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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >A Biologically Inspired Object Spectral-Texture Descriptor and Its Application to Vegetation Classification in Power-Line Corridors
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A Biologically Inspired Object Spectral-Texture Descriptor and Its Application to Vegetation Classification in Power-Line Corridors

机译:生物启发的对象谱纹理描述符及其在电力线走廊植被分类中的应用

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摘要

The use of appropriate features to represent an output class or object is critical for all classification problems. In this letter, we propose a biologically inspired object descriptor to represent the spectral-texture patterns of images or objects. The proposed feature descriptor is generated from the pulse spectral frequencies (PSFs) of a pulse-coupled neural network, which is invariant to rotation, translation, and small scale changes. The proposed method is first evaluated in a rotation- and scale-invariant texture classification using the University of Southern California Signal and Image Processing Institute texture database. It is further evaluated in an application of vegetation species classification in power-line corridor monitoring using airborne multispectral aerial imagery. The results from the two experiments demonstrate that the PSF feature is effective in representing the spectral-texture patterns of objects, and it shows better results than classic color histogram and texture features.
机译:对于所有分类问题,使用适当的特征表示输出类或对象至关重要。在这封信中,我们提出了一种受生物启发的对象描述符,以表示图像或对象的光谱纹理图案。所提出的特征描述符是根据脉冲耦合神经网络的脉冲频谱频率(PSF)生成的,该频率对于旋转,平移和小范围变化均不变。首先使用南加州大学信号与图像处理研究所的纹理数据库在旋转不变和尺度不变的纹理分类中对提出的方法进行评估。植被物种分类在机载多光谱航拍图像的电力线走廊监控中的应用得到了进一步评估。这两个实验的结果表明,PSF功能可有效表示对象的光谱纹理图案,并且比经典的颜色直方图和纹理功能显示出更好的结果。

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