港口城市在“一带一路”建设中具有重要地位和作用，遥感影像数据可以为港口城市的动态监测提供有效的信息支撑。天宫二号空间实验室搭载的宽波段成像仪是新一代中分辨率成像光谱仪，具有谱段宽、视场大等特点，在可见近红外、短波红外、热红外谱段共设置有18个通道，空间分辨率分别为100 m、200 m和400 m，自2016-09—2019-07获取了大量的对地观测多光谱影像数据。本文面向 “一带一路”沿线的25个港口城市，从天宫二号宽波段成像仪获取的多光谱数据中挑选出58组不同时间获取的高质量影像，经过预处理、辐射校正、几何校正等处理和数据质量控制，构建了“一带一路”沿线港口城市专题影像数据集，并对该数据集的应用价值和潜力进行了分析。结果表明该数据集可以为“一带一路”沿线港口城市及其周边区域资源环境遥感监测提供重要信息支撑，在港口城市相关的中大尺度场景分类识别方面具有较大应用价值。

Port cities play a vital role in the implementation of the Belt and Road initiative. Remote sensing data can provide effective information support for the dynamic monitoring of port cities. In this paper, 58 groups of high-quality multispectral image data obtained by the wide-band imaging spectrometer of Tiangong-2 at different times are selected to construct the thematic dataset of 25 port cities along the Belt and Road. This dataset can provide important support for the remote sensing monitoring in resources and environment of the port cities and its surrounding area along the Belt and Road.Firstly, The raw data of wide-band imaging spectrometer undergo through a series of processing steps, such as format analyzing, field of view combination, homogeneity correction, image framing, absolute radiometric correction, geometric correction and format packaging, to generate standard data products and its auxiliary files. Then, image quality evaluation indexes, including signal to noise ratio, information entropy, clarity, contrast, radiation uniformity, etc. are adopted to assess data quality and to select high quality data products. Calibration and data quality control methods are used to improve the accuracy of data products. At last, several typical convolution neural network algorithms are used to verify the application potential of the data set in scene classification and recognition.350 typical port and non-port image samples are extracted from the data set in this article and be marked manually. After data enhancement, 250 samples of them are used to train several typical convolutional neural networks, they are AlexNet, VGG16, ResNet18 and MobileNet-v2, and the remaining samples are used for verification. The average recognition accuracy of the four algorithms is 91%, in which Resnet18 and MobileNet-v2 networks have the highest accuracy rate (93%), Resnet18 network has more advantages than other networks in precision rate, VGG16 and MobileNet-v2 network have the highest recall rate. Therefore, the dataset is suitable for the common convolutional neural networks and has good application effect in the scene classification and recognition of port city.The multispectral remote sensing image data set constructed in this paper comes from the wide-band imaging spectrometer of Tiangong-2. It has the characteristics of wide spectral range, high spatial resolution and diverse observation time, and is a beneficial extension for the existing data set of the port city along the Belt and Road. After strictly data processing and data quality control, the image data has standard format and reliable quality. Some of them has been applied in many fields, such as ocean, land, ecology and environment. In addition, it has a good application effect in the scene classification and recognition of port city. This data set is a valuable data resource for the remote sensing applications of the port city and its surrounding areas along the Belt and Road.