Cargando…
Mapping the Corn Residue-Covered Types Using Multi-Scale Feature Fusion and Supervised Learning Method by Chinese GF-2 PMS Image
The management of crop residue covering is a vital part of conservation tillage, which protects black soil by reducing soil erosion and increasing soil organic carbon. Accurate and rapid classification of corn residue-covered types is significant for monitoring crop residue management. The remote se...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9253822/ https://www.ncbi.nlm.nih.gov/pubmed/35800607 http://dx.doi.org/10.3389/fpls.2022.901042 |
_version_ | 1784740574007394304 |
---|---|
author | Tao, Wancheng Dong, Yi Su, Wei Li, Jiayu Xuan, Fu Huang, Jianxi Yang, Jianyu Li, Xuecao Zeng, Yelu Li, Baoguo |
author_facet | Tao, Wancheng Dong, Yi Su, Wei Li, Jiayu Xuan, Fu Huang, Jianxi Yang, Jianyu Li, Xuecao Zeng, Yelu Li, Baoguo |
author_sort | Tao, Wancheng |
collection | PubMed |
description | The management of crop residue covering is a vital part of conservation tillage, which protects black soil by reducing soil erosion and increasing soil organic carbon. Accurate and rapid classification of corn residue-covered types is significant for monitoring crop residue management. The remote sensing technology using high spatial resolution images is an effective means to classify the crop residue-covered areas quickly and objectively in the regional area. Unfortunately, the classification of crop residue-covered area is tricky because there is intra-object heterogeneity, as a two-edged sword of high resolution, and spectral confusion resulting from different straw mulching ways. Therefore, this study focuses on exploring the multi-scale feature fusion method and classification method to classify the corn residue-covered areas effectively and accurately using Chinese high-resolution GF-2 PMS images in the regional area. First, the multi-scale image features are built by compressing pixel domain details with the wavelet and principal component analysis (PCA), which has been verified to effectively alleviate intra-object heterogeneity of corn residue-covered areas on GF-2 PMS images. Second, the optimal image dataset (OID) is identified by comparing model accuracy based on the fusion of different features. Third, the 1D-CNN_CA method is proposed by combining one-dimensional convolutional neural networks (1D-CNN) and attention mechanisms, which are used to classify corn residue-covered areas based on the OID. Comparison of the naive Bayesian (NB), random forest (RF), support vector machine (SVM), and 1D-CNN methods indicate that the residue-covered areas can be classified effectively using the 1D-CNN-CA method with the highest accuracy (Kappa: 96.92% and overall accuracy (OA): 97.26%). Finally, the most appropriate machine learning model and the connected domain calibration method are combined to improve the visualization, which are further used to classify the corn residue-covered areas into three covering types. In addition, the study showed the superiority of multi-scale image features by comparing the contribution of the different image features in the classification of corn residue-covered areas. |
format | Online Article Text |
id | pubmed-9253822 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92538222022-07-06 Mapping the Corn Residue-Covered Types Using Multi-Scale Feature Fusion and Supervised Learning Method by Chinese GF-2 PMS Image Tao, Wancheng Dong, Yi Su, Wei Li, Jiayu Xuan, Fu Huang, Jianxi Yang, Jianyu Li, Xuecao Zeng, Yelu Li, Baoguo Front Plant Sci Plant Science The management of crop residue covering is a vital part of conservation tillage, which protects black soil by reducing soil erosion and increasing soil organic carbon. Accurate and rapid classification of corn residue-covered types is significant for monitoring crop residue management. The remote sensing technology using high spatial resolution images is an effective means to classify the crop residue-covered areas quickly and objectively in the regional area. Unfortunately, the classification of crop residue-covered area is tricky because there is intra-object heterogeneity, as a two-edged sword of high resolution, and spectral confusion resulting from different straw mulching ways. Therefore, this study focuses on exploring the multi-scale feature fusion method and classification method to classify the corn residue-covered areas effectively and accurately using Chinese high-resolution GF-2 PMS images in the regional area. First, the multi-scale image features are built by compressing pixel domain details with the wavelet and principal component analysis (PCA), which has been verified to effectively alleviate intra-object heterogeneity of corn residue-covered areas on GF-2 PMS images. Second, the optimal image dataset (OID) is identified by comparing model accuracy based on the fusion of different features. Third, the 1D-CNN_CA method is proposed by combining one-dimensional convolutional neural networks (1D-CNN) and attention mechanisms, which are used to classify corn residue-covered areas based on the OID. Comparison of the naive Bayesian (NB), random forest (RF), support vector machine (SVM), and 1D-CNN methods indicate that the residue-covered areas can be classified effectively using the 1D-CNN-CA method with the highest accuracy (Kappa: 96.92% and overall accuracy (OA): 97.26%). Finally, the most appropriate machine learning model and the connected domain calibration method are combined to improve the visualization, which are further used to classify the corn residue-covered areas into three covering types. In addition, the study showed the superiority of multi-scale image features by comparing the contribution of the different image features in the classification of corn residue-covered areas. Frontiers Media S.A. 2022-06-21 /pmc/articles/PMC9253822/ /pubmed/35800607 http://dx.doi.org/10.3389/fpls.2022.901042 Text en Copyright © 2022 Tao, Dong, Su, Li, Xuan, Huang, Yang, Li, Zeng and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Tao, Wancheng Dong, Yi Su, Wei Li, Jiayu Xuan, Fu Huang, Jianxi Yang, Jianyu Li, Xuecao Zeng, Yelu Li, Baoguo Mapping the Corn Residue-Covered Types Using Multi-Scale Feature Fusion and Supervised Learning Method by Chinese GF-2 PMS Image |
title | Mapping the Corn Residue-Covered Types Using Multi-Scale Feature Fusion and Supervised Learning Method by Chinese GF-2 PMS Image |
title_full | Mapping the Corn Residue-Covered Types Using Multi-Scale Feature Fusion and Supervised Learning Method by Chinese GF-2 PMS Image |
title_fullStr | Mapping the Corn Residue-Covered Types Using Multi-Scale Feature Fusion and Supervised Learning Method by Chinese GF-2 PMS Image |
title_full_unstemmed | Mapping the Corn Residue-Covered Types Using Multi-Scale Feature Fusion and Supervised Learning Method by Chinese GF-2 PMS Image |
title_short | Mapping the Corn Residue-Covered Types Using Multi-Scale Feature Fusion and Supervised Learning Method by Chinese GF-2 PMS Image |
title_sort | mapping the corn residue-covered types using multi-scale feature fusion and supervised learning method by chinese gf-2 pms image |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9253822/ https://www.ncbi.nlm.nih.gov/pubmed/35800607 http://dx.doi.org/10.3389/fpls.2022.901042 |
work_keys_str_mv | AT taowancheng mappingthecornresiduecoveredtypesusingmultiscalefeaturefusionandsupervisedlearningmethodbychinesegf2pmsimage AT dongyi mappingthecornresiduecoveredtypesusingmultiscalefeaturefusionandsupervisedlearningmethodbychinesegf2pmsimage AT suwei mappingthecornresiduecoveredtypesusingmultiscalefeaturefusionandsupervisedlearningmethodbychinesegf2pmsimage AT lijiayu mappingthecornresiduecoveredtypesusingmultiscalefeaturefusionandsupervisedlearningmethodbychinesegf2pmsimage AT xuanfu mappingthecornresiduecoveredtypesusingmultiscalefeaturefusionandsupervisedlearningmethodbychinesegf2pmsimage AT huangjianxi mappingthecornresiduecoveredtypesusingmultiscalefeaturefusionandsupervisedlearningmethodbychinesegf2pmsimage AT yangjianyu mappingthecornresiduecoveredtypesusingmultiscalefeaturefusionandsupervisedlearningmethodbychinesegf2pmsimage AT lixuecao mappingthecornresiduecoveredtypesusingmultiscalefeaturefusionandsupervisedlearningmethodbychinesegf2pmsimage AT zengyelu mappingthecornresiduecoveredtypesusingmultiscalefeaturefusionandsupervisedlearningmethodbychinesegf2pmsimage AT libaoguo mappingthecornresiduecoveredtypesusingmultiscalefeaturefusionandsupervisedlearningmethodbychinesegf2pmsimage |