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1.
ABSTRACT

While impressive direct geolocation accuracies better than 5.0?m CE90 (90% of circular error) can be achieved from the last DigitalGlobe’s Very High Resolution (VHR) satellites (i.e. GeoEye-1 and WorldView-1/2/3/4), it is insufficient for many precise geodetic applications. For these sensors, the best horizontal geopositioning accuracies (around 0.55?m CE90) can be attained by using third-order 3D rational functions with vendor’s rational polynomial coefficients data refined by a zero-order polynomial adjustment obtained from a small number of very accurate ground control points (GCPs). However, these high-quality GCPs are not always available. In this work, two different approaches for improving the initial direct geolocation accuracy of VHR satellite imagery are proposed. Both of them are based on the extraction of three-dimensional GCPs from freely available ancillary data at global coverage such as multi-temporal information of Google Earth and the Shuttle Radar Topography Mission 30?m digital elevation model. The application of these approaches on WorldView-2 and GeoEye-1 stereo pairs over two different study sites proved to improve the horizontal direct geolocation accuracy values around of 75%.  相似文献   

2.
ABSTRACT

A 3D forest monitoring system, called FORSAT (a satellite very high resolution image processing platform for forest assessment), was developed for the extraction of 3D geometric forest information from very high resolution (VHR) satellite imagery and the automatic 3D change detection. FORSAT is composed of two complementary tasks: (1) the geometric and radiometric processing of satellite optical imagery and digital surface model (DSM) reconstruction by using a precise and robust image matching approach specially designed for VHR satellite imagery, (2) 3D surface comparison for change detection. It allows the users to import DSMs, align them using an advanced 3D surface matching approach and calculate the 3D differences and volume changes (together with precision values) between epochs. FORSAT is a single source and flexible forest information solution, allowing expert and non-expert remote sensing users to monitor forests in three and four (time) dimensions. The geometric resolution and thematic content of VHR optical imagery are sufficient for many forest information needs such as deforestation, clear-cut and fire severity mapping. The capacity and benefits of FORSAT, as a forest information system contributing to the sustainable forest management, have been tested and validated in case studies located in Austria, Switzerland and Spain.  相似文献   

3.
Population growth worldwide leads to an increasing pressure on the land. Recent studies reported that many areas covered by badlands are decreasing because parts of badlands are being levelled and converted into arable land. It is important to monitor these changes for environmental planning. This paper proposes a remote-sensing-based detection method which allows mapping of badland dynamics based on seasonal vegetation changes in the lower Chambal valley, India. Supervised classification was applied on three Landsat (Thematic Mapper) images, from 3 different seasons; January (winter), April (summer) and October (post-monsoon). Different band selection methods were applied to get the best classification. Validation was done by ground referencing and a GeoEye-1 satellite image. The image from January performed best with overall accuracy of 87% and 0.69 of kappa. This method opens the possibilities of using semi-automatic classification for the Chambal badlands which is so far mapped with manual interpretations only.  相似文献   

4.
In human cognition, both visual features (i.e., spectrum, geometry and texture) and relational contexts (i.e. spatial relations) are used to interpret very-high-resolution (VHR) images. However, most existing classification methods only consider visual features, thus classification performances are susceptible to the confusion of visual features and the complexity of geographic objects in VHR images. On the contrary, relational contexts between geographic objects are some kinds of spatial knowledge, thus they can help to correct initial classification errors in a classification post-processing. This study presents the models for formalizing relational contexts, including relative relations (like alongness, betweeness, among, and surrounding), direction relation (azimuth) and their combination. The formalized relational contexts were further used to define locally contextual regions to identify those objects that should be reclassified in a post-classification process and to improve the results of an initial classification. The experimental results demonstrate that the relational contexts can significantly improve the accuracies of buildings, water, trees, roads, other surfaces and shadows. The relational contexts as well as their combinations can be regarded as a contribution to post-processing classification techniques in GEOBIA framework, and help to recognize image objects that cannot be distinguished in an initial classification.  相似文献   

5.
The characteristics of very high resolution (VHR) satellite data are encouraging development agencies to investigate its use in monitoring and evaluation programmes. VHR data pose challenges for land use classification of heterogeneous rural landscapes as it is not possible to develop generalised and transferable land use classification definitions and algorithms. We present an operational framework for classifying VHR satellite data in heterogeneous rural landscapes using an object-based and random forest classifier. The framework overcomes the challenges of classifying VHR data in anthropogenic landscapes. It does this by using an image stack of RGB-NIR, Normalised Difference Vegetation Index (NDVI) and textural bands in a two-phase object-based classification. The framework can be applied to data acquired by different sensors, with different view and illumination geometries, at different times of the year. Even with these complex input data the framework can produce classification results that are comparable across time. Here we describe the framework and present an example of its application using data from QuickBird (2 images) and GeoEye (1 image) sensors.  相似文献   

6.
Abstract

The goal of this research was to explore the utility of very high spatial resolution, digital remotely sensed imagery for monitoring land‐cover changes in habitat preserves within southern California coastal shrublands. Changes were assessed for Los Penasquitos Canyon Preserve, a large open space in San Diego County, over the 1996 to 1999 period for which imagery was available.

Multispectral, digital camera imagery from two summer dates, three years apart, was acquired using the Airborne Data Acquisition and Registration (ADAR) digital‐camera system. These very high resolution (VHR) image data (1m), composed of three visible and one near‐infrared wavebands (V/NIR), were the primary image input for assessing land cover change. Image‐derived datasets generated from georeferenced and registered ADAR imagery included multitemporal overlays and multitemporal band differencing with threshold selection. Two different multitemporal image classifications were generated from these datasets and compared. Single‐date imagery was analyzed interactively with image‐derived datasets and with information from field observations in an effort to discern change types. A ground sampling survey conducted soon after the 1999 image acquisition provided concurrent ground reference data.

Most changes occurring within the three‐year interval were associated with transitional phenological states and differential precipitation effects on herbaceous cover. Variations in air temperatures and timing of rainfall contributed to differences that the seven‐week image acquisition offset may have caused. Disturbance factors of mechanical clearing, erosion, potentially invasive plants, and fire were evident and their influence on the presence, absence, and type of vegetation cover were likely sources of change signals.

The multitemporal VHR, V/NIR image data enabled relatively fine‐scale land cover changes to be detected and identified. Band differencing followed by multitemporal classification provided an effective means for detecting vegetation increase or decrease. Detailed information on short‐term disturbance effects and long‐term vegetation type conversions can be extracted if image acquisitions are carefully planned and geometric and radiometric processing steps are implemented.  相似文献   

7.
The impact of forest management activities on the ability of forest ecosystems to sequester and store atmospheric carbon is of increasing scientific and social concern. This is because a quantitative understanding of how forest management enhances carbon storage is lacking in most forest management regimes. In this paper two forest regimes, government and community-managed, in Kayar Khola watershed, Chitwan, Nepal were evaluated based on field data, very high resolution (VHR) GeoEye-1 satellite image and airborne LiDAR data. Individual tree crowns were generated using multi-resolution segmentation, which was followed by two tree species classification (Shorea robusta and Other species). Species allometric equations were used in both forest regimes for above ground biomass (AGB) estimation, mapping and comparison. The image objects generated were classified per species and resulted in 70 and 82 % accuracy for community and government forests, respectively. Development of the relationship between crown projection area (CPA), height, and AGB resulted in accuracies of R2 range from 0.62 to 0.81, and RMSE range from 10 to 25 % for Shorea robusta and other species respectively. The average carbon stock was found to be 244 and 140 tC/ha for community and government forests respectively. The synergistic use of optical and LiDAR data has been successful in this study in understanding the forest management systems.  相似文献   

8.
刘专 《测绘科学》2011,36(6):201-203
GeoEye-1卫星传感器采用三线阵CCD扫描传感器,拥有空间分辨率高、单景影像地面成像面积大等诸多优点.本文以澳大利亚霍巴特市作为实验区,在ERDAS LPS平台下,结合国家航测规范,试验研究了不同地形和GCPs条件下应用GeoEye-1影像制作DOM的精度及误差分布情况.通过试验我们对应用GeoEye-1影像制作D...  相似文献   

9.
Abstract

This study presents two approaches of textural classification on a synthetic aperture radar (SAR) ERS‐2 image, with the aim of the location of the flows of lava and of the serviced zones around the volcanic site of the Mountain Cameroon. The first approach is a method of progressive supervised classification, where a single class is extracted at the same moment, by using 7 parameters of texture stemming from the run length method. Three classes of texture were extracted by this method and the images from the three classes were merged by the red green blue coloured composition to produce the final map. For purposes of comparison, a not supervised classification was applied to the same image. The applied not supervised classification uses parameters of texture stemming from the co‐occurrence matrix method, and it lays on the detection of the peaks of a histogram. The results obtained by the two methods are coherent, and the validation of the results was made by observations during a recent mission on the site of study.  相似文献   

10.
Focusing on the central Kalahari, this study utilized fractional cover of photosynthetic vegetation (fPV), non-photosynthetic vegetation (fNPV) and bare soil (fBS), derived in situ and estimated from GeoEye-1 imagery using Multiple Endmember Spectral Mixture Analysis (MESMA) and object-based image analysis (OBIA) to determine superior method for fractional cover estimation and the impact of vegetation morphology on the estimation accuracy. MESMA mapped fractional cover by testing endmember models of varying complexity. Based on OBIA, image was segmented at five segmentation scales followed by classification. MESMA provided more accurate fractional cover estimates than OBIA. The increasing segmentation scale in OBIA resulted in a consistent increase in error. Different vegetation morphology types showed varied responses to the changing segmentation scale, reflecting their unique ecology and physiognomy. While areas under woody cover produced lower error even at coarse segmentation scales, those with herbaceous cover provided low error only at the fine segmentation scale.  相似文献   

11.
Multitemporal land cover classification over urban areas is challenging, especially when using heterogeneous data sources with variable quality attributes. A prominent challenge is that classes with similar spectral signatures (such as trees and grass) tend to be confused with one another. In this paper, we evaluate the efficacy of image point cloud (IPC) data combined with suitable Bayesian analysis based time-series rectification techniques to improve the classification accuracy in a multitemporal context. The proposed method uses hidden Markov models (HMMs) to rectify land covers that are initially classified by a random forest (RF) algorithm. This land cover classification method is tested using time series of remote sensing data from a heterogeneous and rapidly changing urban landscape (Kuopio city, Finland) observed from 2006 to 2014. The data consisted of aerial images (5 years), Landsat data (all 9 years) and airborne laser scanning data (1 year). The results of the study demonstrate that the addition of three-dimensional image point cloud data derived from aerial stereo images as predictor variables improved overall classification accuracy, around three percentage points. Additionally, HMM-based post processing reduces significantly the number of spurious year-to-year changes. Using a set of 240 validation points, we estimated that this step improved overall classification accuracy by around 3.0 percentage points, and up to 6 to 10 percentage points for some classes. The overall accuracy of the final product was 91% (kappa = 0.88). Our analysis shows that around 1.9% of the area around Kuopio city, representing a total area of approximately 0.61 km2, experienced changes in land cover over the nine years considered.  相似文献   

12.
邻域粗糙集是一种有效的影像特征提取方法,邻域粗糙集模型存在稳定性不高和邻域半径需要反复调整的不足,难以实现地物特征的自动化提取。提出一种多邻域粗糙集加权特征提取方法用于高分辨率遥感影像特征提取。该方法首先利用不同半径的邻域粗糙集对影像的光谱和纹理特征进行提取,求得不同邻域半径下的有效特征子集;然后统计所有邻域半径下各个特征出现的概率,将概率作为权重与特征进行加权得到最终地物特征。QucikBird影像上分类试验表明本文算法优于传统邻域粗糙集特征提取方法,分类总精度平均提高3.88%,Kappa系数平均提高5.16%。在GeoEye-1影像上的分类试验同样证明了本文方法的有效性。  相似文献   

13.
Abstract

This study proposes an automatic procedure for individual fruit tree identification using GeoEye-1 sensor data. Depending on site-specific pruning practices, the morphologic characteristics of tree crowns may generate one or more brightness peaks (tree top) on the imagery. To optimize tree counting and to minimize typical background noises from orchards (i.e. bare soil, weeds, and man-made objects), a four-step algorithm was implemented with spatial transforms and functions suitable for spaced stands (asymmetrical smoothing filter, local minimum filter, mask layer, and spatial aggregation operator). System performance was evaluated through objective criteria, showing consistent results in fast capturing tree position for precision agriculture tasks.  相似文献   

14.
高分辨率卫星的幅宽一般很小,受云雨和轨道回访周期影响,单颗卫星的短时段内覆盖能力有限,因此,单一高分辨率卫星常常无法满足一定时段内的黑臭水体监测需求,需要多源卫星协同监测黑臭水体。为了分析多源高分辨率影像对黑臭水体遥感监测的适用性,本文基于地物光谱仪实测的水体遥感反射率数据,以GeoEye-1、WorldView-2、北京二号(DMC3)、高景一号SV1(SuperView-1)以及GF-PMS系列(GF-1/1B/1C/1D、GF-2、GF-6)传感器波段进行等效计算,结果表明:(1)采用反射率比值模型——BOI(Black and Odorous water Index)模型,GeoEye-1、WorldView-2、SuperView-1和GF-1/1B/1C/1D/2/6影像识别黑臭水体正确率均较高,分别为89.5%、89.5%、92.1%和92.1%。(2)BOI模型不适用于DMC3,这里采用了归一化水体指数NDWI≤0.55判别黑臭水体,识别正确率为89.5%。(3)BOI模型应用于仅有的2景同步卫星影像——GF-2影像,经实测数据验证,识别精度为83.3%,精度较高。针对通州区内的某重叠区,2016年—2021年10颗多源卫星影像协同观测的结果一致性较好,表明了多源遥感影像监测黑臭水体的适用性较好。综合考虑卫星影像空间分辨率和采购成本,给出了合理的协同观测建议。  相似文献   

15.
Abstract

Coastal wetland is a major part of wetlands in the world. Land cover and vegetation mapping in a deltaic lowland environment is complicated by the rapid and significant changes of geomorphic forms. Remote sensing provides an important tool for coastal land cover classification and landscape analysis. The study site in this paper is the Yellow River Delta Nature Reserve (YRDNR) at the Yellow River mouth in Shangdong province, China. Yellow River Delta is one of the fastest growing deltas in the world. YRDNR was listed as a national level nature reserve in 1992. The objectives of this paper are two fold: to study the land cover status of YRDNR, and to examine the land cover change since it was declared as a nature reserve. Land cover and vegetation mapping in YRDNR was developed using multi‐spectral Landsat Thematic Mapper (TM) imagery acquired in 1995. Land cover and landscape characteristics were analyzed with the help of ancillary GIS. Land use investigation data in 1991 were used for comparison with Landsat classification map. Our results show that YRDNR has experienced significant landscape change and environmental improvement after 1992.  相似文献   

16.
Abstract

This paper aims to evaluate what effect applying residents' urban image to virtual city design (a real time virtual model of an actual city) has on wayfinding performance during 'flying-based' navigation mode. Two experiments were conducted to compare two virtual city designs using the virtual model of Tel Aviv city. One design included highlighted urban elements from the residents' urban image, while in the second design no highlighted elements were included.

The experiments proved that using the elements of the residents' urban image in a virtual city design enhances the performance of all participants in the wayfinding tasks, and especially those with a low level of spatial knowledge. Analysis of the trajectory patterns and the verbal reports of the participants during navigation showed that the urban image design facilitates a more intensive use of a position-based strategy, in addition to the path-integration wayfinding strategy, which was found to be dominant in the virtual model without the highlighted urban image elements. On the basis of these findings we propose principles for designing virtual cities from a perspective of wayfinding.  相似文献   

17.
Multiresolution segmentation (MRS) algorithm has been widely used to handle very-high-resolution (VHR) remote sensing images in the past decades. Unfortunately, segmentation quality is limited by the dependency of parameter selection on users’ experience and diverse images. Contrarily, the segmentation by weighted aggregation (SWA) can partly overcome the above limitations and produce an optimal segmentation for maximizing the homogeneity within segments and the heterogeneity across segments. However, SWA is solely tested and justified with digital photos in computer vision field instead of VHR images. This study aims at evaluating SWA performance on VHR imagery. First, multiscale spectral, shape, and texture features are defined to measure homogeneity of image objects for segmentation. Second, SWA is implemented to handle QuickBird, unmanned aerial vehicle (UAV), and GF-1 VHR images and further compared with MRS in eCognition software to demonstrate the applicability of SWA to diverse images in building, vegetation and water, forest stands, farmland, and mountain areas. Third, the results are fully evaluated with quantitative measurements on segmented objects and classification-based accuracy assessment on geographic information system vector data. The results indicate that SWA can produce higher quality segmentations, need fewer parameters and manual interventions, create fewer segmentation levels, incorporate more features, and obtain larger classification accuracy than MRS.  相似文献   

18.
Abstract

Developing countries like India are an urbanization hotspot with many upcoming towns and cities. Growth in small and medium sized towns and cities have been unnoticed and growing without appropriate urban planning. Utilizing the available medium resolution satellite data and geospatial platforms, the growth dynamics of Kurukshetra city was analysed over a period of 24 years. The study employed a combination of change detection technique and spatial metrics (six each of class and landscape levels) analysis to delineate the growth track of the city and its environs. A significant increase in urban built up (dense 237%; open 1038%) is seen majorly at the cost of open area (70%) and tree clad (58%). Phases of city’s aggregation and diffusion are observed using class and landscape level spatial metrics. Understanding and monitoring of land use changes in and around city limits using integrated spatial tools provide better decision making capability.  相似文献   

19.
Abstract

The analysis of remote sensing (RS) images, which is often accomplished using unsupervised image classification techniques, requires an effective method to determine an appropriate number of classification clusters. This paper proposes a preliminary analytical method to evaluate the input parameters for unsupervised RS image classification. Our approach involves first analysing the colour spaces of RS images based on the human visual perception theory. This enables the initial number of clusters and their corresponding centres to be automatically established based on the interaction of different forces in our supposed force field. The proposed approach can automatically determine the appropriate initial number of clusters and their corresponding centres for unsupervised image classification. A comparison of the experimental results with those of existing methods showed that the proposed method can considerably facilitate unsupervised image classification for acquiring accurate results efficiently and effectively without any prior knowledge.  相似文献   

20.
钟琳颖  沈婕  毛威  王孟和  俞肇元 《遥感学报》2021,25(12):2421-2430
作为人类文明的见证,古城文化遗产让文明记忆更加鲜活,城市生活环境得以延续。叙事地图结合了地图学及叙事学对于空间信息、时间信息的表达优势,不仅是近年来地图学的新兴方向,也为古城文化遗产展现提供了新型空间叙事载体。论文针对古城叙事地图表达需求,从地理学时空视角出发,研究了古城叙事地图的组成要素及其结构与关系,构建了古城叙事地图知识图谱。在此基础上,以清江浦古城为例,设计了古城叙事地图的架构,基于清江浦遥感影像、照片、史志等多源资料,利用倾斜摄影测量与三维实景建模技术构建了古城典型景观建筑模型,设计与实现了多尺度、多载体的古城叙事地图。本研究提出的基于时空知识图谱叙事地图设计思路、基于遥感影像等多源资料的古城景观模型构建技术以及多尺度、多载体的古城文化遗产景观复现方法为古城历史文化遗产研究提供了参考。  相似文献   

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