聚类与自适应波段选择结合的高光谱图像降维

针对自适应波段选择法(adaptive band selection,ABS)对高光谱图像降维后得到的最优波段子集用于地物目标分类处理时,分类精度不理想的问题,提出一种K-means聚类与ABS结合的高光谱图像降维方法。算法采用K-means聚类算法对所有波段进行聚类,聚类中分别采用相关系数和欧氏距离2种相似性度量,选取各聚类中ABS指数最大的波段,作为最优波段子集。通过实验,将所提方法与ABS进行分类精度比较。实验结果表明,所提方法在分类精度上优于ABS法,以相关系数作为相似性度量的K-means聚类与ABS结合的降维方法分类效果更好。     

基于3维上下文预测的高光谱图像无损压缩

如今高光谱数据的有效压缩已成为遥感技术发展中需要迫切解决的问题,为了对高光谱数据进行有效压缩,提出了一种基于3维上下文预测的高光谱图像无损压缩算法。该算法首先根据相邻波段间的相关性大小进行波段分组,同时对各个分组重新进行波段排序;然后采用自适应波段选择算法对高光谱图像进行降维,再利用k-means算法对降维后的波段谱向矢量进行聚类;最后在参考波段和当前波段中通过定义3维上下文预测结构,在聚类结果的基础上,对各个分类分别训练其最优的预测系数。实验结果表明,该方法可显著降低压缩后图像编码的平均比特率。     

基于类别可分性的高光谱图像波段选择

高光谱遥感数据具有丰富的光谱信息,应用十分广泛,但其冗余的光谱信息有时会限制高光谱图像的分类等的精度以及计算复杂度.为了提高解译效率,高光谱图像降维不可或缺,这也是高光谱图像处理的研究热点之一.提出了一种基于类别可分性的高光谱图像波段选择方法(Endmember Separability Based band Selection,ESBB),该方法通过Mahalanobis距离最大化图像中各类地物的可分性来确定最优的波段组合.相较于其他监督波段选择算法,该方法不需要大量训练样本,不用对每个组合做分类处理.对波段选择后的结果进行分类的实验结果证明,该方法是一个快速有效的波段选择方法,可以得到一个较好的分类精度.     

一种改进的高光谱数据自适应波段选择方法

高光谱遥感数据具有的波段数目多、波段宽度窄、数据量庞大等特点给图像的进一步解译带来了困难。为了解决这一问题,对自适应波段选择的降维方法进行了改进,不仅考虑了高光谱遥感图像波段的信息量和波段间的相关性,更重要的是考虑了各地物连续光谱间的可分性。光谱间的可分性距离越大,表明类间的可分性越大,地物越清晰。首先选出了能有效区分图像上任意两类别的理想波段子集,再根据波段子集中任意3波段的相关系数之和最小和它们的均方差最小两个指标,选出任意两类对间那些包含信息量大、相关性又小、谱间差异又大的3波段组合(且不唯一),最后对整幅影像选出的最佳3波段45、75、85合成的假彩色图像用光谱角度制图法(SAM)进行了分类,总体分类精度达到91.7%,Kappa系数达到0.82。     

基于偏最小二乘法的高光谱图像波段选择

波段选择是高光谱图像降维的重要手段,将偏最小二乘法引入到高光谱图像波段选择中来,提出一种基于偏最小二乘法的波段选择方法.首先用偏最小二乘法计算训练集样本的潜在向量,接着分析波段与潜在向量的相关程度以确定各波段对于图像分类的重要程度,最后分析候选波段的相关度,获得最终选择波段.实验结果表明,与其他现有波段选择方法相比,该...     

利用信息熵的高光谱遥感影像降维方法

高光谱遥感影像以其众多的波段数目，为地表观测提供近乎连续的波谱数据；然而海量的高光谱遥感影像存在着大量的信息冗余，为数据的处理带来了挑战。因此在对高光谱遥感影像进行存储、分析及可视化等操作之前，对高光谱遥感影像降维处理成为预处理的关键环节之一。利用信息熵理论，将高光谱遥感影像的各波段抽象为具有相关性的独立个体，设计了高光谱遥感影像的决策表矩阵，进而计算各波段的信息熵，量化各波段的信息量，从而将各波段根据信息增益进行排序。用户可根据高光谱遥感影像应用的精度需求，按排序选择波段组合，从而达到降维目的。以遥感分类结果的精度评价为例，对高光谱遥感降维方法的可行性和优越性进行评价。实验结果表明，该方法相较其他特征选取降维方法，能获得更高的分类精度。     

基于自动子空间划分的高光谱数据特征提取

针对遥感高光谱图像数据量大、维数高的特点,提出了一种自动子空间划分方法用于高光谱图像数据量减小处理。该方法主要包括3个处理步骤:数据空间划分,子空间主成分分析和基于类别可分性准则的特征选择。该方法充分利用了高光谱图像各波段数据之间的局部相关性,将整个数据划分为若干个具有较强相关性的独立子空间,然后在子空间内利用主成分分析进行特征提取,根据各类地物间的类别可分性选择有效特征,最后利用地物分类来验证该方法的有效性。实验结果表明,该方法能够有效地实现高光谱图像数据维数减小和特征提取,同现有的自适应子空间分解方法和分段主成分变换方法相比,该方法所提取的特征用于分类时能获得较好的分类精度。利用该方法进行处理,当高光谱数据维数降低了90%时,9类地物分类实验的总体分类精度可以达到80.2%。     

组合因子最优的线性预测波段选择

目的 高光谱图像分辨率高,数据量大,信息的冗余程度高,给数据处理带来了很大的困难。为了高效地实现数据降维,使降维后的数据冗余度小且信息量大,提出一种基于组合因子最优的波段选择方法。方法 首先对高光谱数据进行波段子空间划分,在各子空间中通过线性预测误差来计算误差最小和次小的两个波段,结合它们的标准差,计算出它们的组合因子,通过比较组合因子来决定所要去除的波段。结果 该方法的计算效率高,相同条件下计算时间比最快的方法有轻微的减少。使用支持向量机(SVM)对波段子集分类,并将该方法与其他方法进行分类准确率比较,相同条件下比其他方法的最高准确率有1.5%的提升。结论 组合因子的方法综合考虑了波段子集的最小冗余度和最大信息量,得到了较好的波段子集,并且有较小的计算复杂度,适用于AVIRIS (airborne visible infrared imaging spectrometer)等各种高光谱图像数据。     

高光谱影像波段选择算法研究*

基于高光谱影像数据的特点,分析了高光谱数据的降维方法。着重探讨了波段选择的若干算法:熵及联合熵、最佳指数因子、自动子空间划分、自适应波段选择、波段指数和最优波段指数等算法。分析了各种算法的有效性、局限性和计算复杂度,并针对波段指数的不足,设计了最优波段指数(OBI)波段选择新算法。最后通过具体的试验,验证了各种算法的性能。     

基于同质性降维和CMP算法的高光谱图像分类

针对高光谱数据维数高,波段间冗余信息大的问题,提出一种基于同质性降维和组合匹配追踪算法的高光谱图像分类方法。该方法首先利用均值漂移算法对高光谱图像进行分割得到同质性图像块,对同质性的图像块进行流行学习得到降维映射函数,然后由降维后的高光谱数据训练稀疏最小二乘支持向量机分类模型,为避免正交匹配追踪稀疏重构算法迭代次数多的缺点,提出一种基于组合匹配追踪的稀疏重构求解方法。通过高光谱数据的分类结果可以得出,该方法有效提高了高光谱图像的分类精度。     

基于多元数据和不同分类算法的遥感影像信息提取及精度评价 ——以祁连山东段为例

以祁连山东段典型山地系统为研究区,通过提取研究区TM影像的主成分、各类植被指数、基于灰度共生矩阵的影像纹理特征以及研究区地形特征等数据,应用最优波段指数方法得到最优波段组合,并运用非监督分类、最大似然法、支持向量机分类法、决策树分类法对上述最优波段进行分类研究。结果表明多尺度数据挖掘有利于分类精度的提高,同时选取合适的判断标准的决策树分类方法在遥感信息提取中有比较直观意义和较高的分类精度。在上述分类方法中分类精度由高到低为决策树分类>支持向量机法>最大似然法>非监督分类法。决策树分类总体分类精度为94.50%,kappa系数为0.9122。
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基于信噪比估计的波段选择与高光谱异常检测

有效的波段选择方法可以极大地提高高光谱图像处理速度的同时改善处理效果。为了自动判断低信噪比波段,提出了一种基于小波变换的图像信噪比估计(SNR estimation,SNRE)方法,利用小波变换后对角方向上的高频成分估计噪声方差并计算信噪比。将该方法分别结合基于方差和相关系数(V_COR)的最优索引指数、最大信息量(MI)、高阶矩(偏度或峰度)结合信息散度(K3_KL)等3种基于信息量的波段选择方法后选择波段。将这些改进后的波段选择方法应用于高光谱异常检测。实验结果表明SNRE预选波段结合MI和K3_KL选择波段用于异常检测能进一步提高检测精度。     

A novel feature ranking algorithm for text classification: Brilliant probabilistic feature selector (BPFS)

Text classification (TC) is a very crucial task in this century of high-volume text datasets. Feature selection (FS) is one of the most important stages in TC studies. In the literature, numerous feature selection methods are recommended for TC. In the TC domain, filter-based FS methods are commonly utilized to select a more informative feature subsets. Each method uses a scoring system that is based on its algorithm to order the features. The classification process is then carried out by choosing the top-N features. However, each method's feature order is distinct from the others. Each method selects by giving the qualities that are critical to its algorithm a high score, but it does not select by giving the features that are unimportant a low value. In this paper, we proposed a novel filter-based FS method namely, brilliant probabilistic feature selector (BPFS), to assign a fair score and select informative features. While the BPFS method selects unique features, it also aims to select sparse features by assigning higher scores than common features. Extensive experimental studies using three effective classifiers decision tree (DT), support vector machines (SVM), and multinomial naive bayes (MNB) on four widely used datasets named Reuters-21,578, 20Newsgroup, Enron1, and Polarity with different characteristics demonstrate the success of the BPFS method. For feature dimensions, 20, 50, 100, 200, 500, and 1000 dimensions were used. The experimental results on different benchmark datasets show that the BPFS method is more successful than the well-known and recent FS methods according to Micro-F1 and Macro-F1 scores.     

分段2维主成分分析的超光谱图像波段选择

目的 超光谱图像具有极高的谱间分辨率,巨大的数据量给分类识别等后续处理带来很大压力。为了有效降低图像数据维数,提出基于分段2DPCA的超光谱图像波段选择算法。方法 首先根据谱间相关性对原始图像进行波段分组,然后根据主成分反映每个光谱波段的信息比重分别对每组图像进行波段选择,从而实现超光谱图像的谱间降维。结果 该算法有效降低了超光谱图像的光谱维数,选择的波段明显反映出不同地物像元矢量的区别。结论 实验结果表明,该波段选择算法相对传统算法速度更快,并且较好地保留了原始图像的局部重要信息,对后续处理有积极意义。     

采用分段主成分和PPI的高光谱影像分类

高光谱遥感影像波段多且存在混合像元,特征提取以及端元提取都是高光谱影像分类必不可少的工作,分类方法的选择也是因地适宜。以福建省泉州市德化县下属某一地区的CASI影像为实验数据,基于分段主成分(segmental principal component analysis,SPCA)和纯净像元指数法(pure pixel index,PPI),提出了最小距离(minimum distance classification,MDC)和二进制编码(binary encoding,BE)的高光谱影像分类方法。实验结果表明,MDC的总体精度为69.71%,BE的总体精度为70.88%。对单一地物精度而言2种方法各有其长,MDC对道路的分类精度更高,为98.08%;而植被、耕地和水体采用BE方法的分类精度更高,分别为94.12%、98.08%、98.11%。本文提出的方法应用于CASI高光谱影像,对该研究区的地物分类研究有一定的实用性和参考价值。     

Sentinel-2 Satellite Image Fusion Methodand Quality Evaluation Analysis

Sentinel-2 satellite sensors acquire three kinds of optical remote sensing images with different spatial resolutions.How to improve the spatial resolution of lower spatial resolution bands by fusion method is one of the problems faced by Sentinel-2 applications.Taking the Sentinel\|2B image as the data source,a high spatial resolution band was generated or selected from the four 10m spatial resolution bands by four methods:the maximum correlation coefficient,the central wavelength nearest neighbor,the pixel maximum and the principal component analysis.We fused the one high spatial resolution band produced and six multispectral bands with 20 m spatial resolution by the five fusion methods of PCA,HPF,WT,GS and Pansharp to produce six multispectral bands with 10 m spatial resolution and the fusion results were evaluated from three aspects:qualitative and quantitative (information entropy,average gradient,spectral correlation coefficient,root mean square error and general image quality index) and classification accuracy of fused images.Results show that the fusion quality of Pansharp with the maximum correlation coefficient is better than other fusion methods,and the classification accuracy is slightly lower than the GS with the pixel maximum of the highest classification accuracy and far higher than the original four multispectral image with 10 m spatial resolution.According to the classification accuracy of experimental data,different fusion methods have different advantages in extraction of different ground objects.In application,appropriate schemes should be selected according to actual research needs.This research can provide reference for Sentinel-2 satellite and similar satellite data processing and application.     

Band elimination of hyperspectral imagery using partitioned band image correlation and capacitory discrimination

Due to the very large number of bands in hyperspectral imagery, two major problems which arise during classification are the ‘curse of dimensionality’ and computational complexity. To overcome these, dimensionality reduction is an important task for hyperspectral image analysis. An unsupervised band elimination method is proposed which iteratively eliminates one band from the pair of most correlated neighbouring bands depending on the discriminating capability of the bands. Correlation between neighbouring bands is calculated over partitioned band images. Capacitory discrimination is used to measure the discrimination capability of a band image. Finally, four evaluation measures, namely classification accuracy, kappa coefficient, class separability, and entropy are calculated over the selected bands to measure the efficiency of the proposed method. The proposed unsupervised band elimination technique is compared to three popular state-of-the-art approaches, both qualitatively and quantitatively, and shows promising results compared to them.     

Unsupervised hyperspectral band selection using ranking based on a denoising error matching approach

In this paper, we focus on utilizing the image denoising method for ranking of significant bands in hyperspectral imagery. We make use of the fact that the denoising error of bands varies with the significant information content of the bands in hyperspectral imagery. The denoising error is computed for each band individually and compared using a matching parameter with the denoising error of a reference image. The reference image is selected to be the first principal component corresponding to the maximum information. Three matching parameters including mutual information (MI), correlation coefficient (r) and the structural similarity index (SSIM) were used for ranking the bands based on the match with the denoising error of the reference image. The proposed algorithm is tested using three datasets, namely, Indian Pines, Salinas and Dhundi. The Indian Pines and Salinas datasets were acquired from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensor and comprised rural and agricultural area. The Dhundi dataset of Hyperion comprises mostly of features corresponding to snow-covered mountainous regions. To assess the accuracy of the proposed method, a supervised classification was carried out using a random forest classifier with 20% training pixels selected randomly from the ground reference. The proposed method yielded significantly better results determined by the kappa coefficient (κ) of 0.756, 0.910 and 0.996 for the Indian Pines, Salinas and Dhundi datasets, respectively, over several other state of the art methods. The classification results of the proposed method also yielded better results than those obtained by the state-of-the-art methods for hyperspectral band selection.