共查询到18条相似文献，搜索用时 157 毫秒
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粒子群优化算法用于高光谱遥感影像分类的自动波段选择 总被引：1，自引：0，他引：1
针对传统SVM分类方法的缺点,采用粒子群优化(particle swarm optimization,PSO)算法自动选择合适的渡段影像并对SVM核函数参数进行优化,提出一种新的PSO-BSSVM分类模型.经过对高光谱遥感影像的分类试验,并与K_最近邻(K-NN)、径向基神经网络(RBF-NN)和标准的支持向量机(SVM)三种分类方法进行对比实验,证明PSO-BSSVM方法能优选高光谱遥感影像的波段和优化SVM参数,明显提高影像的分类精度. 相似文献
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结合纹理的SVM遥感影像分类研究 总被引：7，自引：0，他引：7
针对传统统计模式识别分类方法分类精度不高,分类时未加入像元灰度的空间分布和结构特征以及分类时样本不足等缺陷,采用一种结合纹理的支持向量机(SVM)遥感图像分类方法。该方法在对Landsat7 ETM遥感影像进行纹理特征提取的基础上,构建了结合纹理的SVM分类模型。以河南省汝阳县为试验区,利用此模型对该区域的土地利用类型进行分类研究,并将分类结果与最大似然法和单源数据(光谱)SVM分类结果进行定性和定量比较分析。研究结果表明:该方法能够有效地解决单数据源分类效果破碎、分类精度不高等问题;对高维输入向量具有较高的推广能力;总精度达到90%,比单源信息的SVM分类法提高了6%,而与最大似然法相比,总精度提高了近9%,取得了良好的效果。 相似文献
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文章提出了一种结合改进的最佳指数法(OIF)和支持向量机(SVM)进行高光谱遥感影像分类新方法.利用本文提出的稳定系数进行波段初选择,根据相关系数选择波段组合生成新影像,并对新影像进行OIF计算,得到OIF值最大的波段组合为最佳波段组合;构建SVM分类器,对最佳波段组合分类;最后将分类结果与其他监督分类方法比较,并在相同核函数下与PCA和SVM结合的方法进行精度比较分析.实验结果表明,本文方法能够有效提取最佳波段组合,在SVM算法下获得较高分类精度. 相似文献
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针对高光谱影像分类问题，提出了一种显著性特征提取方法。首先，利用超像素分割算法将高光谱影像3个相邻波段分割为若干个小区域。然后，基于分割得到的小区域计算反映不同区域的显著性特征。最后，沿着光谱方向采用大小为3、步长为1的滑窗法获得所有波段的显著性特征。进一步将提取的显著性特征与光谱特征进行结合，并将结合后的特征输入到支持向量机中进行分类。利用Pavia大学、Indian Pines和Salinas 3组高光谱影像数据进行分类试验。试验结果表明，与传统的空间特征提取方法和基于卷积神经网络的高光谱影像分类方法相比，提取的显著性特征能够获得更高的高光谱影像分类精度，且结合光谱特征能够进一步提高分类精度。 相似文献
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由于物体表面的空间分布通常是富有规律且局部连续的,在高光谱影像分类中应充分利用其光谱和空间信息。本文在对高光谱影像立方体进行降维处理的基础上,提出了一种联合空域和谱域信息的高光谱影像高效分类方法。首先,分别选用主成分分析(Principal Component Analysis,PCA)和正交投影波段选择(Orthogonal Projection Band Selection,OPBS)两种方法对原始高光谱数据进行预处理,获取降维后的影像数据。然后在其基础上提取扩展形态学特征(Extended Morphology Profiles,EMP)和地物表面纹理特征,组成联合光谱和纹理、形状结构特征。最后,采用支持向量机(Support Vector Machine,SVM)分类器对联合特征进行分类。针对不同真实高光谱数据集的实验结果表明,本文提出的方法运算效率高且具有令人满意的分类性能。 相似文献
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高光谱影像波段众多且相关性强，导致分类存在信息冗余且计算量较大。提出了可分离非负矩阵分解方法来选取高光谱影像的代表性波段子集，在保证分类精度的同时降低计算量。该方法假设高光谱影像的波段集合具有可分离特性，改进传统非负矩阵分解模型，将波段选择转换为可分离非负矩阵分解问题，采用迭代投影方法来依次选取能够非负线性表达其他波段的代表性波段。在此基础上，利用两个公开高光谱数据集对比几种主流方法，采用定量评价和分类精度指标来综合评价所提的波段选择方法的效果。实验结果表明，可分离非负矩阵分解方法的分类精度高于其他几种方法，而且计算效率排名第2，能够选取合适的波段子集以满足高光谱遥感的应用需求。 相似文献
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Behnaz Bigdeli Farhad Samadzadegan Peter Reinartz 《Journal of the Indian Society of Remote Sensing》2013,41(4):763-776
With recent technological advances in remote sensing sensors and systems, very high-dimensional hyperspectral data are available for a better discrimination among different complex land-cover classes. However, the large number of spectral bands, but limited availability of training samples creates the problem of Hughes phenomenon or ‘curse of dimensionality’ in hyperspectral data sets. Moreover, these high numbers of bands are usually highly correlated. Because of these complexities of hyperspectral data, traditional classification strategies have often limited performance in classification of hyperspectral imagery. Referring to the limitation of single classifier in these situations, Multiple Classifier Systems (MCS) may have better performance than single classifier. This paper presents a new method for classification of hyperspectral data based on a band clustering strategy through a multiple Support Vector Machine system. The proposed method uses the band grouping process based on a modified mutual information strategy to split data into few band groups. After the band grouping step, the proposed algorithm aims at benefiting from the capabilities of SVM as classification method. So, the proposed approach applies SVM on each band group that is produced in a previous step. Finally, Naive Bayes (NB) as a classifier fusion method combines decisions of SVM classifiers. Experimental results on two common hyperspectral data sets show that the proposed method improves the classification accuracy in comparison with the standard SVM on entire bands of data and feature selection methods. 相似文献
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高光谱遥感影像的波段光谱特征是各类地物内在物理化学性质的反映,在对不同地物进行分类与识别时具有巨大潜能,但由于其波段多造成的信息冗余,需要对高光谱数据进行有效降维,以提高高光谱影像的分类准确度。本文提出了基于判别局部片排列的流形学习算法（DLA）对Hypersion高光谱数据进行降维,通过对局部样本数据进行流形学习框架内的优化训练,将原始光谱特征空间转换为低维的最优判别流形子空间,然后在该子空间内利用最大似然分类器对Hypersion影像中的每个像素进行分类,并与主成分分析（PCA）、原始光谱特征（spectral）降维方法的分类效果进行比较。结果表明,DLA能够有效提高高光谱数据的分类准确度,对不同树种分类取得了满意效果。 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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This study presents a deep extraction of localized spectral features and multi-scale spatial features convolution (LSMSC) framework for spectral-spatial fusion based classification of hyperspectral images (HSIs). First, adjacent spectral bands are grouped based on their similarity measurements, where the whole hypercube is partitioned into several sub-cubes, each corresponding to one band group. Then, the proposed localized spectral features extraction (LSF) strategy is used to extract localized spectral features, which are extracted from each band group using the 1D convolutional neural network (CNN). Meanwhile, the proposed HiASPP strategy is employed to extract the multi-scale features from the first several principal components of each sub-cube. Finally, the extracted spectral and spatial features are concatenated for spectral-spatial fusion based classification of HSI. Experiments conducted on three publicly available datasets have demonstrated that the proposed architecture outperforms several state-of-the-art approaches. 相似文献
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Parham Pahlavani Mahdi Hasanlou Siamak Talebi Nahr 《Journal of the Indian Society of Remote Sensing》2017,45(1):11-23
Currently, hyperspectral images have potential applications in many scientific areas due to the high spectral resolution. Extracting suitable and adequate bands/features from high dimensional data is a crucial task to classify such data. To overcome this issue, dimension reduction techniques have direct effects to improve the efficiency of classifiers on hyperspectral images. One common approach for decreasing the dimensionality is the feature/band selection by considering the optimum dimensionality of the hyperspectral imagery. In this paper, a new method was proposed to select optimal band for classification application, based on a metaheuristic Invasive Weed Optimization (IWO) algorithm. In this regard, the K-nearest neighbour (K-NN) technique was used as the classifier. Moreover, as a by-product of our band selection method, a new method was proposed to estimate an optimum dimension of the reduced hyperspectral images for better classification. Experimental results over three real-world hyperspectral datasets clearly showed that the proposed IWO-based band selection algorithm of this study led to the significant progress in selecting suitable bands for classification applications and estimation of optimum dimensionality of these datasets. In this regard, the overall accuracy (OA) of classification of the proposed IWO-based band selection algorithm was 92.02, 93.57, and 89.72 % for each dataset, respectively. Moreover, results reveal the superiority of the proposed IWO-based band selection algorithm against the other algorithms including GA, SA, ACO, and PSO for band selection purpose. 相似文献
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In response to the curse of dimensionality in hyperspectral images (HSIs), to date, numerous dimensionality reduction methods have been proposed among which the feature extraction (FE) methods are of particular interest. This paper introduces a new supervised pixel-based FE called spectral segmentation and integration (SSI). In SSI, the spectral signature curve (SSC) of the pixels are identically divided into some non-overlapping segments, called channels. The existing bands in each channel are then integrated using a mean-weighted operator, leading to some new features in a very lower number than the original bands. SSI applies a particle swarm optimization (PSO) algorithm to globally search and locate the optimum positions and widths of the channels. For the sake of evaluation and comparison, the features provided by the proposed SSI method were applied to the well-known SVM classifier. The results were compared to not only a most recent pixel-based FE method, namely, spectral region splitting but also six conventional FE methods, including nonparametric weighted feature extraction, decision boundaries feature extraction, clustering-based feature extraction, semi-supervised local discriminant analysis, band correlation clustering and principal component analysis. Experimental results, obtained on two HSIs, proved the superiority of the proposed SSI. 相似文献
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高光谱影像的引导滤波多尺度特征提取 总被引：1，自引：0，他引：1
为了解决高光谱遥感影像分类中单一尺度特征无法有效表达地物类间差异和区分地物边界的不足,提高影像分类精度和改善分类目视解译效果,提出了采用引导滤波提取多尺度的空间特征的方法。首先,利用主成分分析对高光谱影像进行降维,移除噪声并突出主要特征;然后,将第1主成分作为引导影像,将包含信息量最多的若干主成分分别作为输入影像,应用依次增加的滤波半径分别进行引导滤波处理提取多个尺度的特征,获得影像不同尺度的结构信息;最后,将多尺度特征输入分类器中进行影像监督分类。采用仿真数据和帕维亚大学(Pavia University)、帕维亚城区(Pavia Centre)等3幅高光谱实验数据,提取了基于引导滤波的多尺度特征、多尺度形态特征和多尺度纹理特征,输入到支持向量机、随机森林和K近邻分类器中,进行了实验。实验结果表明:采用支持向量机分类Pavia University数据,相对于采用多尺度形态特征的分类结果,引导滤波特征的总体精度提高了6.5%;Pavia Centre和Salinas两幅影像最高分类精度均由引导滤波特征实现,分别达到98.51%和98.39%。实验证实基于引导滤波提取的多尺度特征能有效地描述地物结构,进而获得更高的分类精度和改善目视解译效果。 相似文献