【目的】研究城市土地类型的变化,分析城市进化过程对环境气候、城市发展以及政府决策产生的影响。【方法】以15~30 m分辨率的NWPU-RESISC45标准数据集和哈尔滨城区Landsat 8遥感影像为实验数据,制作了包含城市建筑及道路、水体、植被、裸地4种土地类型的遥感影像数据集。在实验数据中加入纹理信息,提取SIFT(scale-invariant feature transform, SIFT)特征点。通过K-means聚类算法获取包含大量语义信息的视觉词典,从而构造视觉词袋模型(bag of visual words, BOVW)。然后将BOVW提取的特征点与支持向量机(support vector machine, SVM)分类器结合,对制作的数据集进行分类。最后,利用2013年、2019年同一季节Landsat 8影像,以哈尔滨市松北区为例计算各土地类型的位置及面积变化信息。【结果】基于BOVW和SVM的分类结果与5种单一分类模型和3种“特征提取+分类器”模型对比,发现使用尺寸为550个词汇的视觉词典时,本研究模型的分类与变化监测精确度分别为79.40%、79.29%。结合哈尔滨城市具体数据的监测结果表明,在2013-2019年间,哈尔滨市松北区城市建筑及道路与植被类型的覆盖面积减少明显,水体与裸地类型的覆盖面积增加,这一变化情况符合近年来哈尔滨市政府陆续推出的环境保护五年规划,以及其总体规划中合理控制城市规模的相关政策要求。【结论】对于时间跨度长、分辨率不高的Landsat遥感影像,BOVW和SVM的变化监测模型在土地类型变化监测方面效果良好,在一定程度上可提高分类与变化监测的精度,为土地类型变化监测提供借鉴。

【Objective】 By studying changes in urban land types, we can determine the impact of urban evolution on environmental climate, urban development and government decision-making. 【Method】 Using the NWPU-RESISC45 standard dataset with a resolution of 15-30 m alongside Landsat 8 remote sensing images of the Harbin urban area as experimental data, we created a remote sensing image dataset that included four land types: urban buildings and roads, water bodies, vegetation and bare land. Texture information was added to the experimental data to extract SIFT (the scale-invariant feature transform) feature points. A visual dictionary containing a large amount of semantic information was also obtained using the K-means clustering algorithm to construct a bag of visual words (BOVW). The feature points extracted using BOVW were then combined with a support vector machine (SVM) to classify the dataset. Finally, using Landsat8 images of the same seasons in 2013 and 2019 and using those of the Songbei District of Harbin City as examples, the location and area change information for each land type were calculated. 【Result】 The classification results based on BOVW and SVM were compared with five single classification models and three “feature extraction + classifier” models. When using a visual dictionary of 550 words, the classification and change-monitoring accuracies of the model were 79.40% and 79.29%, respectively. The monitoring results, combined with the specific data of Harbin City, also showed that the coverage area of urban buildings and roads type, vegetation types in the Songbei District of Harbin decreased significantly, whilst the coverage area of water bodies and bare land types increased between 2013 and 2019. This change was in line with the five-year plan for the environmental protection successively launched by the Harbin municipal government in recent years, alongside the relevant policy requirements for the reasonable control of the urban scale in the master plan. 【Conclusion】 For Landsat remote sensing images with a long time-span and a low resolution, the change-monitoring models built using BOVW and SVM were both effective in monitoring land type changes. To a certain extent, this could improve the accuracy of classification and change monitoring, whilst providing a reference for the land type change monitoring.