Abstract

We show that for the classification of fragments of a hyperspectral image, it is very effective to first transform its spectral features into principal components and then to recognize it using a convolutional neural network trained on a sample composed of fragments of this image. High percentage of correct classification was obtained when working with a large-format hyperspectral image while some of the classes of the hyperspectral image are very close to each other and, accordingly, are difficult to distinguish by hyperspectra. We investigate the dependence of the correct classification on the change in the size of the fragments from which the training and validation samples are composed and on the parameters of the convolutional neural network.

中文翻译：

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使用传统神经网络对高光谱图像进行分类

摘要

我们表明，对于高光谱图像的碎片分类，首先将其光谱特征转换为主成分，然后使用在由该图像碎片组成的样本上训练的卷积神经网络来识别它是非常有效的。在处理大格式高光谱图像时获得了高比例的正确分类，而高光谱图像的某些类别彼此非常接近，因此难以通过高光谱进行区分。我们研究了正确分类对组成训练和验证样本的片段大小变化以及卷积神经网络参数的依赖性。