共查询到20条相似文献，搜索用时 15 毫秒
1.
In this study, we test the potential of two different classification algorithms, namely the spectral angle mapper (SAM) and object-based classifier for mapping the land use/cover characteristics using a Hyperion imagery. We chose a study region that represents a typical Mediterranean setting in terms of landscape structure, composition and heterogeneous land cover classes. Accuracy assessment of the land cover classes was performed based on the error matrix statistics. Validation points were derived from visual interpretation of multispectral high resolution QuickBird-2 satellite imagery. Results from both the classifiers yielded more than 70% classification accuracy. However, the object-based classification clearly outperformed the SAM by 7.91% overall accuracy (OA) and a relatively high kappa coefficient. Similar results were observed in the classification of the individual classes. Our results highlight the potential of hyperspectral remote sensing data as well as object-based classification approach for mapping heterogeneous land use/cover in a typical Mediterranean setting. 相似文献
2.
图像分类中基于核映射的光谱匹配度量方法 总被引：1，自引：0，他引：1
针对多光谱遥感数据特点利用SSV匹配技术改进高斯核函数得到新的KSSV函数,然后在由KSSV核函数映射得到的高维空间中利用SAM匹配技术代替基于欧氏距离的相似性度量。如此可以充分挖掘多光谱影像中的波谱特征信息并有效利用,提高模式识别方法应用的有效性。将此方法分别应用于非监督分类(k均值)与监督分类(最小距离、SVM)的试验表明,改进度量的分类方法可显著提高地类间的可区分度并有效降低类内的不一致性,更有效针对多光谱遥感影像中的地物类型,获得较好的精度改进。 相似文献
3.
Land cover mapping forms a reference base for resource managers in their decision-making processes to guide rural/urban growth and management of natural resources. The aim of this study was to map land cover dynamics within the Upper Shire River catchment, Malawi. The article promotes innovation of automated land cover mapping based on remote sensing information to generate data products that are both appropriate to, and usable within different scientific applications in developing countries such as Malawi. To determine land cover dynamics, 1989 and 2002 Landsat images were used. Image bands were combined in transformations and indices with physical meaning; together with spatial data, to enhance classification accuracy. A maximum likelihood classification for each image was computed for identification of land cover variables. The results showed that the combination of spatial and digital data enhanced classification accuracy and the ability to categorise land cover features, which are relatively inhomogeneous. 相似文献
4.
Scott J Goetz Alessandro Baccini Nadine T Laporte Tracy Johns Wayne Walker Josef Kellndorfer Richard A Houghton Mindy Sun 《Carbon balance and management》2009,4(1):2-7
Mapping and monitoring carbon stocks in forested regions of the world, particularly the tropics, has attracted a great deal
of attention in recent years as deforestation and forest degradation account for up to 30% of anthropogenic carbon emissions,
and are now included in climate change negotiations. We review the potential for satellites to measure carbon stocks, specifically
aboveground biomass (AGB), and provide an overview of a range of approaches that have been developed and used to map AGB across
a diverse set of conditions and geographic areas. We provide a summary of types of remote sensing measurements relevant to
mapping AGB, and assess the relative merits and limitations of each. We then provide an overview of traditional techniques
of mapping AGB based on ascribing field measurements to vegetation or land cover type classes, and describe the merits and
limitations of those relative to recent data mining algorithms used in the context of an approach based on direct utilization
of remote sensing measurements, whether optical or lidar reflectance, or radar backscatter. We conclude that while satellite
remote sensing has often been discounted as inadequate for the task, attempts to map AGB without satellite imagery are insufficient.
Moreover, the direct remote sensing approach provided more coherent maps of AGB relative to traditional approaches. We demonstrate
this with a case study focused on continental Africa and discuss the work in the context of reducing uncertainty for carbon
monitoring and markets. 相似文献
5.
利用雷达干涉数据进行城市不透水层百分比估算 总被引：2，自引：0，他引：2
人工不透水层是城市地区的重要特征.作为城市生态环境的关键指数,不透水层百分比(Impervious Surfaces Percentage, ISP)常用于城市水文过程模拟、水质面源污染及城市专题制图等研究中.本文利用ERS-1/2 重复轨道雷达干涉数据,采用分类与回归树(CART)算法探究了雷达遥感在城市ISP估算中的可行性和潜力,并与SPOT5 HRG光学遥感图像的估算结果进行了分析比较.香港九龙港岛实验区的初步研究结果表明,雷达干涉数据在城市不透水层研究中具有一定的应用潜力,特别是裸土和稀疏植被的ISP估算结果要好于光学遥感,这主要得益于雷达干涉数据(特别是长时间相干图像)在人工建筑物和裸土或稀疏植被之间具有很强的区分能力,另外,雷达干涉数据和光学遥感数据间的融合能够提高ISP估算精度. 相似文献
6.
Saygin Abdikan 《国际地球制图》2018,33(1):21-37
Remote sensing data utilize valuable information via various satellite sensors that have different specifications. Image fusion allows the user to combine different spatial and spectral resolutions to improve the information for purposes such as forest monitoring and land cover mapping. In this study, I assessed the contribution of dual-polarized Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar data to multispectral Landsat imagery. The research investigated the separability of forested areas using different image fusion techniques. Quality analysis of the fused images was conducted using qualitative and quantitative analyses. I applied the support vector machine image classification method for land cover mapping. Among all methods examined, the à trous wavelet transform method best differentiated the forested area with an overall accuracy (OA) of 94.316%, while Landsat had an OA of 92.626%. The findings of this study indicated that optical-SAR-fused images improve land cover classification, which results in higher quality forest inventory data and mapping. 相似文献
7.
遥感和GIS技术应用于伊拉克南部Basrah省土地利用/覆盖变化及城市扩张研究(英文) 总被引：2，自引：0，他引：2
In recent years, land use/cover dynamic change has become a key subject that needs to be dealt with in the study of global
environmental change. In this paper, remote sensing and geographic information systems (GIS) are integrated to monitor, map,
and quantify the land use/cover change in the southern part of Iraq (Basrah Province was taken as a case) by using a 1:250
000 mapping scale. Remote sensing and GIS software were used to classify Landsat TM in 1990 and Landsat ETM+ in 2003 imagery
into five land use and land cover (LULC) classes: vegetation, sand, urban area, unused land, and water bodies. Supervised
classification and normalized difference build-up index (NDBI) were used respectively to retrieve its urban boundary. An accuracy
assessment was performed on the 2003 LULC map to determine the reliability of the map. Finally, GIS software was used to quantify
and illustrate the various LULC conversions that took place over the 13-year span of time. Results showed that the urban area
had increased by the rate of 1.2% per year, with area expansion from 3 299.1 km2 in 1990 to 3 794.9 km2 in 2003. Large vegetation area in the north and southeast were converted into urban construction land. The land use/cover
changes of Basrah Province were mainly caused by rapid development of the urban economy and population immigration from the
countryside. In addition, the former government policy of “returning farmland to transportation and huge expansion in military
camps” was the major driving force for vegetation land change. The paper concludes that remote sensing and GIS can be used
to create LULC maps. It also notes that the maps generated can be used to delineate the changes that take place over time.
Supported by the Al-Basrah University, Iraq, the Geo-information Science and Technology Program (No. IRT 0438)China). 相似文献
8.
9.
High quality data on plant species occurrence count among the essential data sources for ecological research and conservation purposes. Ecologically valuable small grain mosaics of heterogeneous shrub and herbaceous formations however pose a challenging environment for creating such species occurrence maps. Remote sensing can be useful for such purposes, it however faces several challenges, especially the need of ultra high spatial resolution (centimeters) data and distinguishing between plant species or genera. Unmanned aerial vehicles (UAVs) are capable of producing data with sufficient resolution; their use for identification of plant species is however still largely unexplored. A fusion of spectral data with LiDAR-derived vertical information can improve the classification accuracy, such a solution is however costly. A cheaper alternative of vertical data acquisition can be represented by the use of the structure-from-motion photogrammetry (SfM) utilizing the images taken for (multi/hyper)spectral analysis. We investigated the use of such a fusion of UAV-borne multispectral and SfM-derived vertical information acquired from a single sensor for classification of shrubland vegetation at species level and compared its accuracy with that derived from multispectral information only. Multispectral images were acquired using Tetracam Micro-MCA6 camera in the west of Czechia in a shrubland landscape protected within the NATURA 2000 network. Using (i) multispectral imagery only and (ii) multispectral-SfM fusion, we classified the vegetation into six classes representing four woody plant species and two meadow types. Our results prove that the multispectral-SfM fusion performs significantly better than multispectral only (88.2% overall accuracy, 85.2% mean producer’s accuracy and 85.7% mean user’s accuracy for fusion instead of 73.3%, 75.1% and 63.7%, respectively, for multispectral). We concluded that the fusion of multispectral and SfM information acquired from a single UAV sensor is a viable method for shrub species mapping. 相似文献
10.
《International Journal of Digital Earth》2013,6(3):194-216
Information on Earth's land surface cover is commonly obtained through digital image analysis of data acquired from remote sensing sensors. In this study, we evaluated the use of diverse classification techniques in discriminating land use/cover types in a typical Mediterranean setting using Hyperion imagery. For this purpose, the spectral angle mapper (SAM), the object-based and the non-linear spectral unmixing based on artificial neural networks (ANNs) techniques were applied. A further objective had been to investigate the effect of two approaches for training sites selection in the SAM classification, namely of the pixel purity index (PPI) and of the direct selection of training points from the Hyperion imagery assisted by a QuickBird imagery and field-based training sites. Object-based classification outperformed the other techniques with an overall accuracy of 83%. Sub-pixel classification based on the ANN showed an overall accuracy of 52%, very close to that of SAM (48%). SAM applied using the training sites selected directly from the Hyperion imagery supported by the QuickBird image and the field visits returned an increase accuracy by 16%. Yet, all techniques appeared to suffer from the relatively low spatial resolution of the Hyperion imagery, which affected the spectral separation among the land use/cover classes. 相似文献
11.
Johan Beekhuizen Keith C. Clarke 《International Journal of Applied Earth Observation and Geoinformation》2010
The classification of satellite imagery into land use/cover maps is a major challenge in the field of remote sensing. This research aimed at improving the classification accuracy while also revealing uncertain areas by employing a geocomputational approach. We computed numerous land use maps by considering both image texture and band ratio information in the classification procedure. For each land use class, those classifications with the highest class-accuracy were selected and combined into class-probability maps. By selecting the land use class with highest probability for each pixel, we created a hard classification. We stored the corresponding class probabilities in a separate map, indicating the spatial uncertainty in the hard classification. By combining the uncertainty map and the hard classification we created a probability-based land use map, containing spatial estimates of the uncertainty. The technique was tested for both ASTER and Landsat 5 satellite imagery of Gorizia, Italy, and resulted in a 34% and 31% increase, respectively, in the kappa coefficient of classification accuracy. We believe that geocomputational classification methods can be used generally to improve land use and land cover classification from imagery, and to help incorporate classification uncertainty into the resultant map themes. 相似文献
12.
Informal settlements are a common feature of developing countries. In South Africa the improvement of living conditions in these settlements and the upgrading to formal housing types are regarded as being of central importance to the nation's development. Effective settlement improvement and upgrading activities, which we here term "management", require adequate spatial data. To date, the acquisition of spatial models of informal settlements has been based on conventional mapping techniques, and mostly on photogrammetry. Data are compiled using analogue or analytical methods. These are manual and hence require both considerable expertise and expensive equipment. Moreover, these methods are uneconomical over the often relatively small, densely populated areas covered by informal settlements and are also too expensive to employ with a regularity required to support such tasks as change detection. Alternative imaging sources and mapping techniques are therefore needed.
In this article we examine the problem of spatial information acquisition for informal settlement management from three perspectives: spatial information requirements, the role which imagery can play in satisfying these spatial information requirements, and effective imaging options. We focus on the potential of high resolution satellite imaging, small format digital aerial imagery and digital multispectral video systems for rapid mapping. We also discuss the example of automated shack extraction from aerial imagery. 相似文献
In this article we examine the problem of spatial information acquisition for informal settlement management from three perspectives: spatial information requirements, the role which imagery can play in satisfying these spatial information requirements, and effective imaging options. We focus on the potential of high resolution satellite imaging, small format digital aerial imagery and digital multispectral video systems for rapid mapping. We also discuss the example of automated shack extraction from aerial imagery. 相似文献
13.
S. K. Pathan P. Jothimahi D. Sampat Kumar S. P. Pendharkar 《Journal of the Indian Society of Remote Sensing》1989,17(3):11-22
Bombay Metropolitan Region covering an area of about 4,360 sq. km. was selected for urban land use studies and for urban land use zoning. Urban land use mapping was carried out using SPOT multispectral linear array imagery on 1∶25,000 scale employing visual analysis tehcniques. Fifteen maps were prepared depicting the spatial distribution of various urban classes in the Greater Bombay and New Bombay regions. Sixteen urban land use maps were also prepared using Landsat TM data showing the distribution of land use pattern on 1∶50,000 scale for the entire metropolitan region. Urban land use zoning was carried out based upon suitability index on 1∶250,000 scale. This map provides information on the areas to be used for construction and areas to be kept under green belt in the metropolitan region. This study is a joint venture of Space Applications Centre with Bombay Metropolitan Development authority. 相似文献
14.
KennedyOkelloWERE 《遥感学报》2010,14(1):180-196
<正>Land cover is a fundamental variable that links many facets of the natural environment and a key driver of global environmental change.Alterations in its status can have significant ramifications at local,regional and global levels.Hence,it is imperative to map land cover at a range of spatial and temporal scales with a view to understanding the inherent patterns for effective characterization,prediction and management of the potential environmental impacts.This paper presents the results of an effort to map land cover patterns in Kinangop division,Kenya,using geospatial tools.This is a geographic locality that has experienced rapid land use transformations since Kenya's independence culminating in uncontrolled land cover changes and loss of biodiversity.The changes in land use/cover constrain the natural resource base and presuppose availability of quantitative and spatially explicit land cover data for understanding the inherent patterns and facilitating specific and multi-purpose land use planning and management.As such,the study had two objectives viz.(i) mapping the spatial patterns of land cover in Kinangop using remote sensing and GIS and;(ii) evaluating the quality of the resultant land cover map.ASTER satellite imagery acquired in January 23,2007 was procured and field data gathered between September l0 and October 16,2007.The latter were used for training the maximum likelihood classifier and validating the resultant land cover map.The land cover classification yielded 5 classes,overall accuracy of 83.5%and kappa statistic of 0.79,which conforms to the acceptable standards of land cover mapping. This qualifies its application in environmental decision-making and manifests the utility of geospatial techniques in mapping land resources. 相似文献
15.
In this paper, we present a two-stage method for mapping habitats using Earth observation (EO) data in three Alpine sites in South Tyrol, Italy. The first stage of the method was the classification of land cover types using multi-temporal RapidEye images and support vector machines (SVMs). The second stage involved reclassification of the land cover types to habitat types following a rule-based spatial kernel. The highest accuracies in land cover classification were 95.1% overall accuracy, 0.94 kappa coefficient and 4.9% overall disagreement. These accuracies were obtained when the combination of images with topographic parameters and homogeneity texture was used. The habitat classification accuracies were rather moderate due to the broadly defined rules and possible inaccuracies in the reference map. Overall, our proposed methodology could be implemented to map cost-effectively alpine habitats over large areas and could be easily adapted to map other types of habitats. 相似文献
16.
Classifying historical remotely sensed imagery using a tempo-spatial feature evolution (T-SFE) model 总被引：1，自引：0，他引：1
Yichun Xie Zongyao Sha Yongfei Bai 《ISPRS Journal of Photogrammetry and Remote Sensing》2010,65(2):182-190
Large and growing archives of orbital imagery of the earth’s surface collected over the past 40 years provide an important resource for documenting past and current land cover and environmental changes. However uses of these data are limited by the lack of coincident ground information with which either to establish discrete land cover classes or to assess the accuracy of their identification. Herein is proposed an easy-to-use model, the Tempo-Spatial Feature Evolution (T-SFE) model, designed to improve land cover classification using historical remotely sensed data and ground cover maps obtained at later times. This model intersects (1) a map of spectral classes (S-classes) of an initial time derived from the standard unsupervised ISODATA classifier with (2) a reference map of ground cover types (G-types) of a subsequent time to generate (3) a target map of overlaid patches of S-classes and G-types. This model employs the rules of Count Majority Evaluation, and Subtotal Area Evaluation that are formulated on the basis of spatial feature evolution over time to quantify spatial evolutions between the S-classes and G-types on the target map. This model then applies these quantities to assign G-types to S-classes to classify the historical images. The model is illustrated with the classification of grassland vegetation types for a basin in Inner Mongolia using 1985 Landsat TM data and 2004 vegetation map. The classification accuracy was assessed through two tests: a small set of ground sampling data in 1985, and an extracted vegetation map from the national vegetation cover data (NVCD) over the study area in 1988. Our results show that a 1985 image classification was achieved using this method with an overall accuracy of 80.6%. However, the classification accuracy depends on a proper calibration of several parameters used in the model. 相似文献
17.
Synthetic aperture radar (SAR) is an important alternative to optical remote sensing due to its ability to acquire data regardless of weather conditions and day/night cycle. The Phased Array type L-band SAR (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) provided new opportunities for vegetation and land cover mapping. Most previous studies employing PALSAR investigated the use of one or two feature types (e.g. intensity, coherence); however, little effort has been devoted to assessing the simultaneous integration of multiple types of features. In this study, we bridged this gap by evaluating the potential of using numerous metrics expressing four feature types: intensity, polarimetric scattering, interferometric coherence and spatial texture. Our case study was conducted in Central New York State, USA using multitemporal PALSAR imagery from 2010. The land cover classification implemented an ensemble learning algorithm, namely random forest. Accuracies of each classified map produced from different combinations of features were assessed on a pixel-by-pixel basis using validation data obtained from a stratified random sample. Among the different combinations of feature types evaluated, intensity was the most indispensable because intensity was included in all of the highest accuracy scenarios. However, relative to using only intensity metrics, combining all four feature types increased overall accuracy by 7%. Producer’s and user’s accuracies of the four vegetation classes improved considerably for the best performing combination of features when compared to classifications using only a single feature type. 相似文献
18.
常规多时相遥感影像变化检测主要基于光谱信息,没有充分利用纹理、几何、形状等多种特征信息,不足以体现检测目标的完整性和准确性。本文针对不同特征在变化检测中应用的优势,在提取影像多种特征的基础上,构建了1维和多维两种基于信息融合策略的变化检测方法,即利用1维特征空间加权距离相似度运算、多维特征空间的模糊集融合和支持向量机融合策略进行变化检测。利用多时相QuickBird高分辨率遥感影像进行城市土地覆盖变化检测试验,结果表明,本文方法可以有效集成不同特征的优势与表征变化信息的能力,提高变化检测过程的稳定性和适用性,同时能够更好地保持变化地物的结构和形状,突出主要变化目标。 相似文献
19.
近年来,随着遥感技术的不断发展,利用遥感技术开展土地覆盖信息的提取工作已经变得越来越普遍。本文主要利用遥感技术进行土地覆盖信息的提取,为后续土地信息的分析调查提供了有利的数据。此次研究选取了渝西地区作为研究区,使用TM／ETM遥感图像作为基础数据。在提取覆盖信息之前,首先,采用遥感图像处理技术,对研究区进行了图像预处理;接着,对研究区四种地类进行采样处理,利用得到的采样数据,对研究区的遥感图像进行了光谱分析;最后,进行监督分类得到覆盖信息的明显特征,可以看出建筑用地在明显增多。并对分类结果进行精度评价,得到最后结论,可以看出每一时期的总分类精度都在85％以上,符合分类要求。 相似文献
20.
3S技术在城市建设用地执法检查中的应用研究 总被引：1，自引：0，他引：1
选用2004年、2005年和2006年三个时相的QuickBird卫星遥感图像作为信息源,用GIS作为遥感数据处理工具来识别违法地块,利用GPS进行地块实际测量来确定违法用地的位置和面积,以郑州市城市建设用地执法检查为例,探讨3S技术在建设用地执法检查中的应用。 相似文献