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1.
SVM and SAM classifiers for the lithological mapping using Hyperion data in parts of Gadag schist belt of western Dharwar craton, Karnataka, India were used. The main objective of the present study is to assess and compare the potential use of Hyperion data set for lithological mapping. Accuracy assessment of the derived thematic maps was based on the analysis of the confusion matrix statistics computed for each classification map. For consistency, the same set of validation points were used in evaluating the accuracy of the lithological thematic maps produced. On the basis of the accuracy assessment results, it appears that SVM generally outperformed the SAM classifier in both OA accuracy and individual classes’ accuracies. OA accuracy and Kc for SVM is 96.93% and 0.9655, whereas for SAM it is 74.02% and 0.7085 respectively. SVM classification is the best in describing the spatial distribution and the cover density of each lithology, as was also indicated from the statistics of the individual class results. The individual class accuracy were also analyzed for the SVM and the result show that PA ranges from 87% to 100% and UA ranges from 91% to 100%, whereas for SAM ranges from 15% to 95%, and from 31% to 100% respectively. The SVM method could effectively classify and improve on the existing geological map for the Gadag schist belt (GSB) using hyperspectral data. The results could be validated through field visits. Therefore, it is concluded that hyperspectral remote sensing data can be efficiently used to improve existing maps, especially in areas where same rock types show variable degree of alteration over smaller spatial scales.  相似文献   

2.
Classification of different land features with similar spectral response is an enigmatical task for pixel-based classifiers, as most of these algorithms rely only on the spectral information of the satellite data. This study evaluated the performance of six major pixel-based land-use classification techniques (both common and advanced) for accurate classification of the heterogeneous land-use pattern of Jharia coalfield, India. WorldView-2 satellite data was used in the present study. The land-use classification results revealed that Maximum Likelihood classifier algorithm performed best out of the four common algorithms with an overall accuracy of about 84%. The advanced classifiers used in the study were Neural-Net and Support Vector Machine both of which gave excellent results with an overall accuracy of 91% and 95%, respectively. It was observed that use of very high-resolution data is not sufficient for obtaining high classification accuracy, selection of an appropriate classification algorithm is equally important to get better classification results. Advanced classifiers gave higher accuracy with minimal errors, hence, for critical planning and monitoring tasks these classifiers should be preferred.  相似文献   

3.
The performances of regular support vector machines and random forests are experimentally compared for hyperspectral imaging land cover classification. Special characteristics of hyperspectral imaging dataset present diverse processing problems to be resolved under robust mathematical formalisms such as image classification. As a result, pixel purity index algorithm is used to obtain endmember spectral responses from Indiana pine hyperspectral image dataset. The generalized reduced gradient optimization algorithm is thereafter executed on the research data to estimate fractional abundances in the hyperspectral image and thereby obtain the numeric values for land cover classification. The Waikato environment for knowledge analysis (WEKA) data mining framework is selected as a tool to carry out the classification process by using support vector machines and random forests classifiers. Results show that performance of support vector machines is comparable to that of random forests. This study makes a positive contribution to the problem of land cover classification by exploring generalized reduced gradient method, support vector machines, and random forests to improve producer accuracy and overall classification accuracy. The performance comparison of these classifiers is valuable for a decision maker to consider tradeoffs in method accuracy versus method complexity.  相似文献   

4.
Synthetic aperture radar (SAR) has operational applications in crop mapping and monitoring in many countries due to the distinct backscatter signature at various stages of crop growth. Polarimetric analysis of SAR data from different satellites was used for information extraction from different types of scatters in imaged terrain. The scattering processes were analyzed through the received scatter matrix derived from the target decomposition of SAR data. Three decomposition techniques, namely Freeman–Durden, Cloude–Pottier and Touzi decomposition of the ALOS PALSAR-1 data, were used in this study to extract land use/cover information with a specific emphasis on agriculture. The decomposed output parameters from these techniques were classified with supervised classifier of support vector machine (SVM) using region of interest (ROI) selected land use/cover classes. An accuracy assessment for the classified output was carried out using the ROI. The Ramgarh village in Jaisalmer district of Rajasthan with the predominance of agricultural land, sand dunes and settlements was chosen as the study area. Freeman–Durden decomposition resulted in the highest overall accuracy of about 85% in the land use/cover classification among the three decomposition techniques adopted in the study. It was also observed that the accuracy of land use/cover mapping derived from Cloude–Pottier and Touzi decompositions improved with the use of eigenvalues in the SVM classification. Higher accuracies in the classification of agriculture land were noted with all the three decomposition techniques. The four parameters of Cloude–Pottier (H, A, α, β) and Touzi (α s, Φ s, ψ, τ) decompositions improved the classification accuracy for all the classes due to eigenvalues. The overall classification accuracy was above 88% for both the decomposition techniques with four parameters. The soil moisture values for agriculture land and sand dunes were validated through soil moisture maps generated using Oh 1992 and 2004 models.  相似文献   

5.
Spectral unmixing is a key technology of optical remote sensing image analysis; it not only influences the accuracy of the extraction of land cover information and automatic classification of topographical objects, but also greatly hinders the development of quantitative remote sensing. Independent component analysis (ICA) is a statistical method which is recently developed to extract the independent linear components, and which can realize the extraction of endmembers as well as fractional abundances with little a priori knowledge. However, ICA still cannot process the correlations among the various components. To overcome this problem, variational Bayesian independent component analysis (VBICA) has been proposed to process optical remote sensing images. In the Bayesian framework, the separation of independent components of remote sensing image has finally been achieved with conditional independence standards of Bayesian network and approximate variational algorithm. In the simulative image and real AVIRIS hyperspectral remote sensing image, the VBICA algorithm demonstrates its better performance. The experiment’s results indicate that the proposed VBICA algorithm is feasible, which has obvious advantages and a good application prospect. The reason is that it can effectively overcome the correlations between the various components in remote sensing images and break through the limitations of traditional remote sensing images analysis. Last but not least, the VBICA algorithm is applied in the classification of the TM multispectral remote sensing images. Compared to basic maximum likelihood classification, principal component analysis and FastICA algorithms, VBICA improves the classification accuracy of remote sensing images, and contributes to the further extension of the application of ICA in remote sensing image analysis.  相似文献   

6.
Listwaenites are highly altered ultramafic rocks that are potentially associated with economic mineralization and research on these is extremely important worldwide. In the present study, the classification of mineralized listwaenites developed along the serpentinite–amphibolite interface of the Semail Ophiolite, its associated lithology and the zones of alteration and mineralization in the Fanjah Saddle of the Central Oman Mountains region of the Sultanate of Oman are carried out, using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data. The developed band ratioing, Principal Component Analysis (PCA) and the Spectral Angle Mapper (SAM) supervised classification and image processing techniques applied on the ASTER data set have proved their capability for better interpretation and identification of hydrothermally altered rocks and associated mineralization. The hyperspectral tools (Minimum Noise Fraction (MNF), Pixel Purity Index (PPI) and nD-visualizer) extracted end member spectra and SAM classification clearly show the occurrence of minerals and their spatial distributions.The promising results are verified and confirmed in the field by identification of alteration and mineralization such as listwaenites, silicification, serpentinization and talc alteration and are validated further through laboratory analysis. The confirmation of the occurrence of base metal mineralization along the serpentinite–amphibolite interface in listwaenites suggests that detailed investigation in this and other arid regions which have similar geological conditions may locate mineral deposits. The hyperspectral tools applied on ASTER satellite data show that these can be used as a powerful tool to explore the listwaenites and the potential associated mineralization in other arid geographical regions worldwide.  相似文献   

7.
探索利用高光谱数据的岩性填图新方法是遥感地质应用领域的重要需求之一。本文运用随机森林方法和EO-1Hyperion高光谱数据,对新疆塔里木西北部柯坪地区的局部区域进行岩性分类,并对相关问题进行分析。分别利用光谱特征以及加入光谱一阶导数特征进行岩性分类,并对不同特征对岩性分类的重要性进行分析,同时与现有的基于光谱角制图方法(SAM)进行比较。结果表明,与SAM方法相比,随机森林方法得到了更高精度的岩性分类结果,是一种有效可行的岩性分类方法。根据特征重要性的排序,蓝绿光波段、短波红外波段以及相应的一阶导数特征对研究区Hyperion数据的沉积岩岩性分类贡献更大。  相似文献   

8.
Human activities in many parts of the world have greatly changed the natural land cover. This study has been conducted on Pichavaram forest, south east coast of India, famous for its unique mangrove bio-diversity. The main objectives of this study were focused on monitoring land cover changes particularly for the mangrove forest in the Pichavaram area using multi-temporal Landsat images captured in the 1991, 2000, and 2009. The land use/land cover (LULC) estimation was done by a unique hybrid classification approach consisting of unsupervised and support vector machine (SVM)-based supervised classification. Once the vegetation and non-vegetation classes were separated, training site-based classification technology i.e., SVM-based supervised classification technique was used. The agricultural area, forest/plantation, degraded mangrove and mangrove forest layers were separated from the vegetation layer. Mud flat, sand/beach, swamp, sea water/sea, aquaculture pond, and fallow land were separated from non-vegetation layer. Water logged areas were delineated from the area initially considered under swamp and sea water-drowned areas. In this study, the object-based post-classification comparison method was employed for detecting changes. In order to evaluate the performance, an accuracy assessment was carried out using the randomly stratified sampling method, assuring distribution in a rational pattern so that a specific number of observations were assigned to each category on the classified image. The Kappa accuracy of SVM classified image was highest (94.53 %) for the 2000 image and about 94.14 and 89.45 % for the 2009 and 1991 images, respectively. The results indicated that the increased anthropogenic activities in Pichavaram have caused an irreversible loss of forest vegetation. These findings can be used both as a strategic planning tool to address the broad-scale mangrove ecosystem conservation projects and also as a tactical guide to help managers in designing effective restoration measures.  相似文献   

9.
Applied in Djebel Meni (Northwestern of Algeria), this research highlights the results obtained from the supervised classification using the Spectral Angle Mapper (SAM) algorithm, through introducing the spectral signatures of illite, kaolinite, and montmorillonite, via Jet Propulsion Laboratory (JPL) spectral library. These results were compared to the ones of the SAM classification, which use spectral signatures obtained by the Sequential Maximum Angle Convex Cone (SMACC) endmembers extraction algorithm. This implies the ability to detect and identify any object present on the Earth’s surface, whether its nature is mineral, vegetal, or human made, from hyperspectral imaging. By extracting the spectral signatures with the SMACC algorithm and matching them to the current signatures of JPL spectral library, comparing spectral signatures with another is not an easy task. Indeed, for a better comparison and a more appropriate interpretation in the use of the SAM classification, the results obtained were very relatively convincing because, regarding very strong similarities. It appears also that the signatures extracted with SMACC occupy the same areas as those of the JPL spectral library. This method of detection and identification of any present object on the Earth’s surface is rather conclusive.  相似文献   

10.
土地利用／土地覆盖变化研究是近年来全球变化研究的焦点之一。全球和区域尺度的土地覆盖特征对全球环境状况的评估、模拟未来全球环境的情景有重要的作用。2000年在Internat ionalJournalofRemoteSensing杂志上出版了题为"GlobalandRegionalLandCoverCharacterizat ion from Remotely Sensed Data"的专辑。在此基础上,介绍、总结了国际上利用遥感影像进行全球和区域等大尺度土地覆盖研究的新进展。分别从数据源与制图的时空尺度、制图方法（数据预处理、分类、精度评估）等方面进行了介绍,并对现今的两个全球土地覆盖数据库进行了比较分析。  相似文献   

11.
Satellite images of various spatial resolutions and different image classification techniques have been utilized for land cover (LC) mapping at local and regional scale studies. Mapping capabilities and achievable accuracies of LC classification in a mountain environment are, however, influenced by the spatial resolution of the utilized images and applied classification techniques. Hence, developing and characterizing regionally optimized methods are essential for the planning and monitoring of natural resources. In this study, the potential of four non-parametric image classification techniques, i.e., k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), and neural network (NN) on the accuracy of LC classification was evaluated in the Hindu Kush mountains ranges of northern Pakistan. Moreover, we have assessed the impact of the spatial resolution of the utilized satellite imagery, i.e., SPOT-5 with 2.5 m and Landsat-8 with 30 m on the accuracy of the derived LC classification. For the classification of LC based on SPOT-5, we have achieved the highest overall classification accuracy (OCA) = 89% with kappa coefficient (KC = 0.86) using SVM followed by k-NN, RF, and NN. However, for LC classification derived from Landsat-8 imagery, we achieved the highest OCA = 71% with KC = 0.59 using RF and SVM followed by k-NN and NN. The higher accuracy derived from SPOT-5 versus Landsat-8 indicated that the results of LC classification based on SPOT-5 are more accurate and reliable than Landsat-8. The findings of the present study will be useful for the classification and mapping task of LC in a mountain environment using SPOT-5 and Landsat-8 at local and regional scale studies.  相似文献   

12.
为了深化遥感监测方法在生态环境调查中的应用,本文以吉林西部为试验区,设计了一种多时相遥感数据分类方案。该方案以物候信息为主,结合地物特征变量(植被、水体和土地信息)构建的多维特征空间数据集用于土地覆被分类。该遥感分类方案提取了9种地表覆被类型,结果表明:地表植被季节变化信息和土地利用信息的引入能明显改善土地覆被的分类精度;与基于原始波段的分类方案相比,多时相遥感数据分类方案的分类精度最好,总体分类精度为95.50%,Kappa系数为95.04%。  相似文献   

13.
Huge vegetal losses caused by unsustainable quarrying practices have limited the role played by vegetation cover in mitigating the global impact of climate change. There is a need for a holistic study that will employ remotely sensed data in GIS domain to determine the extent of the effect of quarrying activities on vegetation cover in the study area. The need for extraordinary details with high accuracy and enhanced imagery has necessitated the use of Geo-Eye-1 satellite imagery with 1-m resolution for 2015. The study was carried out in Ebonyi State where 27 quarry sites were studied in six local government areas where mining activities were operated. The study employed geographical information system, Garmin eTrex GPS receiver and global positioning system. Geographical information was processed using ArcGIS to map the patterns and extent of land use. The findings from the study showed that the visual interpretation of the satellite image confirmed that white patches on the imagery were areas affected by intensified quarrying activities. The very dark green colours were interpreted as forest with no observed human activities. The supervised classification of land use also showed that quarrying activities occupied 0.1% of the study area with a total of 402.855 ha of green cover lost in Ebonyi State due to quarrying operations. Quarrying practices have destroyed arable lands, economic trees and forests in the area. This implies that there is a need for policies to be enforced with strict adherence to sustainable quarrying guidelines and consistent monitoring.  相似文献   

14.
Land surface temperature (LST) plays an important role in local, regional and global climate studies. LST controls the distribution of the budget for radiation heat between the atmosphere and the earth’s surface. Therefore, it is important to evaluate abrupt changes in land use/land cover (LULC). Penang Island, Malaysia has been experiencing a rapid and drastic change in urban expansion over the past two decades due to growth in industrial and residential areas. The aim of this study was to investigate and evaluate the impact of LST with respect to land use changes in Penang Island, Malaysia. Three supervised classification techniques known as maximum likelihood, minimum distance-to-mean and parallelepiped were applied to the images to extract thematic information from the acquired scene by using PCI Geomatica 10.1 image processing software. These remote sensing classification techniques help to examine land-use changes in Penang Island using multi-temporal Landsat data for the period of 1999–2007. Training sites were selected within each scene and seven land cover classes were assigned to each classifier. The relative performance of each technique was evaluated. The accuracy of each classification map was assessed using a reference data set consisting of a large number of samples collected per category. Two Landsat satellite images captured in 1999 and 2007 were chosen to classify the LULC types using the maximum likelihood classification method, determined from visible and near-infrared bands. The study revealed that the maximum likelihood classifier produced superior results and achieved a high degree of accuracy. The LST and normalised difference vegetation index (NDVI) were computed based on changes in LULC. The results showed that the urban (highly built-up) area increased dramatically, and grassland area increased moderately. Inversely, barren land decreased obviously, and forest area decreased moderately. While urban (minimally built-up) area decreased slightly. These changes in LULC caused at significant difference in LST between urban and rural areas. Strong correlation values were observed between LST and NDVI for all LULC classes. The remote sensing technique used in this study was found to be efficient; it reduced the time for the analysis of the urban expansion, and it was found to be a useful tool to evaluate the impact of urbanisation with LST.  相似文献   

15.
高光谱遥感数据具有波段多、数据量大、处理复杂等特点, 基于GPU的高性能计算在遥感领域得到了快速发展, 为高光谱数据的快速处理提供了硬件和技术条件。采用GPU对高光谱遥感数据常用的SAM、PPI等处理算法进行应用实验, 验证基于GPU的高光谱遥感数据快速处理技术。实验采用新疆东天山地区的一景星载Hyperion数据, 利用支持IDL开发语言的GPULib、CUDA运行时API库进行算法效率的验证, 结果表明, 基于GPU的高光谱数据处理效率比常规的多核CPU主机处理效率有较大提升, 具有一定的应用推广价值。   相似文献   

16.
基于多源数据融合方法的中国1 km土地覆盖分类制图   总被引：1，自引：0，他引：1  
精确的全球及区域土地覆盖数据是陆地表层过程研究的重要基础。在集成研究兴起和多种数据并存的背景下,利用多源信息融合技术进行大尺度土地覆盖制图具有重要的现实意义。证据理论清楚地表达了由于不确定和不完全信息所带来的对命题认识的“无知”,能够确定相应的假设在目前的认知与知识状态下的确定、不确定和“无知”程度,是多源数据决策融合的重要方法。基于证据理论,将2000年中国1∶10万土地利用数据、中国植被图集（1∶100万）的植被型分类、中国1∶10万冰川图、中国1∶[KG-*2]100万沼泽湿地图和MODIS 2001年土地覆盖产品（MOD12Q1）进行了融合,最终基于最大信任度原则进行决策,产生了新的、IGBP分类系统的2000年中国土地覆盖数据。新的土地覆盖数据在保持了中国土地利用数据的总体精度的同时,补充了中国植被图中对植被类型及植被季相的信息,更新了中国湿地图,增加了中国冰川图最新信息,使分类系统更加通用。  相似文献   

17.
植被覆盖区卫星高光谱遥感岩性分类   总被引：1，自引：0，他引：1  
植被高覆盖区岩石和土壤在遥感图像上表现为弱信息、小目标,如何利用卫星高光谱遥感提取岩性弱信息是目前遥感地质应用中的最大挑战之一。以黑龙江呼玛地区为例,选择美国EO-1卫星Hyperion高光谱数据。由于植被与下伏岩石-土壤的光谱混合,分别计算研究区含土壤因子和不含土壤因子的植被指数,并对两类不同的植被指数进行主成分分析,以此分离植被和岩石-土壤组分。在含土壤因子植被指数主成分分析的二维组分散点图上,明显区分出背景植被与异常岩石-土壤组分,证实了植被与岩石-土壤组分经主成分分析分离的效果。同时在不添加土壤因子植被指数的分析中,明显区分出植被覆盖信息。通过对实验区典型岩石进行野外光谱测试,然后对光谱进行连续统去除处理,将其作为参考光谱,与分离后的岩石-土壤光谱进行光谱特征拟合（SFF）,从而成功地识别出研究区内不同岩石类型,特别是玄武岩、流纹岩、砂砾岩、安山质凝灰岩、大理岩和石英片岩识别效果较好。根据研究区内不同岩石地层单元内岩石组合特征,通过分离后的组分合成图像,成功地实现了岩性分类。与已知地质图叠加,证实通过卫星高光谱数据提取的不同岩石类型颜色边界与地质图岩性界线吻合较好。结果表明：通过植被与岩石-土壤光谱组分分离,结合高光谱遥感的光谱特征拟合,能够识别不同的岩石类型,实现植被覆盖区岩性分类。  相似文献   

18.
Frequent human activity and rapid urbanization have led to an assortment of environmental issues. Monitoring land-cover change is critical to efficient environmental management and urban planning. The current study had two objectives. The first was to compare pixel-based random forest (RF) and decision tree (DT) classifier methods and a support vector machine (SVM) algorithm both in pixel-based and object-based approaches for classification of land-cover in a heterogeneous landscape for 2010. The second was to examine spatio-temporal land-cover change over the last two decades (1990–2010) using Landsat data. This study found that the object-based SVM classifier is the most accurate with an overall classification accuracy of 93.54% and a kappa value of 0.88. A post-classification change detection algorithm was used to determine the trend of change between land-cover classes. The most significant change from 1990 to 2010 was caused by the expansion of built-up areas. In addition to the net changes, the rate of annual change for each phenomenon was calculated to obtain a better understanding of the process of change. Between 1990 and 2010, an average of 4.53% of lands turned to the built-up annually and there was an annual decrease of about 0.81% in natural land. If the current trend of change continues, regardless of the actions of sustainable development, drastic declines in natural areas will ensue. The results of this study can be a valuable baseline for land-cover managers in the region to better understand the current situation and adopt appropriate strategies for management of land-cover.  相似文献   

19.
冯博  段培新  程旭  卢辉雄  李瑞炜  张恩  汪冰 《现代地质》2022,36(6):1594-1604
为深入研究和探讨高分五号(GF-5)航天高光谱遥感技术在铀矿地质找矿中的应用效果和潜力,基于龙首山成矿带航天高光谱数据,开展高光谱数据处理和蚀变信息提取工作,创新实现了GF-5高光谱波段修复,通过构建标准光谱库和诊断光谱,运用MNF算法、PPI算法,结合SAM光谱角填图技术,完成蚀变矿物端元提取和光谱匹配,实现研究区钠长石、方解石、石英、绿泥石、赤铁矿和高岭土蚀变矿物的提取,综合区域铀矿成矿地质背景,通过开展地面波谱测量和野外调查,在验证蚀变准确度的基础上,剖析航天高光谱蚀变信息和成矿规律,构建了区域找矿定位模型,圈定找矿预测区3处,取得了较好的找矿效果,为国产GF-5高光谱遥感在地质找矿中的应用提供了参考。  相似文献   

20.
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