In most of the existing studies on the band selection using the convolutional neural networks (CNNs), there is no exact explanation of how feature learning helps to find the important bands. In this letter, a CNN-based band selection method is presented, and the process of feature tracing is explained in detail. First, a 1-D CNN model is designed and trained to reach high accuracy. Next, the derivative of the sum of partial output combinations of a layer is obtained with respect to the input layer of the CNN. Then, the derivative maps are used to obtain the sample saliency maps and class saliency maps (CSMs). Finally, the bands are selected using the CSMs. The proposed model is verified by experiments on different data sets and compared with other related methods. The results show that the proposed model can achieve better performance than the other methods.

中文翻译：

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使用卷积神经网络的解释性梯度显着图选择波段

在大多数使用卷积神经网络 (CNN) 进行波段选择的现有研究中，没有准确解释特征学习如何帮助找到重要波段。在这封信中，提出了一种基于 CNN 的波段选择方法，并详细解释了特征跟踪的过程。首先，设计并训练一维 CNN 模型以达到高精度。接下来，得到一个层的部分输出组合之和相对于CNN的输入层的导数。然后，派生图用于获得样本显着图和类显着图（CSM）。最后，使用 CSM 选择频段。通过在不同数据集上的实验验证了所提出的模型，并与其他相关方法进行了比较。