基于特征优选的北疆典型区域非农化遥感监测
S29; 该研究旨在准确把握耕地"非农化"的时空格局,为制定合理的土地利用和耕地保护政策提供重要依据.随着特征提取技术和分类算法的进步,利用遥感影像进行大规模耕地动态监测变得更加准确和高效.该研究选用Sentinel-2卫星影像,探讨了不同算法和特征变量在耕地非农化监测中的优势.首先提取了 4类特征共计31个指标,并通过主成分分析(principipal component analysis,PCA)和相关系数矩阵进行特征优选,获得了 12个关键指标,并设计了 5种特征组合方案.随后,采用7种基础算法执行影像分类,并通过"单阶段"和"二阶段&q...
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| Published in | 农业工程学报 Vol. 40; no. 5; pp. 275 - 286 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
兵团空间信息工程技术研究中心,石河子 832003
01.03.2024
石河子大学信息科学与技术学院,石河子 832003 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.11975/j.issn.1002-6819.202308171 |
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| Abstract | S29; 该研究旨在准确把握耕地"非农化"的时空格局,为制定合理的土地利用和耕地保护政策提供重要依据.随着特征提取技术和分类算法的进步,利用遥感影像进行大规模耕地动态监测变得更加准确和高效.该研究选用Sentinel-2卫星影像,探讨了不同算法和特征变量在耕地非农化监测中的优势.首先提取了 4类特征共计31个指标,并通过主成分分析(principipal component analysis,PCA)和相关系数矩阵进行特征优选,获得了 12个关键指标,并设计了 5种特征组合方案.随后,采用7种基础算法执行影像分类,并通过"单阶段"和"二阶段"两种分类策略,提取耕地"非农化"信息.研究结果表明,有效选择多种特征变量和算法对于提高监测精度至关重要.在所有测试的模型中,采用Softmax构建的二阶段模型精度最高,最优特征组合为光谱特征+光谱指数特征+纹理特征,特征变量维度减少至12个.总体精度、平均用户精度、平均生产者精度和Kappa系数分别达到94.92％、95.16％、93.15％和0.88.对比2020年和2022年研究区数据发现,耕地转变为非农化用地的面积为146.153 km2,而非农化用地转变为耕地的面积为123.074 km2,导致耕地净减少23.079 km2.综上所述,该研究提出的耕地"非农化"监测方法可以为相关的地物信息提取和耕地资源保护与可持续利用等研究提供技术支持和方法参考. |
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| AbstractList | S29; 该研究旨在准确把握耕地"非农化"的时空格局,为制定合理的土地利用和耕地保护政策提供重要依据.随着特征提取技术和分类算法的进步,利用遥感影像进行大规模耕地动态监测变得更加准确和高效.该研究选用Sentinel-2卫星影像,探讨了不同算法和特征变量在耕地非农化监测中的优势.首先提取了 4类特征共计31个指标,并通过主成分分析(principipal component analysis,PCA)和相关系数矩阵进行特征优选,获得了 12个关键指标,并设计了 5种特征组合方案.随后,采用7种基础算法执行影像分类,并通过"单阶段"和"二阶段"两种分类策略,提取耕地"非农化"信息.研究结果表明,有效选择多种特征变量和算法对于提高监测精度至关重要.在所有测试的模型中,采用Softmax构建的二阶段模型精度最高,最优特征组合为光谱特征+光谱指数特征+纹理特征,特征变量维度减少至12个.总体精度、平均用户精度、平均生产者精度和Kappa系数分别达到94.92％、95.16％、93.15％和0.88.对比2020年和2022年研究区数据发现,耕地转变为非农化用地的面积为146.153 km2,而非农化用地转变为耕地的面积为123.074 km2,导致耕地净减少23.079 km2.综上所述,该研究提出的耕地"非农化"监测方法可以为相关的地物信息提取和耕地资源保护与可持续利用等研究提供技术支持和方法参考. |
| Abstract_FL | The spatiotemporal pattern of non-agriculturalization can be accurately and rapidly captured for the rational formulation of land use and cultivated land protection.Remote sensing imagery has been more precise and efficient for the large-scale dynamic monitoring of cultivated land,with the advancements in feature extraction and classification.There are also different and feature variables during dynamic monitoring of cultivated land.In this study,Sentinel-2 satellite imagery was employed to monitor the non-agriculturalization of cultivated land.The experimental and validation zones were selected from the northern part of Xinjiang,a vital agricultural production base in China.The reclaimed areas were also in the Eighth Division of the Xinjiang Production and Construction Corps in Shihezi City.The data source was comprised of Sentinel-2 remote sensing images,covering the research area as of July 17,2022,and August 16,2020.The images were improved to facilitate the automatic extraction of information,according to the non-agricultural land use on cultivated land.Initially,the spectral indices and gray-level co-occurrence matrix were calculated to obtain 3 1 features,including spectral,spectral index,and texture features.Subsequently,Pearson correlation coefficients were computed for the eight band features,seven non-red edge spectral indices,and eight red edge spectral indices,leading to the removal of redundant features with weak classification and high correlation.Principal component analysis(PCA)was applied to extract the first three components of eight texture features.Feature importance scores were ranked using the random forest(RF)with the Gini index,resulting in the selection of optimal features:red,green,blue,narrow NIR(B8A),normalized difference vegetation index(NDVI),modified soil adjusted vegetation index(MSAVI),red-edge chlorophyll index(Clre),red-edge vegetation index(REDVI),triangular vegetation index(TVI),PCA1,PCA2 and PCA3 of texture features.12 features and the original were selected to construct five classification schemes,including four optimal combinations(Scheme 1-3 and Scheme 5)and the original(Scheme 4).Seven classification algorithms were then utilized to evaluate:RF,artificial neural network(ANN),minimum distance(MID),maximum likelihood(MAL),mahalanobis distance(MAD),support vector machine(SVM),and Softmax.The results included:1)The importance ranking of feature variables was as follows:band features,red edge spectral index features,non-red edge spectral index features,texture features.2)Accuracy improvement depended mainly on the effective selection of multiple feature variables and algorithms.The two-stage model was constructed using Softmax.The highest accuracy was achieved with the optimal combination of feature variables,namely band+spectral index+texture features.The dimensionality of the feature variable was reduced to 12.The overall accuracy,average user's accuracy,average producer's accuracy,and Kappa coefficient were 94.92％,95.16％,93.15％,and 0.88,respectively.3)A comparison of 2020 and 2022 data revealed that there was an area of 146.153 km2 transitioning from cultivated land to non-agricultural use,while an area of 123.074 km2 was transitioned from non-agricultural to cultivated land.Overall,the cultivated land decreased by 23.079 km2.In conclusion,the non-agriculturalization of cultivated land can provide technical support and references for the extraction of land cover information,particularly for the resource protection and sustainable utilization of cultivated land. |
| Author | 夏明辉 许淼淼 赵庆展 戴建国 曹宇娟 张国顺 穆正阳 |
| AuthorAffiliation | 石河子大学信息科学与技术学院,石河子 832003;兵团空间信息工程技术研究中心,石河子 832003 |
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| Author_FL | DAI Jianguo ZHANG Guoshun ZHAO Qingzhan XIA Minghui MU Zhengyang XU Miaomiao CAO Yujuan |
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| DocumentTitle_FL | Remote sensing monitoring of non-agriculturalization in typical areas of the Northern Xinjiang of China based on feature optimization |
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| Keywords | 特征优选 耕地 non-agriculturalization Sentinel-2 cultivated land remote sensing 非农化 遥感 feature selection |
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| Title | 基于特征优选的北疆典型区域非农化遥感监测 |
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