针对传统城墙病害检测采用人工勘测方法，检测效率较低且易受到主观因素干扰等问题，提出一种基于卷积神经网络的城墙多光谱成像病害无损检测方法，利用最小噪声分离方法对城墙多光谱成像数据进行预处理，降低数据维度的同时保留原始数据特征，减少数据噪声；为解决城墙不同病害类型的像素混杂多样造成分类准确率较低的问题，利用卷积操作对城墙病害进行特征提取，保留最重要的特征并去除无关特征，稀疏网络模型；通过全连接层对提取到的特征进行整合梳理和分类，并加入两次dropout防止过拟合问题的出现。最后在城墙多光谱数据集上，使用训练后的卷积神经网络分类模型对城墙病害进行像素级分类检测，并将预测结果进行可视化展示。实验结果表明：总体精度和Kappa系数分别为93.28%和0.91，表明所提方法是有效的，该方法对提高城墙病害检测准确率、掌握城墙病害分布具有重要意义。

This paper proposes a nondestructive detection method for detecting wall disease by employing multi-spectral imaging based on convolutional neural networks. This method aims to address issues such as low detection efficiency and easy interference by subjective factors that are associated with the use of artificial survey methods in traditional wall disease detection. The minimum noise separation method is used to preprocess the multispectral imaging data of a city wall, which reduces the dimensions of the data while preserving the original data features and reducing data noise. To address the problem of low classification accuracy caused by mixed and diverse pixels of different types of wall damage, a convolution operation is used to extract the features of wall damage, with the most important features retained and irrelevant features removed, resulting in a sparse network model. The extracted features are integrated and sorted through a full connection layer. Two dropout are included to prevent overfitting. Finally, on a wall multispectral dataset, the trained convolution neural network classification model is used to detect wall damage at the pixel level, and the predicted results are displayed visually. Experimental results show that the overall accuracy and Kappa coefficient are 93.28% and 0.91, respectively, demonstrating the effectiveness of the proposed method, which is crucial for enhancing the detection accuracy of wall disease and fully understanding its distribution.