data and the chemical composition of the samples taken was established. Based on the data obtained, a neural network-aided classification of soil samples was implemented depending on the content of constituent elements such as carbon and calcium. A normalized spectral-spatial convolutional neural network was used as a classifier. As a result of the work, an approach to the classification of high-resolution hyper-spectral images based on the refinement of a multiclass convolutional neural network using an ensemble of binary classifiers is proposed. It is shown that the classification of soil samples by carbon and calcium content is carried out with an accuracy of 0.96. |
| format | Article |
| id | doaj-art-e2fd60068150487e80c3f952c7c90f1f |
| institution | Kabale University |
| issn | 0134-2452 2412-6179 |
| language | English |
| publishDate | 2023-10-01 |
| publisher | Samara National Research University |
| record_format | Article |
| series | Компьютерная оптика |
| spelling | doaj-art-e2fd60068150487e80c3f952c7c90f1f2025-01-23T05:49:41ZengSamara National Research UniversityКомпьютерная оптика0134-24522412-61792023-10-0147579580510.18287/2412-6179-CO-1260Ensembles of spectral-spatial convolutional neural network models for classifying soil types in hyperspectral imagesN.A. Firsov0 V.V. Podlipnov1N.A. Ivliev2D.D. Ryskova3A.V. Pirogov4A.A. Muzyka5A.R. Makarov6V.E. Lobanov7V.I. Platonov8A.N. Babichev9V.A. Monastyrskiy10V.I. Olgarenko11D.P. Nikolaev12R.V. Skidanov13A.V. Nikonorov14N.L. Kazanskiy15V.A. Soyfer16IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS; Samara National Research UniversityIPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS; Samara National Research UniversityIPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS; Samara National Research UniversitySamara National Research UniversitySamara National Research University; IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RASSamara National Research University; IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RASSamara National Research University; IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RASSamara National Research University; Adyghe State UniversitySamara National Research UniversitySamara National Research UniversitySamara National Research UniversitySamara National Research UniversityInstitute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute)IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS; Samara National Research UniversityIPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS; Samara National Research UniversityIPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS; Samara National Research UniversityIPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS; Samara National Research UniversityThe paper presents a study of various approaches to the classification of soil covers based on neural network algorithms using hyperspectral remote and proximal sensing of the Earth. The spectral distributions were recorded in the laboratory using an Offner imaging scanning hyperspectrometer. Spectral-spatial characteristics of nine soil samples from various parts of a farming land in the Samara region were experimentally studied. Using a method of energy dispersion microanalysis, the correspondence between the hyperspectral data and the chemical composition of the samples taken was established. Based on the data obtained, a neural network-aided classification of soil samples was implemented depending on the content of constituent elements such as carbon and calcium. A normalized spectral-spatial convolutional neural network was used as a classifier. As a result of the work, an approach to the classification of high-resolution hyper-spectral images based on the refinement of a multiclass convolutional neural network using an ensemble of binary classifiers is proposed. It is shown that the classification of soil samples by carbon and calcium content is carried out with an accuracy of 0.96.https://www.computeroptics.ru/eng/KO/Annot/KO47-5/470514e.htmlhyperspectral imageshyperspectral sensingproximal sensingconvolutional neural networksspectral-spatial classificationsoil cartography |
| spellingShingle | N.A. Firsov V.V. Podlipnov N.A. Ivliev D.D. Ryskova A.V. Pirogov A.A. Muzyka A.R. Makarov V.E. Lobanov V.I. Platonov A.N. Babichev V.A. Monastyrskiy V.I. Olgarenko D.P. Nikolaev R.V. Skidanov A.V. Nikonorov N.L. Kazanskiy V.A. Soyfer Ensembles of spectral-spatial convolutional neural network models for classifying soil types in hyperspectral images Компьютерная оптика hyperspectral images hyperspectral sensing proximal sensing convolutional neural networks spectral-spatial classification soil cartography |
| title | Ensembles of spectral-spatial convolutional neural network models for classifying soil types in hyperspectral images |
| title_full | Ensembles of spectral-spatial convolutional neural network models for classifying soil types in hyperspectral images |
| title_fullStr | Ensembles of spectral-spatial convolutional neural network models for classifying soil types in hyperspectral images |
| title_full_unstemmed | Ensembles of spectral-spatial convolutional neural network models for classifying soil types in hyperspectral images |
| title_short | Ensembles of spectral-spatial convolutional neural network models for classifying soil types in hyperspectral images |
| title_sort | ensembles of spectral spatial convolutional neural network models for classifying soil types in hyperspectral images |
| topic | hyperspectral images hyperspectral sensing proximal sensing convolutional neural networks spectral-spatial classification soil cartography |
| url | https://www.computeroptics.ru/eng/KO/Annot/KO47-5/470514e.html |
| work_keys_str_mv | AT nafirsov ensemblesofspectralspatialconvolutionalneuralnetworkmodelsforclassifyingsoiltypesinhyperspectralimages AT vvpodlipnov ensemblesofspectralspatialconvolutionalneuralnetworkmodelsforclassifyingsoiltypesinhyperspectralimages AT naivliev ensemblesofspectralspatialconvolutionalneuralnetworkmodelsforclassifyingsoiltypesinhyperspectralimages AT ddryskova ensemblesofspectralspatialconvolutionalneuralnetworkmodelsforclassifyingsoiltypesinhyperspectralimages AT avpirogov ensemblesofspectralspatialconvolutionalneuralnetworkmodelsforclassifyingsoiltypesinhyperspectralimages AT aamuzyka ensemblesofspectralspatialconvolutionalneuralnetworkmodelsforclassifyingsoiltypesinhyperspectralimages AT armakarov ensemblesofspectralspatialconvolutionalneuralnetworkmodelsforclassifyingsoiltypesinhyperspectralimages AT velobanov ensemblesofspectralspatialconvolutionalneuralnetworkmodelsforclassifyingsoiltypesinhyperspectralimages AT viplatonov ensemblesofspectralspatialconvolutionalneuralnetworkmodelsforclassifyingsoiltypesinhyperspectralimages AT anbabichev ensemblesofspectralspatialconvolutionalneuralnetworkmodelsforclassifyingsoiltypesinhyperspectralimages AT vamonastyrskiy ensemblesofspectralspatialconvolutionalneuralnetworkmodelsforclassifyingsoiltypesinhyperspectralimages AT violgarenko ensemblesofspectralspatialconvolutionalneuralnetworkmodelsforclassifyingsoiltypesinhyperspectralimages AT dpnikolaev ensemblesofspectralspatialconvolutionalneuralnetworkmodelsforclassifyingsoiltypesinhyperspectralimages AT rvskidanov ensemblesofspectralspatialconvolutionalneuralnetworkmodelsforclassifyingsoiltypesinhyperspectralimages AT avnikonorov ensemblesofspectralspatialconvolutionalneuralnetworkmodelsforclassifyingsoiltypesinhyperspectralimages AT nlkazanskiy ensemblesofspectralspatialconvolutionalneuralnetworkmodelsforclassifyingsoiltypesinhyperspectralimages AT vasoyfer ensemblesofspectralspatialconvolutionalneuralnetworkmodelsforclassifyingsoiltypesinhyperspectralimages |