Soil Classification Based on Deep Learning Algorithm and Visible Near-Infrared Spectroscopy
Changes in land cover will cause the changes in the climate and environmental characteristics, which has an important influence on the social economy and ecosystem. The main form of land cover is different types of soil. Compared with traditional methods, visible and near-infrared spectroscopy techn...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2021/1508267 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832546055404650496 |
|---|---|
| author | Xueying Li Pingping Fan Zongmin Li Guangyuan Chen Huimin Qiu Guangli Hou |
| author_facet | Xueying Li Pingping Fan Zongmin Li Guangyuan Chen Huimin Qiu Guangli Hou |
| author_sort | Xueying Li |
| collection | DOAJ |
| description | Changes in land cover will cause the changes in the climate and environmental characteristics, which has an important influence on the social economy and ecosystem. The main form of land cover is different types of soil. Compared with traditional methods, visible and near-infrared spectroscopy technology can classify different types of soil rapidly, effectively, and nondestructively. Based on the visible near-infrared spectroscopy technology, this paper takes the soil of six different land cover types in Qingdao, China orchards, woodlands, tea plantations, farmlands, bare lands, and grasslands as examples and establishes a convolutional neural network classification model. The classification results of different number of training samples are analyzed and compared with the support vector machine algorithm. Under the condition that Kennard–Stone algorithm divides the calibration set, the classification results of six different soil types and single six soil types by convolutional neural network are better than those by the support vector machine. Under the condition of randomly dividing the calibration set according to the proportion of 1/3 and 1/4, the classification results by convolutional neural network are also better. The aim of this study is to analyze the feasibility of land cover classification with small samples by convolutional neural network and, according to the deep learning algorithm, to explore new methods for rapid, nondestructive, and accurate classification of the land cover. |
| format | Article |
| id | doaj-art-4a609f470c734f31b3990e66415a45c6 |
| institution | Kabale University |
| issn | 2314-4920 2314-4939 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-4a609f470c734f31b3990e66415a45c62025-02-03T07:24:01ZengWileyJournal of Spectroscopy2314-49202314-49392021-01-01202110.1155/2021/15082671508267Soil Classification Based on Deep Learning Algorithm and Visible Near-Infrared SpectroscopyXueying Li0Pingping Fan1Zongmin Li2Guangyuan Chen3Huimin Qiu4Guangli Hou5School of Geosciences, China University of Petroleum (East China), Qingdao 266580, ChinaInstitute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266061, ChinaCollege of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266590, ChinaCollege of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaInstitute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266061, ChinaInstitute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266061, ChinaChanges in land cover will cause the changes in the climate and environmental characteristics, which has an important influence on the social economy and ecosystem. The main form of land cover is different types of soil. Compared with traditional methods, visible and near-infrared spectroscopy technology can classify different types of soil rapidly, effectively, and nondestructively. Based on the visible near-infrared spectroscopy technology, this paper takes the soil of six different land cover types in Qingdao, China orchards, woodlands, tea plantations, farmlands, bare lands, and grasslands as examples and establishes a convolutional neural network classification model. The classification results of different number of training samples are analyzed and compared with the support vector machine algorithm. Under the condition that Kennard–Stone algorithm divides the calibration set, the classification results of six different soil types and single six soil types by convolutional neural network are better than those by the support vector machine. Under the condition of randomly dividing the calibration set according to the proportion of 1/3 and 1/4, the classification results by convolutional neural network are also better. The aim of this study is to analyze the feasibility of land cover classification with small samples by convolutional neural network and, according to the deep learning algorithm, to explore new methods for rapid, nondestructive, and accurate classification of the land cover.http://dx.doi.org/10.1155/2021/1508267 |
| spellingShingle | Xueying Li Pingping Fan Zongmin Li Guangyuan Chen Huimin Qiu Guangli Hou Soil Classification Based on Deep Learning Algorithm and Visible Near-Infrared Spectroscopy Journal of Spectroscopy |
| title | Soil Classification Based on Deep Learning Algorithm and Visible Near-Infrared Spectroscopy |
| title_full | Soil Classification Based on Deep Learning Algorithm and Visible Near-Infrared Spectroscopy |
| title_fullStr | Soil Classification Based on Deep Learning Algorithm and Visible Near-Infrared Spectroscopy |
| title_full_unstemmed | Soil Classification Based on Deep Learning Algorithm and Visible Near-Infrared Spectroscopy |
| title_short | Soil Classification Based on Deep Learning Algorithm and Visible Near-Infrared Spectroscopy |
| title_sort | soil classification based on deep learning algorithm and visible near infrared spectroscopy |
| url | http://dx.doi.org/10.1155/2021/1508267 |
| work_keys_str_mv | AT xueyingli soilclassificationbasedondeeplearningalgorithmandvisiblenearinfraredspectroscopy AT pingpingfan soilclassificationbasedondeeplearningalgorithmandvisiblenearinfraredspectroscopy AT zongminli soilclassificationbasedondeeplearningalgorithmandvisiblenearinfraredspectroscopy AT guangyuanchen soilclassificationbasedondeeplearningalgorithmandvisiblenearinfraredspectroscopy AT huiminqiu soilclassificationbasedondeeplearningalgorithmandvisiblenearinfraredspectroscopy AT guanglihou soilclassificationbasedondeeplearningalgorithmandvisiblenearinfraredspectroscopy |