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Evaluation of the use of spectral and textural information by an evolutionary algorithm for multi-spectral imagery classification

机译：通过进化算法对光谱和纹理信息的使用进行评估，以进行多光谱图像分类

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Considerably research has been conducted on automated and semi-automated techniques that incorporate image textural information into the decision process as an alternative to improve the information extraction from images while reducing time and cost. The challenge is the selection of the appropriate texture operators and the parameters to address a specific problem given the large set of available texture operators. In this study we evaluate the optimization characteristic of an evolutionary framework to evolve solutions combining spectral and textural information in non-linear mathematical equations to improve multi-spectral image classification. Twelve convolution-type texture operators were selected and divided into three groups. The application of these texture operators to a multi-spectral satellite image resulted into three new images (one for each of the texture operator groups considered). These images were used to evaluate the classification of features with similar spectral characteristics but with distinct textural pattern. Classification of these images using a standard image classification algorithm with and without the aid of the evolutionary framework have shown that the process aided by the evolutionary framework yield higher accuracy values in two out of three cases. The optimization characteristic of the evolutionary framework indicates its potential use as a data mining engine to reduce image dimensionality as the system improved accuracy values with reduced number of channels. In addition, the evolutionary framework reduces the time needed to develop custom solutions incorporating textural information, especially when the relation between the features being investigated and the image textural information is not fully understood.
机译：已经对自动化和半自动化技术进行了相当多的研究，这些技术将图像纹理信息纳入决策过程，以作为替代方案，以改进从图像中提取信息的方式，同时减少时间和成本。面临的挑战是在给定大量可用纹理运算符的情况下，选择合适的纹理运算符和参数以解决特定问题。在这项研究中，我们评估了演化框架的优化特性，以解决将光谱和纹理信息结合到非线性数学方程中以提高多光谱图像分类的解决方案。选择了十二个卷积型纹理运算符并将其分为三组。这些纹理算子在多光谱卫星图像上的应用产生了三张新图像（所考虑的每个纹理算子组一个）。这些图像用于评估具有相似光谱特征但具有明显纹理图案的特征的分类。使用标准图像分类算法在有或没有进化框架的帮助下对这些图像进行分类表明，在三种情况中的两种情况下，进化框架所辅助的过程会产生更高的精度值。演化框架的优化特性表明它潜在地用作数据挖掘引擎以降低图像维数，因为系统可通过减少通道数来提高精度值。此外，演化框架减少了开发结合了纹理信息的定制解决方案所需的时间，尤其是当所研究的特征与图像纹理信息之间的关系尚未完全理解时。

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