共查询到19条相似文献，搜索用时 140 毫秒
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航空影像农田类型分类在地理国情监测中的应用研究 总被引：1，自引：0，他引：1
航空影像的植被信息提取是遥感影像分类中的难点,仅利用光谱信息难以提取农田类型。本文以江苏农田为主要覆盖的典型区域为研究对象,选择航空影像利用随机森林算法提取不同的农田信息。本研究采用多尺度的分割方法,面向对象实现特征信息提取。根据光谱、纹理以及几何形状特性筛选出较为合适的特征作为参数,利用随机森林算法实现植被二级分类,分类精度达到84.60%,KAPPA系数为0.753,可为地理国情生产提供一定的参考。 相似文献
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高分辨率遥感影像的随机森林变化检测方法 总被引：3，自引：0，他引：3
基于面向对象分析(OBIA)的遥感影像变化检测研究已取得显著的进展,代表了遥感影像变化检测的发展范式,未来是发展更加智能的解译分析方法。随机森林作为一种新的机器学习算法,其预测效果和性能稳定性要优于许多单预测器和集成预测方法。本文充分利用OBIA及随机森林机器学习算法的优势,提出了利用随机森林进行面向对象的遥感影像变化检测。首先基于熵率对影像进行超像素分割,通过最优超像素个数评价指数来获取最佳的影像分割结果,并提取每个超像素在前、后时相影像上的光谱特征和Gabor特征作为随机森林的特征输入数据,用于模型的训练。在初始像素级检测结果之上,自动进行分类样本选择并构建分类器模型,用训练好的模型来提取最终的变化区域。利用Quickbird、IKONOS、SPOT-5等3组多光谱影像进行试验,结果表明,本文方法在变化检测精度上要优于对比方法。 相似文献
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珊瑚礁对于海洋生态环境研究具有重要意义,通过分析珊瑚礁底栖物质的分布及健康状况,可以对珊瑚礁生态环境进行评估。本文提出了一种基于面向对象的图像分类方法,通过试验确定不同地貌的最优分割尺度,其中陆地和深海的最优分割尺度为150,各类底栖物质的最优分割尺度为30。以Sentinel-2A卫星遥感影像为例,提取海南三亚珊瑚礁自然保护区的珊瑚礁底栖物质,并使用混淆矩阵对提取结果进行精度评估。结果表明,底栖物质提取总体分类精度为87.91%,Kappa系数为0.83。面向对象分类方法可有效结合珊瑚礁底栖物质的纹理特征和光谱特征,并充分利用遥感影像不同波段的组合特性,可为三亚珊瑚礁保护管理提供方法支撑。 相似文献
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WorldView-3卫星在8个可见光-近红外(VNIR)波段的基础上,新增了8个短波红外(SWIR)波段,大大提高了对地物信息的提取能力。利用随机森林分类方法分别对可见光-近红外8个波段影像和可见光-近红外-短波红外16个波段的影像进行实验验证;采用基于多尺度分割技术的面向对象方法对合理的特征空间进行实验与挑选。结果表明,引入SWIR波段后分类性能总体精度提升了3.78%,人工地物制图精度提升了5.65%,自然地物制图精度提升了2.88%;且允许识别特定类别(村镇中的红色低矮砖瓦房居民区),能保持地物较为完整的形状信息,可提高多光谱遥感影像的分类精度。 相似文献
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为了提高农业遥感数据处理中多光谱影像分割的精度,文章提出了一种面向农田信息提取的遥感影像分割算法:利用KMeans非监督分类算法和Fisher标准估算多光谱遥感影像中各个波段的权值,并将估算的波段权值应用到光谱合并计算中,能够较好地提高农田区域的分割精度,实现基于全局最优合并的区域生长算法,得到最优化的分割结果;从分割结果中提取基于区域的NDVI信息可以较为快速、准确地区分农田和非农田区域。实验结果说明:该方法的分割精度优于传统的全局最优合并算法和FNEA算法,并对遥感影像中旱田和水田的提取均有较好的效果。 相似文献
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针对南方复杂地区水稻遥感信息提取研究中机器自动学习分类研究较少、分类精度不高的问题,以福建省三明市建宁县溪口镇为研究区,基于GF-1号卫星影像,采用面向对象的随机森林遥感分类算法对研究区内水稻田信息进行提取。首先通过优化面向对象分割参数和随机森林分类模型参数,提取并调用了影像中的多种特征;再对光谱特征、植被指数特征、纹理特征、几何特征进行特征空间优选;最后通过设置4种特征优选试验进行对比,得到最优分类模型。实验结果显示,基于特征空间优选的面向对象随机森林分类算法的水稻提取精度高达90%,分类总体精度可达87%,Kappa系数为0.85;与其他试验结果相比,漏分和误分现象较少,实现了南方地区水稻信息高精度自动识别。该方法计算特征少、实现简便,对于国产高分卫星影像在南方复杂地区作物自动提取中的应用具有参考性。 相似文献
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王晶玉 《测绘与空间地理信息》2023,(S1):195-198
针对利用SAR影像提取林火燃烧区主要侧重于单极化后向散射系数,而从火灾导致散射机制变化的角度开展双极化燃烧区提取的研究较少,本文提出了一种基于极化特征变化的面向对象燃烧区提取方法。以两景Sentinel-1A影像为数据源,构建极化特征变化参数集,使用多尺度分割方法结合随机森林算法提取燃烧区,并采用混淆矩阵精度评价方法对燃烧区提取结果进行评价。实验结果表明,分类总体精度为89.13%,Kappa系数为0.76,说明极化特征变化参数在燃烧区提取方面具有良好的应用能力。 相似文献
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为解决利用Sentinel-2卫星影像进行地物信息提取时云层遮挡造成的信息误判问题,提出了一种基于深度学习的遥感影像云区高精度分割方法。该方法通过预处理的遥感样本数据构建出一种深度神经网络模型,自动提取高层次影像特征;再将影像特征输入分类器,实现遥感影像的像素级分类,从而分割出云覆盖矩阵;最后将云覆盖矩阵转化为云二值图,结合感兴趣区矢量准确获取指定区域云检测结果。选取典型区域进行测试,结果表明：该方法检测精度较高,速度较快,且无须辅助信息与人工干预,可用于Sentinel-2卫星影像不规则区域自动云检测。 相似文献
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遥感模式分类中的空间统计学应用——以面向对象的遥感影像农田提取为例 总被引：4，自引：1，他引：3
如何有效地从遥感图像中提取所需信息,是遥感图像处理和应用的关键,而尺度选择问题一直是影响遥感信息提取精度的关键问题之一。本文论述了利用空间统计学方法解决遥感影像模式分类中的尺度问题的理论基础。针对面向对象影像分析问题,将影响遥感影像多尺度分割的尺度分割参数概括为空间属性分割参数、光谱属性分割参数和影像对象面积阈值参数,并分别提出了基于统计学的尺度参数估计方法。以SPOT-5影像面向对象农田提取为例,基于变异函数方法进行了尺度优选试验,系列尺度分类试验结果表明基于空间统计学尺度估计得到的尺度分割结果进行分类能得到最高的精度,进而证明了基于空间统计学方法进行面向对象信息提取尺度估计的有效性。该方法是完全数据驱动的方法,基本不需要先验知识参与。不同于以往分割后评价的尺度选择方法会占用大量计算资源且耗费大量时间,本文提出的方法不仅能在一定程度上保证面向对象信息提取的精度,而且在一定程度上也提高了面向对象信息提取的效率和自动化程度。 相似文献
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利用高分一号影像结合机载LiDAR数据进行面向对象的亚热带森林年度采伐迹地分类提取。在面向对象的遥感软件Ecognition中,首先利用森林小班数据参与分割,利用小班数据的属性信息确定林地和非林地区域,在林地区域再一次进行多尺度分割,并通过ESP工具确定最佳分割尺度,通过特征表达提取对象的光谱、纹理、形状、冠层高度模型(CHM)等特征信息,通过最小冗余最大相关性(mRMR)特征选择算法提取最优特征子集,且CHM在最优特征子集中。利用随机森林(RF)分类器进行年度森林采伐迹地分类提取。年度采伐迹地提取精度达到了87%,与没有CHM特征参与分类的情况对比,提取精度提高了13%。 相似文献
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融合像素—多尺度区域特征的高分辨率遥感影像分类算法 总被引：1，自引：0，他引：1
针对基于像素多特征的高分辨率遥感影像分类算法的"胡椒盐"现象和面向对象影像分析方法的"平滑地物细节"现象,提出了一种融合像素特征和多尺度区域特征的高分辨率遥感影像分类算法。(1)首先采用均值漂移算法对原始影像进行初始过分割,然后对初始过分割结果进行多尺度的区域合并,形成多尺度分割结果。根据多尺度区域合并RMI指数变化和分割尺度对分类精度的影响,确定最优分割尺度。(2)融合光谱特征、像元形状指数PSI(Pixel Shape Index)、初始尺度和最优尺度区域特征,并对多类型特征进行归一化,最后结合支持向量机(SVM)进行分类。实验结果表明该算法既能有效减少基于像素多特征的高分辨率遥感影像分类算法的"胡椒盐"现象,又能保持地物对象的完整性和地物细节信息,提高易混淆类别(如阴影和街道,裸地和草地)的分类精度。 相似文献
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Ismail Colkesen Taskin Kavzoglu 《Journal of the Indian Society of Remote Sensing》2018,46(8):1233-1242
With the increase in spatial resolution of recent sensors, object-based image analysis (OBIA) has gained importance for producing detailed land use maps. One of the main advantages of OBIA is that a variety of spectral, spatial and textural features can be extracted for the segmented image objects that are later utilized in classification. However, using a large number of features not only increases the required computational time, but also requires a large number of ground samples, which is unavailable in most cases. For these reasons, feature selection (FS) has become an important research topic for OBIA based classification studies. In this study, three filter-based FS algorithms namely, Chi square, information gain and ReliefF were applied to determine the most effective object features that ensure high separability among landscape features. For this purpose, importance degree (i.e. ranks) of 110 input object features were firstly estimated by the algorithms, and correlation-based merit function was then applied to determine optimum feature subset size. Multi-resolution segmentation algorithm was applied for segmenting a WorldView-2 image. Support vector machine, random forest and nearest neighbour classifiers were all utilized to classify segmented image objects using the selected object features. Results revealed that the FS algorithms were effective for selecting the most relevant features. Also, the classifiers produced the highest performances with 24 out of 110 features selected by the information gain (IG) algorithm. Particularly, the support vector machine classifier produced the highest overall accuracy (92.00%) with 24 selected features determined by the IG algorithm. A significant improvement of about 4% was achieved by applying FS procedures that was found statistically significant in terms of Wilcoxon signed-ranks test. 相似文献
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Accurate information on the conditions of road asphalt is necessary for economic development and transportation management. In this study, object-based image analysis (OBIA) rule-sets are proposed based on feature selection technique to extract road asphalt conditions (good and poor) using WorldView-2 (WV-2) satellite data. Different feature selection techniques, including support vector machine (SVM), random forest (RF) and chi-square (CHI) are evaluated to indicate the most effective algorithm to identify the best set of OBIA attributes (spatial, spectral, textural and colour). The chi-square algorithm outperformed SVM and RF techniques. The classification result based on CHI algorithm achieved an overall accuracy of 83.19% for the training image (first site). Furthermore, the proposed model was used to examine its performance in different areas; and it achieved accuracy levels of 83.44, 87.80 and 80.26% for the different selected areas. Therefore, the selected method can be potentially useful for detecting road conditions based on WV-2 images. 相似文献
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Reliable and up-to-date urban land cover information is valuable in urban planning and policy development. Due to the increasing demand for reliable land cover information there has been a growing need for robust methods and datasets to improve the classification accuracy from remotely sensed imagery. This study sought to assess the potential of the newly launched Landsat 8 sensor’s thermal bands and derived vegetation indices in improving land cover classification in a complex urban landscape using the support vector machine classifier. This study compared the individual and combined performance of Landsat 8’s reflective, thermal bands and vegetation indices in classifying urban land use-land cover. The integration of Landsat 8 reflective bands, derived vegetation indices and thermal bands overall produced significantly higher accuracy classification results than using traditional bands as standalone (i.e. overall, user and producer accuracies). An overall accuracy above 89.33% and a kappa index of 0.86, significantly higher than the one obtained with the use of the traditional reflective bands as a standalone data-set and other analysis stages. On average, the results also indicate high producer and user accuracies (i.e. above 80%) for most of the classes with a McNemar’s Z score of 9.00 at 95% confidence interval showing significant improvement compared with classification using reflective bands as standalone. Overall, the results of this study indicate that the integration of the Landsat 8’s OLI and TIR data presents an invaluable potential for accurate and robust land cover classification in a complex urban landscape, especially in areas where the availability of high resolution datasets remains a challenge. 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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In this work we propose an approach for mapping flooded areas from Sentinel-2 MSI (Multispectral Instrument) data based on soft fuzzy integration of evidence scores derived from both band combinations (i.e. Spectral Indices - SIs) and components of the Hue, Saturation and Value (HSV) colour transformation. Evidence scores are integrated with Ordered Weighted Averaging (OWA) operators, which model user’s decision attitude varying smoothly between optimistic and pessimistic approach. Output is a map of global evidence degree showing the plausibility of being flooded for each pixel of the input Sentinel-2 (S2) image. Algorithm set up and validation were carried out with data over three sites in Italy where water surfaces are extracted from stable water bodies (lakes and rivers), natural hazard flooding, and irrigated paddy rice fields. Validation showed more than satisfactory accuracy for the OR-like OWA operators (F-score > 0.90) with performance slightly decreased (F-score < 0.75) over heterogeneous conditions (e.g. rice fields). The algorithm was applied with no changes and/or tuning to independent sites from the Copernicus Emergency Management Service (EMS) activations to simulate operational conditions. Over these sites, the proposed approach achieved greater, more consistent and robust mapping accuracy compared to traditional approaches based on the segmentation of single input features. Moreover, OWA operators offer an appealing way of combining and aggregating multiple information in decision making by modelling uncertainty in decision process. 相似文献
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Maria Kampouri Polychronis Kolokoussis Demetre Argialas Vassilia Karathanassi 《国际地球制图》2013,28(12):1273-1285
AbstractThe aim of this study is to investigate the potential of Sentinel-2 imagery for the identification and determination of forest patches of particular interest, with respect to ecosystem integrity and biodiversity and to produce a relevant biodiversity map, based on Simpson’s diversity index in Taxiarchis university research forest, Chalkidiki, North Greece. The research is based on OBIA being developed on to bi-temporal summer and winter Sentinel-2 imagery. Fuzzy rules, which are based on topographic factors, such as terrain elevation and slope for the distribution of each tree species, derived from expert knowledge and field observations, were used to improve the accuracy of tree species classification. Finally, Simpson’s diversity index for forest tree species, was calculated and mapped, constituting a relative indicator for biodiversity for forest ecosystem organisms (fungi, insects, birds, reptiles, mammals) and carrying implications for the identification of patches prone to disturbance or that should be prioritized for conservation. 相似文献