Extended morphological profiles analysis of airborne hyperspectral image classification using machine learning algorithms
When morphological capabilities are used for the class of high decision hyperspectral photographs from metropolitan areas, one must not forget two crucial problems. Among which the primary one is that traditional morphological openings and closings degrade the object obstacles and distorts the items...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2021-01-01
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| Series: | International Journal of Intelligent Networks |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666603021000014 |
| _version_ | 1818436316344352768 |
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| author | R. Anand S. Veni P. Geetha S. Rama Subramoniam |
| author_facet | R. Anand S. Veni P. Geetha S. Rama Subramoniam |
| author_sort | R. Anand |
| collection | DOAJ |
| description | When morphological capabilities are used for the class of high decision hyperspectral photographs from metropolitan areas, one must not forget two crucial problems. Among which the primary one is that traditional morphological openings and closings degrade the object obstacles and distorts the items shape. Morphological profiles (MP) opening and closing via reconstruction can keep us away from this problem, however this system ends in a few unwanted consequences. In this paper, first check out morphological summaries with subjective restoration and steering MPs for the classification of excessive decision hyperspectral snap shots from city areas. Secondly, broaden a supervised face extraction to lessen the dimensionality of the engendered morphological profiles for the prediction. |
| first_indexed | 2024-12-14T17:06:50Z |
| format | Article |
| id | doaj.art-226c0a04e6484685bc5c9e01e84f4264 |
| institution | Directory Open Access Journal |
| issn | 2666-6030 |
| language | English |
| last_indexed | 2024-12-14T17:06:50Z |
| publishDate | 2021-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Intelligent Networks |
| spelling | doaj.art-226c0a04e6484685bc5c9e01e84f42642022-12-21T22:53:41ZengKeAi Communications Co., Ltd.International Journal of Intelligent Networks2666-60302021-01-01216Extended morphological profiles analysis of airborne hyperspectral image classification using machine learning algorithmsR. Anand0S. Veni1P. Geetha2S. Rama Subramoniam3Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India; Corresponding author.Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, IndiaDepertment of CEN, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, IndiaRRSC (South), NRSC/ISRO, ISITE Campus, Bengaluru, 560 037, Karnataka, IndiaWhen morphological capabilities are used for the class of high decision hyperspectral photographs from metropolitan areas, one must not forget two crucial problems. Among which the primary one is that traditional morphological openings and closings degrade the object obstacles and distorts the items shape. Morphological profiles (MP) opening and closing via reconstruction can keep us away from this problem, however this system ends in a few unwanted consequences. In this paper, first check out morphological summaries with subjective restoration and steering MPs for the classification of excessive decision hyperspectral snap shots from city areas. Secondly, broaden a supervised face extraction to lessen the dimensionality of the engendered morphological profiles for the prediction.http://www.sciencedirect.com/science/article/pii/S2666603021000014Hyperspectral imageMachine learningEmpirical morphological profilesSupport vector machineClassificationWavelength |
| spellingShingle | R. Anand S. Veni P. Geetha S. Rama Subramoniam Extended morphological profiles analysis of airborne hyperspectral image classification using machine learning algorithms International Journal of Intelligent Networks Hyperspectral image Machine learning Empirical morphological profiles Support vector machine Classification Wavelength |
| title | Extended morphological profiles analysis of airborne hyperspectral image classification using machine learning algorithms |
| title_full | Extended morphological profiles analysis of airborne hyperspectral image classification using machine learning algorithms |
| title_fullStr | Extended morphological profiles analysis of airborne hyperspectral image classification using machine learning algorithms |
| title_full_unstemmed | Extended morphological profiles analysis of airborne hyperspectral image classification using machine learning algorithms |
| title_short | Extended morphological profiles analysis of airborne hyperspectral image classification using machine learning algorithms |
| title_sort | extended morphological profiles analysis of airborne hyperspectral image classification using machine learning algorithms |
| topic | Hyperspectral image Machine learning Empirical morphological profiles Support vector machine Classification Wavelength |
| url | http://www.sciencedirect.com/science/article/pii/S2666603021000014 |
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