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1.
Sentinel-2影像多特征优选的黄河三角洲湿地信息提取 总被引：7，自引：1，他引：7
以北方典型河口湿地—黄河三角洲湿地为研究区,采用在特征选择和分类提取等方面具有明显优势的随机森林算法,对研究区内的湿地信息进行提取。首先基于多时相、光谱信息丰富的Sentinel-2数据生成4类不同的特征变量,包括光谱特征、植被指数和水体指数、红边指数、纹理特征;再根据以上特征构建6种不同的提取方案,对黄河三角洲湿地信息进行提取并验证不同方案的提取精度,旨在选择最佳方案改善湿地信息提取的效果。结果表明:(1)有效地使用多种特征变量是提高湿地信息提取的关键,就不同特征对湿地信息提取的贡献率而言,红边指数植被指数和水体指数光谱特征纹理特征;(2)基于随机森林算法优选的特征变量提取效果最佳,总体精度高达90.93%,Kappa系数为0.90,表明随机森林算法可以有效地进行特征选择,在特征变量数据挖掘的同时,仍能保证湿地信息提取的精度,提高运行效率。本研究为湿地信息提取在数据源选择、特征选择和方法选择方面提供了一种新思路、方法和技术手段。 相似文献
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Wetland ecosystems have experienced dramatic challenges in the past few decades due to natural and human factors. Wetland maps are essential for the conservation and management of terrestrial ecosystems. This study is to obtain an accurate wetland map using an object-based stacked generalization (Stacking) method on the basis of multi-temporal Sentinel-1 and Sentinel-2 data. Firstly, the Robust Adaptive Spatial Temporal Fusion Model (RASTFM) is used to get time series Sentinel-2 NDVI, from which the vegetation phenology variables are derived by the threshold method. Subsequently, both vertical transmit-vertical receive (VV) and vertical transmit-horizontal receive (VH) polarization backscatters (σ0 VV, σ0 VH) are obtained using the time series Sentinel-1 images. Speckle noise inherent in SAR data, resulting in over-segmentation or under-segmentation, can affect image segmentation and degrade the accuracies of wetland classification. Therefore, we segment Sentinel-2 multispectral images to delineate meaningful objects in this study. Then, in order to reduce data redundancy and computation time, we analyze the optimal feature combination using the Sentinel-2 multispectral images, Sentinel-2 NDVI time series, phenological variables and other vegetation index derived from Sentinel-2 multispectral images, as well as time series Sentinel-1 backscatters at the object level. Finally, the stacked generalization algorithm is utilized to extract the wetland information based on the optimal feature combination in the Dongting Lake wetland. The overall accuracy and Kappa coefficient of the object-based stacked generalization method are 92.46% and 0.92, which are 3.88% and 0.04 higher than that using the pixel-based method. Moreover, the object-based stacked generalization algorithm is superior to single classifiers in classifying vegetation of high heterogeneity areas. 相似文献
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Rei Sonobe Yuki Yamaya Hiroshi Tani Xiufeng Wang Nobuyuki Kobayashi Kan-ichiro Mochizuki 《地理信息系统科学与遥感》2017,54(6):918-938
Sentinel-1A C-SAR and Sentinel-2A MultiSpectral Instrument (MSI) provide data applicable to the remote identification of crop type. In this study, six crop types (beans, beetroot, grass, maize, potato, and winter wheat) were identified using five C-SAR images and one MSI image acquired during the 2016 growing season. To assess the potential for accurate crop classification with existing supervised learning models, the four different approaches namely kernel-based extreme learning machine (KELM), multilayer feedforward neural networks, random forests, and support vector machine were compared. Algorithm hyperparameters were tuned using Bayesian optimization. Overall, KELM yielded the highest performance, achieving an overall classification accuracy of 96.8%. Evaluation of the sensitivity of classification models and relative importance of data types using data-based sensitivity analysis showed that the set of VV polarization data acquired on 24 July (Sentinel-1A) and band 4 data (Sentinel-2A) had the greatest potential for use in crop classification. 相似文献
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基于光学影像的遥感技术受云雨等天气条件影响较大,而合成孔径雷达(SAR)由于具有穿透能力可以很好的克服这一缺陷。本文以黑龙江流域扎龙湿地为研究区域,采用时间序列C波段双极化(VV、VH)Sentinel-1数据,结合面向对象的图像分析技术对扎龙湿地进行分类。对比分析了5种机器学习算法得出随机森林算法的精度最高,总体精度为88.43%,Kappa系数为0.8646,其中沼泽的制图精度达到84.68%,用户精度达到89.47%。使用Sentinel-1数据对扎龙湿地进行湿地信息提取的最佳时相为5月、7月和8月。 相似文献
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LiDAR data are becoming increasingly available, which has opened up many new applications. One such application is crop type mapping. Accurate crop type maps are critical for monitoring water use, estimating harvests and in precision agriculture. The traditional approach to obtaining maps of cultivated fields is by manually digitizing the fields from satellite or aerial imagery and then assigning crop type labels to each field - often informed by data collected during ground and aerial surveys. However, manual digitizing and labeling is time-consuming, expensive and subject to human error. Automated remote sensing methods is a cost-effective alternative, with machine learning gaining popularity for classifying crop types. This study evaluated the use of LiDAR data, Sentinel-2 imagery, aerial imagery and machine learning for differentiating five crop types in an intensively cultivated area. Different combinations of the three datasets were evaluated along with ten machine learning. The classification results were interpreted by comparing overall accuracies, kappa, standard deviation and f-score. It was found that LiDAR data successfully differentiated between different crop types, with XGBoost providing the highest overall accuracy of 87.8％. Furthermore, the crop type maps produced using the LiDAR data were in general agreement with those obtained by using Sentinel-2 data, with LiDAR obtaining a mean overall accuracy of 84.3％ and Sentinel-2 a mean overall accuracy of 83.6％. However, the combination of all three datasets proved to be the most effective at differentiating between the crop types, with RF providing the highest overall accuracy of 94.4％. These findings provide a foundation for selecting the appropriate combination of remotely sensed data sources and machine learning algorithms for operational crop type mapping. 相似文献
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In this study, we test the use of Land Use and Coverage Area frame Survey (LUCAS) in-situ reference data for classifying high-resolution Sentinel-2 imagery at a large scale. We compare several pre-processing schemes (PS) for LUCAS data and propose a new PS for a fully automated classification of satellite imagery on the national level. The image data utilizes a high-dimensional Sentinel-2-based image feature space. Key elements of LUCAS data pre-processing include two positioning approaches and three semantic selection approaches. The latter approaches differ in the applied quality measures for identifying valid reference points and by the number of LU/LC classes (7–12). In an iterative training process, the impact of the chosen PS on a Random Forest image classifier is evaluated. The results are compared to LUCAS reference points that are not pre-processed, which act as a benchmark, and the classification quality is evaluated by independent sets of validation points. The classification results show that the positional correction of LUCAS points has an especially positive effect on the overall classification accuracy. On average, this improves the accuracy by 3.7%. This improvement is lowest for the most rigid sample selection approach, PS2, and highest for the benchmark data set, PS0. The highest overall accuracy is 93.1% which is achieved by using the newly developed PS3; all PS achieve overall accuracies of 80% and higher on average. While the difference in overall accuracy between the PS is likely to be influenced by the respective number of LU/LC classes, we conclude that, overall, LUCAS in-situ data is a suitable source for reference information for large scale high resolution LC mapping using Sentinel-2 imagery. Existing sample selection approaches developed for Landsat imagery can be transferred to Sentinel-2 imagery, achieving comparable semantic accuracies while increasing the spatial resolution. The resulting LC classification product that uses the newly developed PS is available for Germany via DOI: https://doi.org/10.15489/1ccmlap3mn39. 相似文献
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黄河三角洲湿地的动态变化监测对湿地资源合理利用、开发保护具有重要意义。采用C波段全极化高分三号（GF-3）合成孔径雷达数据与欧洲空间局哨兵二号（Sentinel-2B）多光谱数据，分析了黄河三角洲湿地7类地物的光谱、指数、极化散射以及纹理等特征信息，分别基于最大似然法（maximum likelihood，ML）、决策树（decision tree，DT）、支持向量机（support vector machine，SVM）方法实现了有监督分类，评估了两者协同与单独应用于湿地地物分类与识别的能力，结果表明，两者协同分类时，其总体精度分别可达90.4%、95.4%、95.7%，均明显高于两者单独分类的结果，证明了GF-3雷达数据与多光谱数据在湿地协同分类方面的可靠性和应用潜力。 相似文献
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Wetlands provide habitat for a wide variety of plant and animal species and contribute significantly to overall biodiversity in Ireland. Despite these known ecosystem services, the total wetland area in Ireland has reduced significantly over the past few decades leading to an ongoing need to protect such environments. The EU Habitats Directive (92/43/EEC) has recognised several wetlands types as “priority” habitats. This study concentrates on a subset of the priority habitats focussing on some groundwater dependent terrestrial ecosystems, (in particular calcareous fens and turloughs), as well as raised bogs. Monitoring these sites across the country by field visits is resource-intensive. Therefore, this study has evaluated remote sensing as a potentially cost-effective tool for monitoring the ecological health of the wetlands. Identification and presence of certain vegetation communities can indicate the condition of the wetland, which can be used for monitoring, for example, activities causing degradation or the progress of restoration attempts. The ecological composition of the wetlands has been analysed using open-source Sentinel-2 data. 10 bands of Sentinel-2 Level-2 data and 3 indices, Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalised Difference Water Index (NDWI) were used to create vegetation maps of each wetland using Bagged Tree (BT) ensemble classifier and graph cut segmentation also known as MAP (maximum a posteriori) estimation. The proposed methodology has been validated on five raised bogs, five turloughs, and three fens at different times during 2017 and 2018 from which three case studies are presented. An overall classification accuracy up to 87% depending on the size of the vegetation community within each wetland has been achieved which suggests that the proposed method is appropriate for wetland health monitoring. 相似文献
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Wetlands are dynamic landscapes and their spatial extent and types can change over time. Mapping wetland locations, types, and monitoring wetland typological changes have important ecological significance. The National Wetlands Inventory data suffer from two problems: the omission error that some wetlands are not mapped, and the out-of-date wetland types in many counties of the United States. To address these two problems, we proposed an automatic wetland classification model for newly mapped (or existing) wetland polygons lacking typological information. The research goals in this study were (1) to develop a nonparametric and automatic rule-based model to assign wetland types to palustrine wetlands using high-resolution remotely sensed data and (2) to quantify wetland typological changes based on the wetland types obtained from the previous step. The model is a direct application of the Cowardin et al. (1979) wetland classification system without modification. The input information for the proposed model includes Light Detection and Ranging (LiDAR)-derived vegetation height and color infrared aerial imagery-derived vegetation spectral information. We tested the model for the palustrine wetlands in Horry County, SC, and analyzed 29,090 palustrine wetland polygons (101,427 ha). The model achieved an overall agreement of 87% for wetland-type classification and showed the dynamics of wetland typological changes. This nonparametric model can be easily applied to other areas where wetland inventory needs updating. 相似文献
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Forest canopy height is an important indicator of forest carbon storage, productivity, and biodiversity. The present study showed the first attempt to develop a machine-learning workflow to map the spatial pattern of the forest canopy height in a mountainous region in the northeast China by coupling the recently available canopy height (Hcanopy) footprint product from ICESat-2 with the Sentinel-1 and Sentinel-2 satellite data. The ICESat-2 Hcanopy was initially validated by the high-resolution canopy height from airborne LiDAR data at different spatial scales. Performance comparisons were conducted between two machine-learning models – deep learning (DL) model and random forest (RF) model, and between the Sentinel and Landsat-8 satellites. Results showed that the ICESat-2 Hcanopy showed the highest correlation with the airborne LiDAR canopy height at a spatial scale of 250 m with a Pearson’s correlation coefficient (R) of 0.82 and a mean bias of -1.46 m, providing important evidence on the reliability of the ICESat-2 vegetation height product from the case in China’s forest. Both DL and RF models obtained satisfactory accuracy on the upscaling of ICESat-2 Hcanopy assisted by Sentinel satellite co-variables with an R-value between the observed and predicted Hcanopy equalling 0.78 and 0.68, respectively. Compared to Sentinel satellites, Landsat-8 showed relatively weaker performance in Hcanopy prediction, suggesting that the addition of the backscattering coefficients from Sentinel-1 and the red-edge related variables from Sentinel-2 could positively contribute to the prediction of forest canopy height. To our knowledge, few studies have demonstrated large-scale vegetation height mapping in a resolution ≤ 250 m based on the newly available satellites (ICESat-2, Sentinel-1 and Sentinel-2) and DL regression model, particularly in the forest areas in China. Thus, the present work provided a timely and important supplementary to the applications of these new earth observation tools. 相似文献
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Wetland inventory maps are essential information for the conservation and management of natural wetland areas. The classification framework is crucial for successful mapping of complex wetlands, including the model selection, input variables and training procedures. In this context, deep neural network (DNN) is a powerful technique for remote sensing image classification, but this model application for wetland mapping has not been discussed in the previous literature, especially using commercial WorldView-3 data. This study developed a new framework for wetland mapping using DNN algorithm and WorldView-3 image in the Millrace Flats Wildlife Management Area, Iowa, USA. The study area has several wetlands with a variety of shapes and sizes, and the minimum mapping unit was defined as 20 m2 (0.002 ha). A set of potential variables was derived from WorldView-3 and auxiliary LiDAR data, and a feature selection procedure using principal components analysis (PCA) was used to identify the most important variables for wetland classification. Furthermore, traditional machine learning methods (support vector machine, random forest and k-nearest neighbor) were also implemented for the comparison of results. In general, the results show that DNN achieved satisfactory results in the study area (overall accuracy = 93.33 %), and we observed a high spatial overlap between reference and classified wetland polygons (Jaccard index ∼0.8). Our results confirm that PCA-based feature selection was effective in the optimization of DNN performance, and vegetation and textural indices were the most informative variables. In addition, the comparison of results indicated that DNN classification achieved relatively similar accuracies to other methods. The total classification errors vary from 0.104 to 0.111 among the methods, and the overlapped areas between reference and classified polygons range between 87.93 and 93.33 %. Finally, the findings of this study have three main implications. First, the integration of DNN model and WorldView-3 image is useful for wetland mapping at 1.2-m, but DNN results did not outperform other methods in this study area. Second, the feature selection was important for model performance, and the combination of most relevant input parameters contributes to the success of all tested models. Third, the spatial resolution of WorldView-3 is appropriate to preserve the shape and extent of small wetlands, while the application of medium resolution image (30-m) has a negative impact on the accurate delineation of these areas. Since commercial satellite data are becoming more affordable for remote sensing users, this study provides a framework that can be utilized to integrate very high-resolution imagery and deep learning in the classification of complex wetland areas. 相似文献
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本文以雷州半岛为研究区,利用Sentinel-2A影像数据和真实植被样本数据,综合探讨了机器学习中随机森林与支持向量机的分类效果,并与传统的最大似然法进行比较。提取Sentinel-2A影像9个波段、7个植被指数、72个纹理特征,通过递归特征消除法挑选了10个特征组合,并将其应用于3种分类方法中,对其分类效果进行比较。结果表明:①有效使用多种特征变量是提高植被类型识别精度的关键,就不同特征对植被类型识别的重要性而言,光谱特征与纹理特征相当且大于植被指数,三者重要性相差不大;②随机森林分类效果最佳,不但能对特征进行有效选择,而且能保证植被类型提取精度,提高运行效率;③基于随机森林特征选择的递归特征消除法得到的特征组合不能对其他分类器性能进行优化,对随机森林模型本身的优化效果也有限。 相似文献
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Satellite-based wetland mapping faces challenges due to the high spatial heterogeneity and dynamic characteristics of seasonal wetlands. Although normalized difference vegetation index (NDVI) time series (NTS) shows great potential in land cover mapping and crop classification, the effectiveness of various NTS with different spatial and temporal resolution has not been evaluated for seasonal wetland classification. To address this issue, we conducted comparisons of those NTS, including the moderate-resolution imaging spectroradiometer (MODIS) NTS with 500?m resolution, NTS fused with MODIS and Landsat data (MOD_LC8-NTS), and HJ-1 NDVI compositions (HJ-1-NTS) with finer resolution, for wetland classification of Poyang Lake. Results showed the following: (1) the NTS with finer resolution was more effective in the classification of seasonal wetlands than that of the MODIS-NTS with 500-m resolution and (2) generally, the HJ-1-NTS performed better than that of the fused NTS, with an overall accuracy of 88.12% for HJ-1-NTS and 83.09% for the MOD_LC8-NTS. Future work should focus on the construction of satellite image time series oriented to highly dynamic characteristics of seasonal wetlands. This study will provide useful guidance for seasonal wetland classification, and benefit the improvements of spatiotemporal fusion models. 相似文献
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The spatial and temporal distribution of trees has a large impact on human health and the environment through contributions to important climate mechanisms as well as commercial, recreational and social activities in society. A range of tree mapping methodologies has been presented in the literature, but tree cover estimates still differ widely between the individual datasets, and comparisons of the thematic accuracy of the resulting tree maps are rather scarce. The Copernicus Sentinel-2 satellites, which were launched in 2015 and 2017, have a combination of high spatial and temporal resolution. Given that this is a new satellite, a substantial amount of research on development of tree mapping algorithms as well as accuracy assessment of said algorithms have to be done in the years to come. To contribute to this process, a tree map produced through unsupervised classification was created for six Sentinel-2 tiles. The agreement between the tree map and the corresponding national forest inventory, as a function of the band combination chosen, was analysed and the thematic accuracy was assessed for two out of the six tiles. The results show that the highest agreement between the present tree map and the national forest inventory was found for bands 2, 3, 6 and 12. The present tree map has a relative difference in tree cover between 8% and 79% compared to previous estimates, but results are characterised by large scatter. Lastly, it is shown that the overall thematic accuracy of the present map is up to 90%, with the user’s accuracy ranging from 34.85% to 92.10%, and the producer’s accuracy ranging from 23.80% to 97.60% for the various thematic classes. This demonstrates that tree maps with high thematic accuracy can be produced from Sentinel-2. In the future the thematic accuracy can be increased even more through the use of temporal averaging in the mapping procedure, which will enable an accurate estimate of the European tree cover. 相似文献
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卫星遥感技术可用于海岛资源调查。Sentinel-2A与Landsat 8两颗卫星都可免费提供空间分辨率较高的多光谱遥感影像，在海岛调查中的应用潜力较大。本文以浙江舟山普陀山岛为例开展了针对这两种影像在海岛植被分类中的应用效果的研究，分别利用Sentinel-2A多光谱成像仪（MSI）和Landsat 8陆地成像仪（OLI）影像基于最大似然法分类获得了该岛阔叶林、针阔混交林、针叶林、灌丛、草丛等植被及其他地物的分布情况，并进行了精度检验，结果表明MSI的总体分类精度略高于OLI。 相似文献
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Assessment of Sentinel-1A data for rice crop classification using random forests and support vector machines 总被引：1，自引：0，他引：1
This study developed an approach to map rice-cropping systems in An Giang and Dong Thap provinces, South Vietnam using multi-temporal Sentinel-1A (S1A) data. The data were processed through four steps: (1) data pre-processing, (2) constructing smooth time series VH backscatter data, (3) rice crop classification using random forests (RF) and support vector machines (SVM) and (4) accuracy assessment. The results indicated that the smooth VH backscatter profiles reflected the temporal characteristics of rice-cropping patterns in the study region. The comparisons between the classification results and the ground reference data indicated that the overall accuracy and Kappa coefficient achieved from RF were 86.1% and 0.72, respectively, which were slightly more accurate than SVM (overall accuracy of 83.4% and Kappa coefficient of 0.67). These results were reaffirmed by the government’s rice area statistics with the relative error in area (REA) values of 0.2 and 2.2% for RF and SVM, respectively. 相似文献
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Sentinel-2 is planned for launch in 2014 by the European Space Agency and it is equipped with the Multi Spectral Instrument (MSI), which will provide images with high spatial, spectral and temporal resolution. It covers the VNIR/SWIR spectral region in 13 bands and incorporates two new spectral bands in the red-edge region, which can be used to derive vegetation indices using red-edge bands in their formulation. These are particularly suitable for estimating canopy chlorophyll and nitrogen (N) content. This band setting is important for vegetation studies and is very similar to the ones of the Ocean and Land Colour Instrument (OLCI) on the planned Sentinel-3 satellite and the Medium Resolution Imaging Spectrometer (MERIS) on Envisat, which operated from 2002 to early 2012. This paper focuses on the potential of Sentinel-2 and Sentinel-3 in estimating total crop and grass chlorophyll and N content by studying in situ crop variables and spectroradiometer measurements obtained for four different test sites. In particular, the red-edge chlorophyll index (CIred-edge), the green chlorophyll index (CIgreen) and the MERIS terrestrial chlorophyll index (MTCI) were found to be accurate and linear estimators of canopy chlorophyll and N content and the Sentinel-2 and -3 bands are well positioned for deriving these indices. Results confirm the importance of the red-edge bands on particularly Sentinel-2 for agricultural applications, because of the combination with its high spatial resolution of 20 m. 相似文献
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面向Sentinel-2多光谱数据，依据影像地物空间结构和地表实际功能类型将建筑区分为高层建筑区和低层建筑区，构建了一种基于全卷积网络模型的高、低层建筑区快速提取技术。在此基础上，以雄安新区及其周边为试验区，选取2017年3月获取的4景Sentinel-2多光谱数据进行试验验证和分析。结果表明：本文技术能够实现大范围区域内高层和低层建筑区的快速提取，总体平均提取精度达到95.30%，其中高层建筑区平均提取精度为99.22%，低层建筑区平均提取精度为91.38%，该技术明显优于现有基于纹理结构的高低层建筑区提取方法。通过对提取结果进行统计分析发现：约4.4×104 km2的研究区内高层和低层建筑区分别约为94和7351 km2；雄安新区中心三县内高层和低层建筑区分别约为1.25和312.24 km2。本文技术具有很好的推广性，结合Sentinel-2数据大幅宽高频次观测特点和更多类型建筑区样本，可以实现大范围地表多类型建筑区动态监测。 相似文献
20.
The availability of freely available moderate-to-high spatial resolution (10–30 m) satellite imagery received a major boost with the recent launch of the Sentinel-2 sensor by the European Space Agency. Together with Landsat, these sensors provide the scientific community with a wide range of spatial, spectral, and temporal properties. This study compared and explored the synergistic use of Landsat-8 and Sentinel-2 data in mapping land use and land cover (LULC) in rural Burkina Faso. Specifically, contribution of the red-edge bands of Sentinel-2 in improving LULC mapping was examined. Three machine-learning algorithms – random forest, stochastic gradient boosting, and support vector machines – were employed to classify different data configurations. Classification of all Sentinel-2 bands as well as Sentinel-2 bands common to Landsat-8 produced an overall accuracy, that is 5% and 4% better than Landsat-8. The combination of Landsat-8 and Sentinel-2 red-edge bands resulted in a 4% accuracy improvement over that of Landsat-8. It was found that classification of the Sentinel-2 red-edge bands alone produced better and comparable results to Landsat-8 and the other Sentinel-2 bands, respectively. Results of this study demonstrate the added value of the Sentinel-2 red-edge bands and encourage multi-sensoral approaches to LULC mapping in West Africa. 相似文献