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首页> 外文会议>IEEE International Geoscience and Remote Sensing Symposium >Improved Iterative Error Analysis Using Spectral Similarity Measures for Vegetation Classification in Hyperspectral Images
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Improved Iterative Error Analysis Using Spectral Similarity Measures for Vegetation Classification in Hyperspectral Images

机译：基于光谱相似度的改进迭代误差分析在高光谱图像植被分类中的应用

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Iterative error analysis (IEA) is one of popular, sequential and linear constrained endmember extraction algorithm that uses spectral angle mapping (SAM) to calculate angles between spectral vectors. However, IEA has a limit that discriminating similar spectral vector is difficult because SAM does not consider positive and negative correlations. Since vegetation has similar spectral properties, it is difficult to classify different vegetation types. To improve IEA for various applications, such as crop classification and change detection, spectral similarity measures other than SAM have been applied to IEA. Many spectral similarity measures have been developed to calculate the similarities among spectral signatures and these are divided into the original methods and the newly developed hybrid algorithms. In this study, the original methods used were SAM, SCA, and SID, while the hybrid methods included SAMSID, SCASID, Jeffries-matusita measures-SAM (JMSAM), and normalized spectral similarity score (NS3). A Compact airborne spectrographic imager image including three crops and road was used and similarity values of four endmembers extracted by modified IEA were calculated. The CASI image was classified using endmembers and minimum distance classifier. The classification accuracy of the modified IEA with SMA, SCA, SID, SAMSID, SCASID, JMSAM, and NS3 were 84.45%, 85.56%, 61.47%, 65.83%, 62.11%, 93.47%, 90.29%. SID based algorithm has lower accuracy because SID tends to make two similar spectral signatures more similar. The results showed that JASAM was most effective to classify different vegetation types. The modified IEA with JMSAM could classify vegetation more effectively than the original IEA.
机译：迭代误差分析（IEA）是一种流行的，连续且线性约束的端基提取算法，该算法使用光谱角度映射（SAM）来计算光谱向量之间的角度。但是，IEA具有局限性，因为SAM不考虑正相关和负相关，因此很难区分相似的频谱矢量。由于植被具有相似的光谱特性，因此很难对不同的植被类型进行分类。为了改善IEA在各种应用中的应用，例如农作物的分类和变化检测，除SAM以外的光谱相似性度量已应用于IEA。已经开发了许多频谱相似性度量来计算频谱特征之间的相似性，并将这些划分为原始方法和新开发的混合算法。在这项研究中，最初使用的方法是SAM，SCA和SID，而混合方法包括SAMSID，SCASID，Jeffries-matusita度量-SAM（JMSAM）和归一化光谱相似性评分（NS3）。使用包括三个农作物和道路的紧凑型机载光谱成像仪图像，并计算了通过改进的IEA提取的四个末端成员的相似度值。使用末端成员和最小距离分类器对CASI图像进行分类。改进的IEA的SMA，SCA，SID，SAMSID，SCASID，JMSAM和NS3的分类准确度分别为84.45％，85.56％，61.47％，65.83％，62.11％，93.47％，90.29％。基于SID的算法具有较低的准确性，因为SID倾向于使两个相似的频谱特征更加相似。结果表明，JASAM对不同植被类型的分类最有效。与原始IEA相比，使用JMSAM修改的IEA可以更有效地对植被进行分类。

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