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首页> 外文会议>Mathematics of Data/Image Pattern Recognition, Compression and Encryption with Applications IX >Hyperspectral feature classification with alternate wavelet transform representations
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Hyperspectral feature classification with alternate wavelet transform representations

机译：具有备用小波变换表示的高光谱特征分类

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摘要

The effectiveness of many hyperspectral feature extraction algorithms involving classification (and linear spectral unmixing) are dependent on the use of spectral signature libraries. If two or more signatures are roughly similar to each other, these methods which use algorithms such as singular value decomposition (SVD) or least squares to identify the object will not work well. This especially goes for these procedures which are combined with three-dimensional discrete wavelet transforms, which replace the signature libraries with their corresponding lowpass wavelet transform coefficients. In order to address this issue, alternate ways of transforming these signature libraries using bandpass or highpass wavelet transform coefficients from either wavelet or Walsh (Haar wavelet packet) transforms in the spectral direction will be described. These alternate representations of the data emphasize differences between the signatures which lead to improved classification performance as compared to existing procedures.
机译：许多涉及分类（和线性光谱分解）的高光谱特征提取算法的有效性取决于光谱特征库的使用。如果两个或更多个签名彼此大致相似，则这些使用诸如奇异值分解（SVD）或最小二乘之类的算法来识别对象的方法将无法正常工作。尤其是将这些过程与三维离散小波变换相结合，该过程将签名库替换为其相应的低通小波变换系数。为了解决这个问题，将描述使用来自频谱方向上的小波或Walsh（Haar小波包）变换的带通或高通小波变换系数来变换这些签名库的替代方法。数据的这些替代表示方式强调了签名之间的差异，与现有程序相比，这些差异导致改进的分类性能。

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