共查询到20条相似文献，搜索用时 46 毫秒
1.
Martin Yemefack Wietske Bijker Steven M. De Jong 《International Journal of Applied Earth Observation and Geoinformation》2006
This paper investigates statistical relationships between land use/land cover (LULC), Landsat-7 ETM+ imagery and landscape mosaic structure in southern Cameroon where the conversion of tropical rain forest to shifting cultivation leads to dynamic processes, acting on the spatial aggregation of various LULC types. A Global Positioning System (GPS) was used in the field to identify a total of 171 shifting cultivation patches representing eight LULC types in two sub-areas. Because of the lack of a cloud-free image for the date of field sampling, the ETM+ imagery was acquired 2 months after field survey, during which it was assumed that no significant changes in LULC occurred (all dry season). Per pixel correlations were developed between spectral reflectance data, vegetation indices and LULC. As an exploratory study, several statistical methods (analysis of variance, means separations (Tukey HSD), principal component analysis (PCA), geo-statistical analysis, image classification and landscape metrics) were applied on point data and sensor images for evaluating the spatial variability within the landscape. Most variables explained 30–72% of LULC variation in the whole dataset. Those variables with high information content of LULC (infrared bands 4, 5, 7 and derived indices and PC1) also showed long ranges (6 km) spatial dependence as compared to those varying only within 1 km range. The results of these statistical analyses suggested the need to group some LULC types and the application of the Maximum Likelihood Classifier (MLC) for supervised classification provided a LULC map with the highest accuracy (81%) after consolidation of perennial LULC types, such as bush fallow, forest fallow and cocoa plantations. Landscape metrics computed from this map showed a high level of patch diversity and connectivity within the landscape and provided input data that can further be used to simulate predictive maps as substitute to cloud-covered sensor imageries. Landsat-7 ETM+ imagery proved to be useful in discriminating (with about 80% accuracy) the most dynamic LULC types such cropped plots and young fallow patches (shifting every season) and the extension front of the agricultural landscape. 相似文献
2.
Taskin Kavzoglu Hasan Tonbul Merve Yildiz Erdemir Ismail Colkesen 《Journal of the Indian Society of Remote Sensing》2018,46(8):1297-1306
Object-based image analysis (OBIA) has attained great importance for the delineation of landscape features, particularly with the accessibility to satellite images with high spatial resolution acquired by recent sensors. Statistical parametric classifiers have become ineffective mainly due to their assumption of normal distribution, vast increase in the dimensions of the data and availability of limited ground sample data. Despite pixel-based approaches, OBIA takes semantic information of extracted image objects into consideration, and thus provides more comprehensive image analysis. In this study, Indian Pines hyperspectral data set, which was recorded by the AVIRIS hyperspectral sensor, was used to analyse the effects of high dimensional data with limited ground reference data. To avoid the dimensionality curse, principal component analysis (PCA) and feature selection based on Jeffries–Matusita (JM) distance were utilized. First 19 principal components representing 98.5% of the image were selected using the PCA technique whilst 30 spectral bands of the image were determined using JM distance. Nearest neighbour (NN) and random forest (RF) classifiers were employed to test the performances of pixel- and object-based classification using conventional accuracy metrics. It was found that object-based approach outperformed the traditional pixel-based approach for all cases (up to 18% improvement). Also, the RF classifier produced significantly more accurate results (up to 10%) than the NN classifier. 相似文献
3.
The quantitative evaluation of key factors of soil variability together with the spatial pattern of underlying variables are important steps in understanding the functioning of soilscapes. Fifteen physical and chemical variables describing 24 pedons were used to elucidate processes of soil differentiation in a humid forest environment in Southern Cameroon. Landscape positions delineated on the basis of remote-sensed imageries included flat convex summits (FCS), linear valley slopes (LVS), colluvial inland valleys (CIV), and alluvial floodplain. Ordination by principal component analysis (PCA) resulted in a three-factor model, which efficiently summarized the dataset explaining 81.4% of total soil variance (TSV). The pedogenetic factor of ferralitization emerged as the main factor accounting for 43.7% of TSV. Identified underlying processes included basic cation leaching, solum acidification, and in situ clay build up. Cation exchange properties as influenced by organic constituents and soil available P were the second and third most important factors associated with 26.7 and 11.4% of TSV respectively. Organic constituents and related biological processes had strong control on nutrient availability and cation-exchange properties, while soil available phosphorus (AP) showed a strong dependence on potential acidity and organic matter mineralization status. The analysis of variance highlighted significant differences for most soil properties across landscape position and soil depth except for organic carbon (OC), total nitrogen (TN), and total phosphorus. All the soils, however, have low pH, low basic cation saturation, and correlatively high exchangeable Al irrespective of their position in the landscape. 相似文献
4.
针对传统主成分分析(PCA)忽视测站各坐标分量之间相关性的问题,提出了一种小波去噪和多方向主成分分析(WD-MPCA)组合的方法. 该方法弥补了传统PCA的缺陷,与经验模态分解和主成分分析(EMD-PCA)组合方法及小波去噪和主成分分析(WD-PCA)组合方法相比,WD-MPCA组合方法精度最高. 经WD-MPCA组合方法去噪后,其平均中误差分别为0.83 mm、0.85 mm和8.30 mm,比原始坐标残差时间序列的平均中误差分别降低了81.14%、81.91%和40.37%. WD-MPCA组合方法充分考虑了各测站不同分量之间的相关性,可以有效去除信号中的高频随机白噪声(WN)和低频有色噪声(CN),这对高频全球卫星导航系统(GNSS)技术的实际应用和理论发展具有重要的意义. 相似文献
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6.
Abstract Both principal component analysis (PCA) and principal factor analysis (PFA) were used to analyze an experimental multispectral data structure in terms of common and unique variance. Only the common variance of the multispectral data was associated with the principal factor, while higher‐order principal components were associated with both common and unique variance. The unique variance was found to represent small spectral variations within each cover type as well as noise vectors, and was most abundant in the lower‐order principal components. The lower‐order principal components can be useful in research designed to discriminate minor physical variations within features, and to highlight localized change when using multitemporal‐multispectral data. Conversely, PFA of the multispectral data provided an insight into a great potential for discriminating basic land‐cover types by excluding the unique variance which was related to the noise and minor spectral variations. 相似文献
7.
Salim Lamine Sudhir Kumar Singh Szilárd Szabó Nour El Islam Bachari Prashant K. Srivastava 《国际地球制图》2018,33(8):862-878
This study aims to quantify the landscape spatio-temporal dynamics including Land Use/Land Cover (LULC) changes occurred in a typical Mediterranean ecosystem of high ecological and cultural significance in central Greece covering a period of 9 years (2001–2009). Herein, we examined the synergistic operation among Hyperion hyperspectral satellite imagery with Support Vector Machines, the FRAGSTATS® landscape spatial analysis programme and Principal Component Analysis (PCA) for this purpose. The change analysis showed that notable changes reported in the experimental region during the studied period, particularly for certain LULC classes. The analysis of accuracy indices suggested that all the three classification techniques are performing satisfactorily with overall accuracy of 86.62, 91.67 and 89.26% in years 2001, 2004 and 2009, respectively. Results evidenced the requirement for taking measures to conserve this forest-dominated natural ecosystem from human-induced pressures and/or natural hazards occurred in the area. To our knowledge, this is the first study of its kind, demonstrating the Hyperion capability in quantifying LULC changes with landscape metrics using FRAGSTATS® programme and PCA for understanding the land surface fragmentation characteristics and their changes. The suggested approach is robust and flexible enough to be expanded further to other regions. Findings of this research can be of special importance in the context of the launch of spaceborne hyperspectral sensors that are already planned to be placed in orbit as the NASA’s HyspIRI sensor and EnMAP. 相似文献
8.
TerraSAR-X satellite acquires very high spatial resolution data with potential for detailed land cover mapping. A known problem with synthetic aperture radar (SAR) data is the lack of spectral information. Fusion of SAR and multispectral data provides opportunities for better image interpretation and information extraction. The aim of this study was to investigate the fusion between TerraSAR-X and Landsat ETM+ for protected area mapping using high pass filtering (HPF), principal component analysis with band substitution (PCA) and principal component with wavelet transform (WPCA). A total of thirteen land cover classes were identified for classification using a non-parametric C 4.5 decision tree classifier. Overall classification accuracies of 74.99%, 83.12% and 85.38% and kappa indices of 0.7220, 0.8100 and 0.8369 were obtained for HPF, PCA and WPCA fusion approaches respectively. These results indicate a high potential for a combined use of TerraSAR-X and Landsat ETM+ data for protected area mapping in Uganda. 相似文献
9.
A. Soliman R. Brown R.J. Heck 《International Journal of Applied Earth Observation and Geoinformation》2011
Principal component analysis has been applied to remote sensing data to identify spatiotemporal patterns in a time series of images. Thermal inertia is a surface property that relates well to shallow surface thermal and physical properties. Mapping thermal inertia requires quantifying surface energy balance components and soil heat flux, both of which are difficult to measure remotely. This article describes a method to map soil thermal inertia using principal component analysis applied to a time series of thermal infrared images and it also assesses how sensitive this method is to the time intervals between images. Standardized principal component analysis (SPCA) was applied to thermal infrared images captured at half-hour intervals during a complete diurnal cycle. Shallow surface thermal properties accounted for 45%, 82% and 66% of the spatiotemporal variation in surface temperature observed during the heating phase, cooling phase and over the total diurnal cycle respectively. The remaining 55%, 18% and 34% of the variation was attributed to transient effects such as shadows, surface roughness and background noise. Signals related to thermal inertia explained 18% of total variation observed in a complete diurnal cycle and 7% of variation in the cooling series. The SPCA method was found useful to separate critical information such as timing and amplitude of maximum surface temperature variation from delays related to differential heating induced by micro-topography. For the field conditions experienced in this study, decreased temporal resolution when sampling intervals were greater than an hour significantly reduced the quality of results. 相似文献
10.
Impervious surface is an important environmental and socio-economic indicator for numerous urban studies. While a large number of researches have been conducted to estimate the area and distribution of impervious surface from satellite data, the accuracy for impervious surface estimation (ISE) is insufficient due to high diversity of urban land cover types. This study evaluated the use of panchromatic (PAN) data in very high resolution satellite image for improving the accuracy of ISE by various pan-sharpening approaches, with a further comprehensive analysis of its scale effects. Three benchmark pan-sharpening approaches, Gram-Schmidt (GS), PANSHARP and principal component analysis (PCA) were applied to WorldView-2 in three spots of Hong Kong. The on-screen digitization were carried out based on Google Map and the results were viewed as referenced impervious surfaces. The referenced impervious surfaces and the ISE results were then re-scaled to various spatial resolutions to obtain the percentage of impervious surfaces. The correlation coefficient (CC) and root mean square error (RMSE) were adopted as the quantitative indicator to assess the accuracy. The accuracy differences between three research areas were further illustrated by the average local variance (ALV) which was used for landscape pattern analysis. The experimental results suggested that 1) three research regions have various landscape patterns; 2) ISE accuracy extracted from pan-sharpened data was better than ISE from original multispectral (MS) data; and 3) this improvement has a noticeable scale effects with various resolutions. The improvement was reduced slightly as the resolution became coarser. 相似文献
11.
Segmented Principal Component Analysis for Parallel Compression of Hyperspectral Imagery 总被引：1，自引：0，他引：1
Principal component analysis (PCA) is widely used for spectral decorrelation in the JPEG2000 compression of hyperspectral imagery. However, due to the data-dependent nature of principal components, the principal component transform matrix is stored in the JPEG2000 bitstream, constituting an overhead that is often negligible if the spatial size of the image is large. However, in parallel compression in which the data set is partitioned to multiple independent processing nodes, the overhead may no longer remain negligible. It is shown that a segmented approach to PCA can greatly mitigate the detrimental effects of transform-matrix overhead and can outperform wavelet-based decorrelation which entails no such overhead. 相似文献
12.
Jingbin Liu Manuel Hernandez-Pajares Xinlian Liang Jiachun An Zemin Wang Ruizhi Chen Wei Sun Juha Hyyppä 《Journal of Geodesy》2017,91(5):485-502
By utilizing the numerical technique of principal component analysis (PCA), this work analyses temporal and spatial variations of the ionosphere under various solar conditions during the period 1999–2013. Applying the PCA technique to the time series of the global ionospheric total electron content (TEC) maps provides an efficient method for analyzing the main ionospheric variability on a global scale that is able to decompose periodic variations (e.g., annual and semiannual oscillations) while retaining the asymmetry in the temporal and spatial domains (e.g., seasonal and equator anomalies). The TEC series of different local times are processed separately at two time scales: (1) the whole 15 years of the period of study and (2) the individual years. In contrast with previous studies, the analysis of the dataset of the 15 years shows that dawn (e.g., LT4–6) and late morning (LT10–12) are the more remarkable characteristic times for ionospheric variability. This study also reveals a cyclic trend of the variability with respect to local times. The first two modes, which contain 80–90% of the total variance, represent spatial distributions and temporal variations with respect to the different stages of the solar cycle and local times. Annual and semiannual variations are demodulated from the first two modes, and the results show that these variations evidently have distinct trends for daytime and nighttime. An exception is that, under active solar conditions, extremely strong solar irradiance during the daytime has a residual effect on the variability of the nighttime. 相似文献
13.
Protected areas are experiencing increased levels of human pressure. To enable appropriate conservation action, it is critical to map and monitor changes in the type and extent of land cover/use and habitat classes, which can be related to human pressures over time. Satellite Earth observation (EO) data and techniques offer the opportunity to detect such changes. Yet association with field information and expert interpretation by ecologists is required to interpret, qualify and link these changes to human pressure. There is thus an urgent need to harmonize the technical background of experts in the field of EO data analysis with the terminology of ecologists, protected area management authorities and policy makers in order to provide meaningful, context-specific value-added EO products. This paper builds on the DPSIR framework, providing a terminology to relate the concepts of state, pressures, and drivers with the application of EO analysis. The type of pressure can be inferred through the detection of changes in state (i.e. changes in land cover and/or habitat type and/or condition). Four broad categories of changes in state are identified, i.e. land cover/habitat conversion, land cover/habitat modification, habitat fragmentation and changes in landscape connectivity, and changes in plant community structure. These categories of change in state can be mapped through EO analyses, with the goal of using expert judgement to relate changes in state to causal direct anthropogenic pressures. Drawing on expert knowledge, a set of protected areas located in diverse socio-ecological contexts and subject to a variety of pressures are analysed to (a) link the four categories of changes in state of land cover/habitats to the drivers (anthropogenic pressure), as relevant to specific target land cover and habitat classes; (b) identify (for pressure mapping) the most appropriate spatial and temporal EO data sources as well as interpretations from ecologists and field data useful in connection with EO data analysis. We provide detailed examples for two protected areas, demonstrating the use of EO data for detection of land cover/habitat change, coupled with expert interpretation to relate such change to specific anthropogenic pressures. We conclude with a discussion of the limitations and feasibility of using EO data and techniques to identify anthropogenic pressures, suggesting additional research efforts required in this direction. 相似文献
14.
基于中国农业科学院在呼伦贝尔草原实测的120组草地冠层光谱反射率及相应的叶面积指数(LAI)数据,在进行主成分分析(PCA)实现降维处理的基础上,利用径向基函数(radial basis function,RBF)神经网络方法对草地LAI进行了高光谱反演研究.PCA结果表明,前9个主成分的累积贡献率达到了99.782％,能包含原光谱数据的绝大部分信息.将120组LAI及相应的9个主成分样本数据随机分为校正集数据(90组)和预测集数据(30组),分别用于神经网络模型的建立和LAI的预测.所构建的神经网络模型的模拟结果表明,RBF神经网络模型对校正集样本的模拟准确率达到100％(RMSE =0.009 6,R2 =0.999);预测集样本的实测LAI和模拟LAI之间的均方误差和决定系数分别为0.218 6和0.839,取得了较好的模拟效果,有效提高了传统的多元线性回归方程(RMSE =0.416 5,R2=0.570)的计算精度. 相似文献
15.
基于蚁群优化的特征选择新方法 总被引：3，自引：0，他引：3
利用蚁群优化算法解决特征选择问题，以获得能代表问题空间的较优特征子集，并能降低分类系统的搜索空间。以航空纹理影像的特征选择和分类问题为例，利用主分量变换和蚁群优化算法分别对原始纹理影像特征集合进行特征提取、选择和分类。结果表明，本文方法不仅能够降低图像特征空间维数，减少图像分类的工作量，而且还可以提高分类识别的正确率。 相似文献
16.
面向对象遥感影像分类过程中,特征选择是保证分类精度和提高分类速度的关键因素。本文针对高分影像特征过多造成维度灾难、无法取舍有效特征导致低分类精度等问题,提出了一种基于特征贡献度与主成分分析(PCA)结合的特征选择优化方法,定量分析并提取影像特征。本文首先利用特征贡献度进行特征选择,提取有效特征;然后进行PCA变换消除特征间相互影响,降低维度,将提取的143个影像分类特征经选择与变换至20个主成分特征,最终优化的特征在神经网络(ANN)、K最近邻法(KNN)和支持向量机(SVM)三种分类实验结果中的总精度分别提高了10.56%、7.78%和6.11%,实现了较好的分类效果,说明优化的特征选择方法不仅大大降低了特征维度,减少了后端分类计算量,同时有效提高了分类精度。 相似文献
17.
Yangrong Ling Manfred Ehlers E. Lynn Usery Marguerite Madden 《ISPRS Journal of Photogrammetry and Remote Sensing》2007,61(6):381-392
Existing image fusion techniques such as the intensity–hue–saturation (IHS) transform and principal components analysis (PCA) methods may not be optimal for fusing the new generation commercial high-resolution satellite images such as Ikonos and QuickBird. One problem is color distortion in the fused image, which causes visual changes as well as spectral differences between the original and fused images. In this paper, a fast Fourier transform (FFT)-enhanced IHS method is developed for fusing new generation high-resolution satellite images. This method combines a standard IHS transform with FFT filtering of both the panchromatic image and the intensity component of the original multispectral image. Ikonos and QuickBird data are used to assess the FFT-enhanced IHS transform method. Experimental results indicate that the FFT-enhanced IHS transform method may improve upon the standard IHS transform and the PCA methods in preserving spectral and spatial information. 相似文献
18.
This study proposes a landscape metrics-based method for model performance evaluation of land change simulation models. To quantify model performance at both landscape and class levels, a set of composition- and configuration-based metrics including number of patches, class area, landscape shape index, mean patch area and mean Euclidean nearest neighbour distance were employed. These landscape metrics provided detailed information on simulation success of a cellular automata-Markov chain (CA-Markov) model standpoint of spatial arrangement of the simulated map versus the corresponding reference layer. As a measure of model simulation success, mean relative error (MRE) of the metrics was calculated. At both landscape and class levels, the MRE values were accounted for 22.73 and 10.2%, respectively, which are further categorised into qualitative measurements of model simulation performance for simple and quick comparison of the results. Findings of the present study depict a hierarchical and multi spatial level assessment of model performance. 相似文献
19.
Chamundeeswari V.V. Singh D. Singh K. 《Geoscience and Remote Sensing Letters, IEEE》2009,6(2):214-218
In single-band single-polarized SAR images, intensity and texture are the information source available for unsupervised land cover classification. Every textural feature measure identifies texture patterns by different approaches. For efficient land cover classification, textural measures have to be chosen suitably. Therefore, in this letter, the role of various intensity and textural measures is analyzed for their discriminative ability for unsupervised SAR image classification into various land cover types like water, urban, and vegetation areas. To make the algorithm adaptable, these textural features are fused using principal component analysis (PCA), and principal components are used for classification purposes. To highlight the effectiveness of PCA, the difference between PCA- and non-PCA-based classifications is also analyzed. Analysis of the role of texture measures for unsupervised classification of real-world SAR data with application of PCA is presented in this letter. The analysis of how every individual feature measure contributes for classification process is presented, and then, textural measures for a feature set are chosen according to their role in improving classification accuracy. By analysis, it is observed that the feature set comprising mean, variance, wavelet components, semivariogram, lacunarity, and weighted rank fill ratio provides good classification accuracy of up to 90.4% than by using individual textural measures, and this increased accuracy justifies the complexity involved in the process. 相似文献
20.
Sermin Tagil Sevgi Gormus Serhat Cengiz 《Journal of the Indian Society of Remote Sensing》2018,46(8):1285-1296
Perforation, dissection, fragmentation, shrinkage and attrition in ecosystems take place due to urbanization. In this study, where and when temporal and spatial heterogeneity occurs is tried to be explained by taking human intervention in landscape pattern and processes in and around the city of Denizli into account and how this heterogeneity affects habitat conditions within the scope of landscape ecology. Landscape pattern metrics were estimated in order to reveal the change in habitats and present the properties of the landscape. 30 pattern indicators on class and pattern levels, which are important to show human–environment interaction, were analyzed in order to indicate the features of the landscape such as area, side, shape and dispersion. To this end, LANDSAT TM/7–ETM/8-OLI satellite images of 1987 and 2013 were classified for laying the foundations of the analysis. Analyses showed that between 1987 and 2013, complicated shape features, increase in edge habitats, de-growth in core areas and eventually fragmentation in landscape have been dominant. Heterogenic structure in landscape has increased. This points not to the self-functioning of the landscape, but to the domination of human intervention over the landscape. Particularly, due to urban growth and sprawl, fragmentation, isolation and habitat loss in croplands have increased. This study sets forth the usefulness of remote sensing, GIS and landscape metrics in understanding how urban dynamics and ecosystems change in developing urban politics. 相似文献