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891.
New Earth observation missions and technologies are delivering large amounts of data. Processing this data requires developing and evaluating novel dimensionality reduction approaches to identify the most informative features for classification and regression tasks. Here we present an exhaustive evaluation of Guided Regularized Random Forest (GRRF), a feature selection method based on Random Forest. GRRF does not require fixing a priori the number of features to be selected or setting a threshold of the feature importance. Moreover, the use of regularization ensures that features selected by GRRF are non-redundant and representative. Our experiments based on various kinds of remote sensing images, show that GRRF selected features provides similar results to those obtained when using all the available features. However, the comparison between GRRF and standard random forest features shows substantial differences: in classification, the mean overall accuracy increases by almost 6% and, in regression, the decrease in RMSE almost reaches 2%. These results demonstrate the potential of GRRF for remote sensing image classification and regression. Especially in the context of increasingly large geodatabases that challenge the application of traditional methods. 相似文献
892.
基于自然间断点分级法的土地利用数据网格化分析 总被引：4，自引：0，他引：4
土地利用在自然资源统一管理中扮演着重要角色,面对不同区域和年份的数据,统一分析比对口径尤为重要,同时也应反映出相互之间的差异。本文以宜兴市2009年和2017年土地利用现状数据为数据源,首先使用统一的分类标准提取用地类型中的3大类,通过不同大小的单元划分尝试和结果分析,发现适用于该数据的网格尺度大小;然后基于自然间断点分级法进行分级范围划定,对宜兴市三类用地类型的分布和变化趋势进行综合分析,较为真实地反映了宜兴市用地情况;最后通过选用合适的空间尺度和分级范围划定方法,进而构建一个兼具操作性和科学性的土地利用数据网格化方法,为自然资源部门统筹管理和综合治理提供依据。 相似文献
893.
海洋重力场模型反演的质量主要依赖于采用测高数据的精度、空间分辨率和数据分布密集程度。本文联合Geosat GM/ERM、ERS-1 GM/ERM、TOPEX/Poseidon、Envisat、Cryosat-2、Jason-1 ERM/GM和SARAL/AltiKa等多种测高观测数据集,深入比较了多种波形重跟踪算法的效果,回波数据重跟踪处理后的沿轨海面高标准差。统计表明,Sandwell算法优于MLE-4算法、Davis阈值法、改进阈值法和β参数拟合法;基于不同测高数据波形重采样的结果给出了沿轨海面梯度计算中低通滤波的参数选择方法,并采用Sandwell提出的垂线偏差法,反演了全球海域1′×1′的重力场模型。检核表明,反演结果与DTU13和SIO V23.1模型检核的差值均方根分别为3.4、1.8 mGal,与NGDC船测数据的检核精度为4～8 mGal,且本文模型在部分典型海区内精度更优。 相似文献
894.
增强型罗兰导航系统(eLoran)作为全球卫星导航系统(GNSS)的备份系统,是国家定位导航授时(PNT)安全的重要基础设施.针对目前标准eLoran信号存在易受交叉干扰、天波干扰,通信数据传输速率低等的问题,本文基于标准罗兰信号体制提出了两种波形改进方法(衰减函数法与对称波形法)并对新型波形进行性能评估.实验结果表明,两种方法能够有效缩短波形持续时间,加速后沿波形下降,减小发射机功耗.对称波形法能够大幅减小波形持续时间,但缩短波形持续时间也将改变原信号波形的频谱特性,利用衰减函数法可以最大程度保证信号的频谱性能.综合分析可知,新型波形能够有效利用时域资源,空余的时间可用于增加传输信号,进而提高数据调制技术的性能. 相似文献
895.
Feilin Lai 《地理信息系统科学与遥感》2020,57(6):830-844
ABSTRACT Information on urban settlements is crucial for sustainability planning and management. While remote sensing has been used to derive such information, its applicability can be compromised due to the complexity in the urban environment. In this study, we developed a remote sensing method to map land cover types in a large Latin-American city, which is well known for its mushrooming unplanned and informal settlements. After carefully considering the landscape complexity there, we designed a data fusion method combining multispectral imagery and non-spectral data for urban and land mapping. Specifically, we acquired a cloud-free Landsat-8 image and two non-spectral datasets, i.e., digital elevation models and road networks. Then, we implemented a set of experiments with different inputs to evaluate their merits in thematic mapping through a supervised protocol. We found that the map generated with the multispectral data alone had an overall accuracy of 73.3% but combining multispectral imagery and non-spectral data yielded a land cover map with 90.7% overall accuracy. Interestingly, the thermal infrared information helped substantially improve both the overall and categorical accuracies, particularly for the two urban classes. The two types of non-spectral data were critical in resolving several spectrally confused categories, thus considerably increasing the mapping accuracy. However, the panchromatic band with higher spatial resolution and its derived textural measurement only generated a marginal accuracy improvement. The novelties of our work are with the successful separation between the two major types of urban settlements in a complex environment using a carefully designed data fusion approach and the insight into the relative merits of the thermal infrared information and non-spectral data in helping resolve the issue of class ambiguity. These findings should be valuable in deriving accurate urban settlement information which can further advance the research on socio-ecological dynamics and urban sustainability. 相似文献
896.
Information on tree species composition is crucial in forest management and can be obtained using remote sensing. While the topic has been addressed frequently over the last years, the remote sensing-based identification of tree species across wide and complex forest areas is still sparse in the literature. Our study presents a tree species classification of a large fraction of the Białowieża Forest in Poland covering 62 000 ha and being subject to diverse management regimes. Key objectives were to obtain an accurate tree species map and to examine if the prevalent management strategy influences the classification results. Tree species classification was conducted based on airborne hyperspectral HySpex data. We applied an iterative Support Vector Machine classification and obtained a thematic map of 7 individual tree species (birch, oak, hornbeam, lime, alder, pine, spruce) and an additional class containing other broadleaves. Generally, the more heterogeneous the area was, the more errors we observed in the classification results. Managed forests were classified more accurately than reserves. Our findings indicate that mapping dominant tree species with airborne hyperspectral data can be accomplished also over large areas and that forest management and its effects on forest structure has an influence on classification accuracies and should be actively considered when progressing towards operational mapping of tree species composition. 相似文献
897.
针对人工设计的中、低层特征难以实现复杂场景影像的高精度分类以及卷积神经网络依赖大量训练数据等问题,结合迁移学习与集成学习,提出了一种联合卷积神经网络与集成学习的遥感影像场景分类算法。首先基于迁移学习的思想,利用在自然影像数据集上训练好的多个深层卷积神经网络模型作为特征提取器,提取图像多个高度抽象的语义特征;然后构建由Logistic回归和支持向量机组成的Stacking集成模型,对同一图像的多个特征分别训练Logistic模型,将预测概率结果融合构建概率特征;最后利用支持向量机对概率特征训练和预测,得到场景影像的分类结果。利用UCMerced_LandUse和NWPU-RESISC 45两种不同规模的遥感影像数据集进行试验,即使在只有10%的数据作为训练样本情况下,本文方法能够分别达到90.74%和87.21%的分类精度。 相似文献
898.
利用遥感技术能够实现快速提取城市绿地信息,准确地计算出城市绿地面积及覆盖情况等。本文以广州市TM遥感影像为数据源,进行一系列预处理,对监督分类和先计算NDVI再采用非监督分类这两种提取方法进行比较分析。结果表明,先计算NDVI再采用非监督分类法精度较高,说明该方法是一种有效的绿地信息提取方法。 相似文献
899.
Nataliia Kussul Andrii Kolotii Sergii Skakun Olena Rakoid Leonid Shumilo 《International Journal of Digital Earth》2020,13(2):309-321
ABSTRACTFor evaluating the progresses towards achieving the Sustainable Development Goals (SDGs), a global indicator framework was developed by the UN Inter-Agency and Expert Group on Sustainable Development Goals Indicators. In this paper, we propose an improved methodology and a set of workflows for calculating SDGs indicators. The main improvements consist of using moderate and high spatial resolution satellite data and state-of-the-art deep learning methodology for land cover classification and for assessing land productivity. Within the European Network for Observing our Changing Planet (ERA-PLANET), three SDGs indicators are calculated. In this research, harmonized Landsat and Sentinel-2 data are analyzed and used for land productivity analysis and yield assessment, as well as Landsat 8, Sentinel-2 and Sentinel-1 time series are utilized for crop mapping. We calculate for the whole territory of Ukraine SDG indicators: 15.1.1 – ‘Forest area as proportion of total land area’; 15.3.1 – ‘Proportion of land that is degraded over total land area’; and 2.4.1 – ‘Proportion of agricultural area under productive and sustainable agriculture’. Workflows for calculating these indicators were implemented in a Virtual Laboratory Platform. We conclude that newly available high-resolution remote sensing products can significantly improve our capacity to assess several SDGs indicators through dedicated workflows. 相似文献
900.
ABSTRACTResearchers are continually finding new applications of satellite images because of the growing number of high-resolution images with wide spatial coverage. However, the cost of these images is sometimes high, and their temporal resolution is relatively coarse. Crowdsourcing is an increasingly common source of data that takes advantage of local stakeholder knowledge and that provides a higher frequency of data. The complementarity of these two data sources suggests there is great potential for mutually beneficial integration. Unfortunately, there are still important gaps in crowdsourced satellite image analysis by means of crowdsourcing in areas such as land cover classification and emergency management. In this paper, we summarize recent efforts, and discuss the challenges and prospects of satellite image analysis for geospatial applications using crowdsourcing. Crowdsourcing can be used to improve satellite image analysis and satellite images can be used to organize crowdsourced efforts for collaborative mapping. 相似文献