Classification of the Complex Agricultural Planting Structure with a Semi-Supervised Extreme Learning Machine Framework
Many approaches have been developed to analyze remote sensing images. However, for the classification of large-scale problems, most algorithms showed low computational efficiency and low accuracy. In this paper, the newly developed semi-supervised extreme learning machine (SS-ELM) framework with k-m...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/22/3708 |
| _version_ | 1797548152745623552 |
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| author | Ziyi Feng Guanhua Huang Daocai Chi |
| author_facet | Ziyi Feng Guanhua Huang Daocai Chi |
| author_sort | Ziyi Feng |
| collection | DOAJ |
| description | Many approaches have been developed to analyze remote sensing images. However, for the classification of large-scale problems, most algorithms showed low computational efficiency and low accuracy. In this paper, the newly developed semi-supervised extreme learning machine (SS-ELM) framework with k-means clustering algorithm for image segmentation and co-training algorithm to enlarge the sample sets was used to classify the agricultural planting structure at large-scale areas. Data sets collected from a small-scale area within the Hetao Irrigation District (HID) at the upper reaches of the Yellow River basin were used to evaluate the SS-ELM framework. The results of the SS-ELM algorithm were compared with those of the random forest (RF), ELM, support vector machine (SVM) and semi-supervised support vector machine (S-SVM) algorithms. Then the SS-ELM algorithm was applied to analyze the complex planting structure of HID in 1986–2010 by comparing the remote sensing estimated results with the statistical data. In the small-scale case, the SS-ELM algorithm performed better than the RF, ELM, SVM, and S-SVM algorithms. For the SS-ELM algorithm, the average overall accuracy (OA) was in a range of 83.00–92.17%. On the contrary, for the other four algorithms, their average OA values ranged from 56.97% to 92.84%. Whereas, in the classification of planting structure in HID, the SS-ELM algorithm had an excellent performance in classification accuracy and computational efficiency for three major planting crops including maize, wheat, and sunflowers. The estimated areas by using the SS-ELM algorithm based on the remote sensing images were consistent with the statistical data, and their difference was within a range of 3–25%. This implied that the SS-ELM framework could be served as an effective method for the classification of complex planting structures with relatively fast training, good generalization, universal approximation capability, and reasonable learning accuracy. |
| first_indexed | 2024-03-10T14:55:26Z |
| format | Article |
| id | doaj.art-f597982fb501445b8fb6227e0a494dad |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T14:55:26Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f597982fb501445b8fb6227e0a494dad2023-11-20T20:40:32ZengMDPI AGRemote Sensing2072-42922020-11-011222370810.3390/rs12223708Classification of the Complex Agricultural Planting Structure with a Semi-Supervised Extreme Learning Machine FrameworkZiyi Feng0Guanhua Huang1Daocai Chi2College of Water Conservancy, Shenyang Agricultural University, Shenyang 110866, ChinaChinese-Israeli International Center for Research and Training in Agriculture, China Agricultural University, Beijing 100083, ChinaCollege of Water Conservancy, Shenyang Agricultural University, Shenyang 110866, ChinaMany approaches have been developed to analyze remote sensing images. However, for the classification of large-scale problems, most algorithms showed low computational efficiency and low accuracy. In this paper, the newly developed semi-supervised extreme learning machine (SS-ELM) framework with k-means clustering algorithm for image segmentation and co-training algorithm to enlarge the sample sets was used to classify the agricultural planting structure at large-scale areas. Data sets collected from a small-scale area within the Hetao Irrigation District (HID) at the upper reaches of the Yellow River basin were used to evaluate the SS-ELM framework. The results of the SS-ELM algorithm were compared with those of the random forest (RF), ELM, support vector machine (SVM) and semi-supervised support vector machine (S-SVM) algorithms. Then the SS-ELM algorithm was applied to analyze the complex planting structure of HID in 1986–2010 by comparing the remote sensing estimated results with the statistical data. In the small-scale case, the SS-ELM algorithm performed better than the RF, ELM, SVM, and S-SVM algorithms. For the SS-ELM algorithm, the average overall accuracy (OA) was in a range of 83.00–92.17%. On the contrary, for the other four algorithms, their average OA values ranged from 56.97% to 92.84%. Whereas, in the classification of planting structure in HID, the SS-ELM algorithm had an excellent performance in classification accuracy and computational efficiency for three major planting crops including maize, wheat, and sunflowers. The estimated areas by using the SS-ELM algorithm based on the remote sensing images were consistent with the statistical data, and their difference was within a range of 3–25%. This implied that the SS-ELM framework could be served as an effective method for the classification of complex planting structures with relatively fast training, good generalization, universal approximation capability, and reasonable learning accuracy.https://www.mdpi.com/2072-4292/12/22/3708remote sensingimage segmentationextreme-learning machineland usecropping pattern |
| spellingShingle | Ziyi Feng Guanhua Huang Daocai Chi Classification of the Complex Agricultural Planting Structure with a Semi-Supervised Extreme Learning Machine Framework Remote Sensing remote sensing image segmentation extreme-learning machine land use cropping pattern |
| title | Classification of the Complex Agricultural Planting Structure with a Semi-Supervised Extreme Learning Machine Framework |
| title_full | Classification of the Complex Agricultural Planting Structure with a Semi-Supervised Extreme Learning Machine Framework |
| title_fullStr | Classification of the Complex Agricultural Planting Structure with a Semi-Supervised Extreme Learning Machine Framework |
| title_full_unstemmed | Classification of the Complex Agricultural Planting Structure with a Semi-Supervised Extreme Learning Machine Framework |
| title_short | Classification of the Complex Agricultural Planting Structure with a Semi-Supervised Extreme Learning Machine Framework |
| title_sort | classification of the complex agricultural planting structure with a semi supervised extreme learning machine framework |
| topic | remote sensing image segmentation extreme-learning machine land use cropping pattern |
| url | https://www.mdpi.com/2072-4292/12/22/3708 |
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