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1.
Many data fusion methods are available, but it is poorly understood which fusion method is suitable for integrating Landsat Thematic Mapper (TM) and radar data for land cover classification. This research explores the integration of Landsat TM and radar images (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) for land cover classification in a moist tropical region of the Brazilian Amazon. Different data fusion methods—principal component analysis (PCA), wavelet-merging technique (Wavelet), high-pass filter resolution-merging (HPF), and normalized multiplication (NMM)—were explored. Land cover classification was conducted with maximum likelihood classification based on different scenarios. This research indicates that individual radar data yield much poorer land cover classifications than TM data, and PALSAR L-band data perform relatively better than RADARSAT-2 C-band data. Compared to the TM data, the Wavelet multisensor fusion improved overall classification by 3.3%-5.7%, HPF performed similarly, but PCA and NMM reduced overall classification accuracy by 5.1%-6.1% and 7.6%-12.7%, respectively. Different polarization options, such as HH and HV, work similarly when used in data fusion. This research underscores the importance of selecting a suitable data fusion method that can preserve spectral fidelity while improving spatial resolution.  相似文献   

2.
This research aimed to explore the fusion of multispectral optical SPOT data with microwave L-band ALOS PALSAR and C-band RADARSAT-1 data for a detailed land use/cover mapping to find out the individual contributions of different wavelengths. Many fusion approaches have been implemented and analyzed for various applications using different remote sensing images. However, the fusion methods have conflict in the context of land use/cover (LULC) mapping using optical and synthetic aperture radar (SAR) images together. In this research two SAR images ALOS PALSAR and RADARSAT-1 were fused with SPOT data. Although, both SAR data were gathered in same polarization, and had same ground resolution, they differ in wavelengths. As different data fusion methods, intensity hue saturation (IHS), principal component analysis, discrete wavelet transformation, high pass frequency (HPF), and Ehlers, were performed and compared. For the quality analyses, visual interpretation was applied as a qualitative analysis, and spectral quality metrics of the fused images, such as correlation coefficient (CC) and universal image quality index (UIQI) were applied as a quantitative analysis. Furthermore, multispectral SPOT image and SAR fused images were classified with Maximum Likelihood Classification (MLC) method for the evaluation of their efficiencies. Ehlers gave the best score in the quality analysis and for the accuracy of LULC on LULC mapping of PALSAR and RADARSAT images. The results showed that the HPF method is in the second place with an increased thematic mapping accuracy. IHS had the worse results in all analyses. Overall, it is indicated that Ehlers method is a powerful technique to improve the LULC classification.  相似文献   

3.
The successful launch of Landsat 8 provides a new data source for monitoring land cover, which has the potential to significantly improve the characterization of the earth’s surface. To assess data performance, Landsat 8 Operational Land Imager (OLI) data were first compared with Landsat 7 ETM + data using texture features as the indicators. Furthermore, the OLI data were investigated for land cover classification using the maximum likelihood and support vector machine classifiers in Beijing. The results indicated that (1) the OLI data quality was slightly better than the ETM + data quality in the visible bands, especially the near-infrared band of OLI the data, which had a clear improvement; clear improvement was not founded in the shortwave-infrared bands. Moreover, (2) OLI data had a satisfactory performance in terms of land cover classification. In summary, OLI data were a reliable data source for monitoring land cover and provided the continuity in the Landsat earth observation.  相似文献   

4.
Accelerated soil erosion, high sediment yields, floods and debris flow are serious problems in many areas of Iran, and in particular in the Golestan dam watershed, which is the area that was investigated in this study. Accurate land use and land cover (LULC) maps can be effective tools to help soil erosion control efforts. The principal objective of this research was to propose a new protocol for LULC classification for large areas based on readily available ancillary information and analysis of three single date Landsat ETM+ images, and to demonstrate that successful mapping depends on more than just analysis of reflectance values. In this research, it was found that incorporating climatic and topographic conditions helped delineate what was otherwise overlapping information. This study determined that a late summer Landsat ETM+ image yields the best results with an overall accuracy of 95%, while a spring image yields the poorest accuracy (82%). A summer image yields an intermediate accuracy of 92%. In future studies where funding is limited to obtaining one image, late summer images would be most suitable for LULC mapping. The analysis as presented in this paper could also be done with satellite images taken at different times of the season. It may be, particularly for other climatic zones, that there is a better time of season for image acquisition that would present more information.  相似文献   

5.
Mapping the surficial extent of oolitic iron ore deposits hosted in the Oligo–Miocene sedimentary rocks of the Ashumaysi Formation, western Saudi Arabia, was carried out using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. Ore samples were collected from four various locations in the study area, and were studied in the laboratory using the GER 3700 Spectroradiometer (0.4–2.5 µm) and X-ray diffraction (XRD). Principal component analysis (PCA), minimum noise fraction (MNF), and minimum distance classification were used and assessed to map mineralization zones in the study area. Good correspondences were observed between the results obtained from the above mentioned techniques, spectral reflectance analyses, and XRD. The confusion matrix results revealed that mapping of iron ores using MNF is better and more accurate than using PCA. Good matching was also observed between the spectral reflectance curves of the collected samples and the corresponding pixels from Landsat 7 ETM+. The results demonstrated the usefulness of the image processing and interpretation of Landsat 7 ETM+ data for the detection and delineation iron ore deposits in arid and semi-arid areas.  相似文献   

6.
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.
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   

8.
This study assesses the usefulness of Nigeriasat-1 satellite data for urban land cover analysis by comparing it with Landsat and SPOT data. The data-sets for Abuja were classified with pixel- and object-based methods. While the pixel-based method was classified with the spectral properties of the images, the object-based approach included an extra layer of land use cadastre data. The classification accuracy results for OBIA show that Landsat 7 ETM, Nigeriasat-1 SLIM and SPOT 5 HRG had overall accuracies of 92, 89 and 96%, respectively, while the classification accuracy for pixel-based classification were 88% for Landsat 7 ETM, 63% for Nigeriasat-1 SLIM and 89% for SPOT 5 HRG. The results indicate that given the right classification tools, the analysis of Nigeriasat-1 data can be compared with Landsat and SPOT data which are widely used for urban land use and land cover analysis.  相似文献   

9.
Forest cover plays a key role in climate change by influencing the carbon stocks, the hydrological cycle and the energy balance. Forest cover information can be determined from fine-resolution data, such as Landsat Enhanced Thematic Mapper Plus (ETM+). However, forest cover classification with fine-resolution data usually uses only one temporal data because successive data acquirement is difficult. It may achieve mis-classification result without involving vegetation growth information, because different vegetation types may have the similar spectral features in the fine-resolution data. To overcome these issues, a forest cover classification method using Landsat ETM+ data appending with time series Moderate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data was proposed. The objective was to investigate the potential of temporal features extracted from coarse-resolution time series vegetation index data on improving the forest cover classification accuracy using fine-resolution remote sensing data. This method firstly fused Landsat ETM+ NDVI and MODIS NDVI data to obtain time series fine-resolution NDVI data, and then the temporal features were extracted from the fused NDVI data. Finally, temporal features combined with Landsat ETM+ spectral data was used to improve forest cover classification accuracy using supervised classifier. The study in North China region confirmed that time series NDVI features had significant effects on improving forest cover classification accuracy of fine resolution remote sensing data. The NDVI features extracted from time series fused NDVI data could improve the overall classification accuracy approximately 5% from 88.99% to 93.88% compared to only using single Landsat ETM+ data.  相似文献   

10.
The study investigates the performance of image classifiers for landscape-scale land cover mapping and the relevance of ancillary data for the classification success in order to assess and to quantify the importance of these components in image classification. Specifically tested are the performance of maximum likelihood classification (MLC), artificial neural networks (ANN) and discriminant analysis (DA) based on Landsat7 ETM+ spectral data in combination with topographic measures and NDVI. ANN produced high accuracies of more than 75% also with limited input information, while MLC and DA produced comparable results only by incorporating ancillary data into the classification process. The superiority of ANN classification was less pronounced on the level of the single land cover classes. The use of ancillary data generally increased classification accuracy and showed a similar potential for increasing classification accuracy than the selection of the classifier. Therefore, a stronger focus on the development of appropriate and optimised sets of input variables is suggested. Also the definition and selection of land cover classes has shown to be crucial and not to be simply adaptable from existing land cover class schemes. A stronger research focus towards discriminating land cover classes by their typical spectral, topographic or seasonal properties is therefore suggested to advance image classification.  相似文献   

11.
姜亢  胡昌苗  于凯  赵永超 《遥感学报》2014,18(2):287-306
地形校正可以减小地形起伏对地物光谱的影响,提高计算机分类在山区的精度。设计了针对全球土地覆盖分类的Landsat TM/ETM+数据地形校正方法 SCOS(Smoothed COS余弦),首先对地形的坡度角进行抹平处理,很大程度上削弱了地表非朗伯性对地形校正的影响,然后利用简单有效的余弦校正去除地形效应。该方法与其他常用地形校正算法的对比分析是通过对全球不同区域、不同地表覆盖的有代表性的6景Landsat TM/ETM+数据的试验,采用统计分析与目视判读的方式,从过度校正和类内均一性两个方面进行的。结果表明,该方法在目视效果和统计结果上优于常规方法,并且更加简单有效,无需复杂的大气参数及传感器参数,满足全球地表覆盖分类对地形校正的需求。  相似文献   

12.
The main aim of this research is to highlight the environment change indicators during the last 20 years in a representa-tive area of the southern part of Iraq(Basrah Province was taken as a case) to understand the main causes which led to widespread environment degradation phenomena using a 1:250000 mapping scale.Remote sensing and GIS’s 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 land,sand land,urban area,unused land,and water bodies.Supervised classification and Normalized Difference Vegetation Index(NDVI),Normalized Difference Build-up Index(NDBI),Normalized Difference Water Index(NDWI),Normalized Difference Salinity In-dex(NDSI),and Topsoil Grain Size Index(GSI) were adopted in this research and used respectively to retrieve its class boundary.The results showed a clear deterioration in vegetative cover(514.9 km2) and an increase of sand dune accumulations(438.6 km2),accounting for 10.1,and 10.6 percent,respectively,of the total study area.In addition,a decrease in the water bodies’ area was de-tected(228.9 km2).Sand area accumulations had increased in the total study area,with an annual increasing expansion rate of(33.7 km2·yr·1) during the thirteen years covered by the study.It is therefore imperative that Iraqi government undertake a series of pru-dent actions now that will enable to be in the best possible position when the current environmental crisis ultimately passes.  相似文献   

13.
吴孟哲  陈锟山 《遥感学报》2006,10(4):578-585
本论文尝试讨论两个主题：主题一为利用主成分分析PCA方法应用于像元阶层资料融合技术的研究。主题二为应用Dempster-Shafer evidence theory方法于特征阶层数据融合技术的研究。在第一个主题中，由于合成孔径雷达的数据具有全偏极特性，在此选取了对植被较为敏感的HV极化合成孔径雷达数据，与具有光谱特性的光学SPOT数据做数据融合处理以利接下来的地物分类。首先，本研究利用小波转换技术来滤除合成孔径雷达斑驳噪声，在接下来融合步骤中，主成分分析出来的第一部分（PCI）是用做完滤除噪声后的合成孔径雷达取代，在数据融合后，进行地物分类是采用最大似然法来分类融合影像。在第二个主题中，利用全偏极雷达数据的极化特性结合SPOT数据的光谱特性，其主要目的是为了增加分类的精确度。首先使用李式滤波器滤除全偏极雷达数据噪声，接下来同样是使用采用最大似然法来分类融合影像，（不同的在于全偏极雷达影像使用Wishart几率分布，在光学影像采用multivariate Gaussian几率分布）将每个类别中每个像元属于某个类别的几率值计算出来，再利用Dempster-Shafer evidence theory来结合这些类别的机率值。最后产生出一张新的分类影像。实验的结果显示分类的精确度比较于未融合的资料都有明显提升的效果，也证明了此两个数据融合方法对于不同数据特性的融合都是很成功的。  相似文献   

14.
遥感与GIS技术在区域农业地质调查中的应用   总被引：3，自引：2，他引：3  
较为详细地介绍了遥感与GIS技术在区域生态环境与农业地质研究中的应用。WVS分类（Water, Vegetation and Soil Classification）及地面覆被分类图的编制，为区域性生态环境研究提供了重要的基础资料。在此基础上，结合归一化变换、相关分析与熵的引入，为区域性生态地球化学环境评价提供了定量分析数据。  相似文献   

15.
In this study an attempt is made for studying the Himalayan glacier features using TerraSAR-X and Indian Remote Sensing Satellite, Linear Imaging Self Scanning System III (IRS LISS –III) images. New generation, synthetic aperture radar (SAR) data from TerraSAR-X (TS-X) sensor provide opportunity for glacier feature studies in Himalayan rugged terrain. Spot Light High resolution mode TS-X data give idea about glacial features which remained untraceable from other existing SAR system. However, presence of speckle noise in SAR images degrades the interpretability of the glacier features. Speckle suppression filters (Lee, Frost, Enhanced Lee, Gamma-Map) are applied on SAR intensity images. On the basis of field sight seeing and insitu observations it is observed that still features are not clear. Hence attempt has been made for fusing multitemporal multispatial speckle reduced TS-X SAR data and multispectral IRS LISS-III data for extracting the glacial features such as crevasses, exposed ice and superaglacier lakes. Principal component analysis (PCA) represents the high spectral resolution data in a linear subspace with minimum information loss. Herein, PCA based image fusion technique is adopted for this study and comparison is made between IHS fusion technique and PCA based technique for glacier studies in the Himalayan region.  相似文献   

16.
Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information. However, detection of short-term processes and land use patterns of high spatial–temporal variability is a challenging task.We present a novel framework using multi-temporal TerraSAR-X data and machine learning techniques, namely discriminative Markov random fields with spatio-temporal priors, and import vector machines, in order to advance the mapping of land cover characterized by short-term changes. Our study region covers a current deforestation frontier in the Brazilian state Pará with land cover dominated by primary forests, different types of pasture land and secondary vegetation, and land use dominated by short-term processes such as slash-and-burn activities. The data set comprises multi-temporal TerraSAR-X imagery acquired over the course of the 2014 dry season, as well as optical data (RapidEye, Landsat) for reference. Results show that land use land cover is reliably mapped, resulting in spatially adjusted overall accuracies of up to 79% in a five class setting, yet limitations for the differentiation of different pasture types remain.The proposed method is applicable on multi-temporal data sets, and constitutes a feasible approach to map land use land cover in regions that are affected by high-frequent temporal changes.  相似文献   

17.
以钱塘江流域为研究区域,利用2010年ETM,MODIS和DEM多源数据,进行土地利用分类研究。在分析土地类型的光谱特性和植被指数年度变化基础上,运用光谱指数法和代数法从数据中提取各种土地覆被类型特征。利用WEKA软件平台下的C4.5决策树算法构建决策树分类模型,对钱塘江流域土地覆被类型进行分类研究,取得较高的分类精度。  相似文献   

18.
This paper describes a simple and adaptive methodology for large area forest/non-forest mapping using Landsat ETM+ imagery and CORINE Land Cover 2000. The methodology is based on scene-by-scene analysis and supervised classification. The fully automated processing chain consists of several phases, including image segmentation, clustering, adaptive spectral representativity analysis, training data extraction and nearest-neighbour classification. This method was used to produce a European forest/non-forest map through the processing of 415 Landsat ETM+ scenes. The resulting forest/non-forest map was validated with three independent data sets. The results show that the map’s overall point-level agreement with our validation data generally exceeds 80%, and approaches 90% in central European conditions. Comparison with country-level forest area statistics shows that in most cases the difference between the forest proportion of the derived map and that computed from the published forest area statistics is below 5%.  相似文献   

19.
李大成  唐娉  胡昌苗  郑柯 《遥感学报》2014,18(2):307-319
Landsat 5卫星较低的时间分辨率(16天)使得其很难获得大区域的、时相一致的清晰影像数据集。本文发展了一种基于半物理模型的时空融合算法-即乘性调制融合算法,并借助多时序的MODIS反射率数据来生成多时相的Landsat TM/ETM+反射率合成影像,经镶嵌后得到区域尺度的高时空分辨率地表反射率数据集(Landsat TM/ETM+)。本文利用吉林省2006年—2011年的Landsat 5 TM地表反射率数据以及500 m的MOD09A1反射率产品来生成3个时相的Landsat 5 TM反射率合成数据,从而获得研究区在上述时相下地表反射率数据的镶嵌图。初步分析表明,所生成的Landsat 5 TM反射率数据的光谱分布特征与MOD09A1反射率数据较为一致,且图像在整体上光谱特征的连续性较好。  相似文献   

20.
The classification of tropical fragmented landscapes and moist forested areas is a challenge due to the presence of a continuum of vegetation successional stages, persistent cloud cover and the presence of small patches of different land cover types. To classify one such study area in West Africa we integrated the optical sensors Landsat Thematic Mapper (TM) and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) with the Phased Arrayed L-band SAR (PALSAR) sensor, the latter two on-board the Advanced Land Observation Satellite (ALOS), using traditional Maximum Likelihood (MLC) and Neural Networks (NN) classifiers. The impact of texture variables and the use of SAR to cope with optical data unavailability were also investigated. SAR and optical integrated data produced the best classification overall accuracies using both MLC and NN, respectively equal to 91.1% and 92.7% for TM and 95.6% and 97.5% for AVNIR-2. Texture information derived from optical images was critical, improving results between 10.1% and 13.2%. In our study area, PALSAR alone was able to provide valuable information over the entire area: when the three forest classes were aggregated, it achieved 75.7% (with MCL) and 78.1% (with NN) overall classification accuracies. The selected classification and processing methods resulted in fine and accurate vegetation mapping in a previously untested region, exploiting all available sensors synergies and highlighting the advantages of each dataset.  相似文献   

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