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Mohamed Barakat A. Gibril Suzana A. Bakar Kouame Yao Mohammed Oludare Idrees 《国际地球制图》2017,32(7):735-748
In this study, we investigated the performance of different fusion and classification techniques for land cover mapping in Hilir Perak, Peninsula Malaysia using RADAR and Landsat-8 images in a predominantly agricultural area. The fusion methods used are Brovey Transform, Wavelet Transform, Ehlers and Layer Stacking and their results classified into seven different land cover classes which include (1) pixel-based classifiers (spectral angle mapper (SAM), maximum likelihood (ML), support vector machine (SVM)) and (2) Object-based (rule-based and standard nearest neighbour (NN)) classifiers. The result shows that pixel-based classification achieved maximum accuracy of the optical data classification using SVM in Landsat-8 with 74.96% accuracy compared to SAM and ML. For multisource data classification, the highest overall accuracy recorded for layer stacking (SVM) was 79.78%, Ehlers fusion (SVM) with 45.57%, Brovey fusion (SVM) with 63.70% and Wavelet fusion (SVM) 61.16%. And for object-based classifiers, the overall classification accuracy is 95.35% for rule-based and 76.33% for NN classifier, respectively. Based on the analysis of their performances, object-based and the rule-based classifiers produced the best classification accuracy from the fused images. 相似文献
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利用SVM的全极化、双极化与单极化SAR图像分类性能的比较 总被引:1,自引:0,他引:1
支持向量机(SVM)以其在小训练样本时良好的分类性能,目前已广泛应用于多个领域.本文在极化SAR图像特征提取基础上,将SVM应用于极化SAR图像分类,定性和定量地比较了全极化、双极化和单极化SAR图像的分类性能,分析了不同的极化组合对分类结果的影响,并根据地物极化散射特性分析了分类精度差异的成因.实测极化SAR数据的实验结果表明,全极化数据能获得最好的分类性能,双极化次之,单极化最低,且在某些情况下,双极化与全极化分类性能接近. 相似文献
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应用分水岭变换与支持向量机的极化SAR图像分类 总被引:1,自引:0,他引:1
结合分水岭变换与支持向量机的特性,提出一种新的极化SAR图像分类算法。其基本思想是先通过分水岭变换及区域合并处理,将极化SAR图像分割成一系列同质区;再以同质区为基本单元,进行特征提取及样本选择后采用支持向量机分类。实验结果表明,该算法可有效降低相干斑对分类的影响,与传统基于像素的SVM算法相比,其分类精度有显著的提高,且结果也更易于理解。 相似文献
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高光谱影像光谱-空间多特征加权概率融合分类 总被引:3,自引:3,他引:0
提出了一种基于光谱-空间多特征加权概率融合的高光谱影像分类方法。首先,利用最小噪声分离(minimum noise fraction,MNF)方法对高光谱影像进行降维和特征提取,并以得到的MNF特征影像作为光谱特征,联合灰度共生矩阵(gray level co-occurrence matrix,GLCM)提取的纹理特征、基于OFC算子建立的多尺度形态学特征以及采用连续最大角凸锥(sequential maximum angle convex cone,SMACC)提取的端元组分特征,组成3组光谱-空间特征;然后利用支持向量机(support vector machine,SVM)对每一组光谱-空间特征进行分类,得到每组特征的概率输出结果;最后,建立多特征加权概率融合模型,应用该模型将不同特征的概率输出结果进行加权融合,得到最终分类结果。为了验证该方法的有效性,利用ROSIS和 AVIRIS影像进行试验,总体分类精度分别达到97.65%和96.62%。结果表明本文的方法不但较好地克服了传统基于单一特征高光谱影像分类的局限性,而且其分类效果也优于常规矢量叠加(vector stacking,VS)和概率融合的多特征分类方法,有效地改善了高光谱影像的分类结果。 相似文献
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PCA、ICA和Gabor小波决策融合的SAR目标识别 总被引:2,自引:0,他引:2
提出了一种基于主成分分析(PCA)、独立分量分析(ICA)和Gabor小波决策融合的合成孔径雷达SAR(Synthetic Aperture Radar)图像目标识别方法。首先用PCA、ICA和Gabor小波变换分别对SAR目标图像提取特征向量,再用3个支持向量机分类器分别对3种方法提取得到的特征向量分类,通过基于等级的决策融合方法对3个支持向量机分类器的输出进行决策融合,得到最终类别决策结果。采用MSTAR数据库中3个目标进行识别实验,实验结果表明,PCA、ICA和Gabor小波决策融合后得到的识别率高于单独用其中任何一个特征得到的识别率。因此,该方法可提高目标的正确识别率,是一种有效的SAR图像目标识别方法。 相似文献
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Roads and buildings constitute a significant proportion of urban areas. Considerable amount of research has been done on the road and building extraction from remotely sensed imagery. However, a few of them have been concentrating on using only spectral information. This study presents a comparison between three object-based models for urban features’ classification, specifically roads and buildings, from WorldView-2 satellite imagery. The three applied algorithms are support vector machines (SVMs), nearest neighbour (NN) and proposed rule-based system. The results indicated that the proposed rules in this study, despite the spectral complexity of land cover types, performed a satisfactory output with an overall accuracy of 92.92%. The advantages offered by the proposed rules were not provided by other two applied algorithms and it revealed the highest accuracy compared to SVM and NN. The overall accuracy for SVM was 76.76%, which is almost similar to the result achieved by NN (77.3%). 相似文献
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Synthetic Aperture Radar (SAR) data are of high interest for different applications in remote sensing specially land cover classification. SAR imaging is independent of solar illumination and weather conditions. It can even penetrate some of the Earth’s surface materials to return information about subsurface features. However, the response of radar is more a function of geometry and structure than a surface reflection occurs in optical images. In addition, the backscatter of objects in the microwave range depends on the frequency of the band used, and the grey values in SAR images are different from the usual assumption of the spectral reflectance of the Earth’s surface. Consequently, SAR imaging is often used as a complementary technique to traditional optical remote sensing. This study presents different ensemble systems for multisensor fusion of SAR, multispectral and LiDAR data. First, in decision ensemble system, after extraction and selection of proper features from each data, crisp SVM (Support Vector Machine) and Fuzzy KNN (K Nearest Neighbor) are utilized on each feature space. Finally Bayesian Theory is applied to fuse SVMs when Decision Template (DT) and Dempster Shafer (DS) are applied as fuzzy decision fusion methods on KNNs. Second, in feature ensemble system, features from all data are applied on a cube. Then classifications were performed by SVM and FKNN as crisp and fuzzy decision making system respectively. A co-registered TerrraSAR-X, WorldView-2 and LiDAR data set form San Francisco of USA was available to examine the effectiveness of the proposed method. The results show that combinations of SAR data with different sensor improves classification results for most of the classes. 相似文献
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In this paper, the linear discriminative Laplacian eigenmaps (LDLE) dimensionality reduction (DR) algorithm is introduced to C-band polarimetric synthetic aperture radar (PolSAR) agricultural classification. A collection of homogenous areas of the same crop class usually presents physical parameter variation, such as the biomass and soil moisture. Furthermore, the local incidence angle also impacts a lot on the same crop category when the vegetation layer is penetrable with C-band radar. We name this phenomenon as the “observed variation of the same category” (OVSC). The most common PolSAR features, e.g., the Freeman–Durden and Cloude–Pottier decompositions, show an inadequate performance with OVSC. In our research, more than 40 coherent and incoherent PolSAR decomposition models are stacked into the high-dimensionality feature cube to describe the various physical parameters. The LDLE algorithm is then performed on the observed feature cube, with the aim of simultaneously pushing the local samples of the same category closer to each other, as well as maximizing the distance between local samples of different categories in the learnt subspace. Finally, the classification result is obtained by nearest neighbor (NN) or Wishart classification in the reduced feature space. In the simulation experiment, eight crop blocks are picked to generate a test patch from the 1991 Airborne Synthetic Aperture Radar (AIRSAR) C-band fully polarimetric data from of Flevoland test site. Locality preserving projections (LPP) and principal component analysis (PCA) are then utilized to evaluate the DR results of the proposed method. The classification results show that LDLE can distinguish the influence of the physical parameters and achieve a 99% overall accuracy, which is better than LPP (97%), PCA (88%), NN (89%), and Wishart (88%). In the real data experiment, the Chinese Hailaer nationalized farm RadarSat2 PolSAR test set is used, and the classification accuracy is around 94%, which is again better than LPP (90%), PCA (88%), NN (89%), and Wishart (85%). Both experiments suggest that the LDLE algorithm is an effective way of relieving the OVSC phenomenon. 相似文献
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分别利用多通道Gabor滤波器和HSV颜色模型对图像进行特征提取,得到两种特征空间。用顺序向前浮点法搜索,以J-M距离(Jeffreys-Matusitas distance)为评价指标进行特征选择,最后利用综合后的特征数据在SVM基础上实现图像的监督分类。上述方法提高了彩色纹理图像和遥感图像的分类正确率。实验表明,多特征融合的分类效果比单一特征要好。 相似文献
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Single, interferometric dual, and quad-polarization mode data were evaluated for the characterization and classification of seven land use classes in an area with shifting cultivation practices located in the Eastern Amazon (Brazil). The Advanced Land-Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data were acquired during a six month interval. A clear-sky Landsat-5/TM image acquired at the same period was used as additional ground reference and as ancillary input data in the classification scheme. We evaluated backscattering intensity, polarimetric features, interferometric coherence and texture parameters for classification purposes using support vector machines (SVM) and feature selection. Results showed that the forest classes were characterized by low temporal backscattering intensity variability, low coherence and high entropy. Quad polarization mode performed better than dual and single polarizations but overall accuracies remain low and were affected by precipitation events on the date and prior SAR date acquisition. Misclassifications were reduced by integrating Landsat data and an overall accuracy of 85% was attained. The integration of Landsat to both quad and dual polarization modes showed similarity at the 5% significance level. SVM was not affected by SAR dimensionality and feature selection technique reveals that co-polarized channels as well as SAR derived parameters such as Alpha-Entropy decomposition were important ranked features after Landsat’ near-infrared and green bands. We show that in absence of Landsat data, polarimetric features extracted from quad-polarization L-band increase classification accuracies when compared to single and dual polarization alone. We argue that the joint analysis of SAR and their derived parameters with optical data performs even better and thus encourage the further development of joint techniques under the Reducing Emissions from Deforestation and Degradation (REDD) mechanism. 相似文献
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极化合成孔径雷达观测系统获取的影像需要经过极化定标处理才能进行定量的分析与应用。当前的极化定标方案普遍采用分布式地物解算串扰和交叉极化通道不平衡误差,需要在定标前选取满足一定散射特征的分布式地物作为定标参考样本。利用螺旋散射的极化特征与分布特点,提出了一种新的极化定标参考地物自动提取方法。该方法根据极化目标分解构建螺旋散射比率特征,并采用自适应阈值分割方法自动提取地物样本。采用C波段机载极化雷达影像进行实验,结果表明,所提方法能够保持影像极化定标的准确性,并能提高极化定标的精度。 相似文献
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极化SAR参数优化与光学波谱相结合的面向对象土地覆盖分类 总被引:1,自引:1,他引:0
提出一种基于极化参数优化的面向对象分类方法。该方法结合光学和SAR数据,有效提高了对地物的识别能力。本文方法的关键在于:在■分解中,使用光学影像指导SAR影像选择同质点,使其更精确地估计极化参数并结合光学波谱信息作为输入特征;使用面向对象的分类方法,仅将光学影像作为分割输入,避免SAR噪声引起的分割错误。以美国Bakersfield地区的Sentinel-1/2数据为例,确定7种地物类型,对比分析不同输入与不同分类器对分类结果的影响。研究表明,优化输入参数在纹理丰富区域能够有效提高分类精度;面向对象的分类结果更加稳定并较好地维持地表几何特征;改进分类方法较传统分类方法总体精度提高了近10%,达到92.6%。 相似文献
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综合多特征的极化SAR图像随机森林分类算法 总被引:2,自引:1,他引:1
为抑制相干斑噪声对极化SAR图像分类结果的干扰,本文提出一种综合多特征的极化SAR图像随机森林分类方法。该方法首先利用简单线性迭代聚类(SLIC)算法生成超像素作为分类单元;然后,基于高维极化特征图像,利用训练好的随机森林模型,统计决策树的分类投票数,计算各超像素的类别概率;最后,利用超像素间的空间邻域特征,采用概率松弛算法(PLR)迭代修正超像素的类别后验概率,并依据最大后验概率(MAP)准则得到分类结果;实现综合利用超像素和空间邻域特征,降低相干斑噪声干扰的极化SAR图像分类方法。实验对比结果表明:本文方法能得有效抑制极化SAR图像中相干斑噪声的干扰,得到高精度且光滑连续的分类结果。 相似文献
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Evaluation of Morphological Texture Features for Mangrove Forest Mapping and Species Discrimination Using Multispectral IKONOS Imagery 总被引:2,自引:0,他引:2
《Geoscience and Remote Sensing Letters, IEEE》2009,6(3):393-397