时空融合技术在区域地表覆盖时序分类中的应用

由于中高分辨率遥感影像数据时序性不强,分类过程中无法准确记录地物的时序特征.为增加地物时序变化特征,本文使用时空融合模型重建高时序高分辨率遥感影像,分析加入时相特征对分类结果的影响.以河北省石家庄市中部地区为例,本文采用3种时空融合模型重建高时序的30 m分辨率的遥感影像,增加影像时序分类特征,采用随机森林对年度重建时...

Application of spatio-temporal fusion technology in time series classification of regional land cover

Due to the low time series of medium and high-resolution remote sensing image data, the time series characteristics of ground objects cannot be accurately recorded during the classification process. In order to increase the temporal change characteristics of ground features, the study uses the spatio-temporal fusion model to reconstruct high-sequence and high-resolution remote sensing images, and analyzes the influence of adding temporal features on the classification results. Taking the central area of Shijiazhuang City, Hebei Province as an example, the study uses three spatiotemporal fusion models to reconstruct high-time sequence 30-meter resolution remote sensing images to add time series classification features. This study uses random forests to classify annual reconstructed time series images, and analyzed the impact of different number of reconstructed images and different time spans on the classification results. Experiments show that the accuracy of image classification by reconstructing annual time series is better than that of single image. The classification accuracy increases with the number of time series images, when the number of images is selected as 12 scenes (one scene per month), the classification accuracy tends to be stable. And different time periods have different influence on classification results. With the introduction of imagery during vegetation change, the classification accuracy is the highest.