多时相遥感影像样本迁移模型与地表覆盖智能分类

Training Sample Transfer Learning from Multi-temporal Remote Sensing Images for Dynamic and Intelligent Land Cover Classification

摘要:

利用时间序列遥感影像重建过去几十年的地表覆盖是实现时空多维地理场景感知与动态建模的基础，但存档历史遥感影像分类面临样本选择难、多时相影像协同解译水平低的问题。研究提出了一种基于已有土地覆盖产品与对应遥感影像中几何及属性特征约束的样本时空迁移方法，将迁移获得的训练样本嵌入多时相地表覆盖分类框架，获得多期地表覆盖分类结果，实现历史时期地表环境的智能感知与动态制图。太湖流域多时相分类的结果表明，无监督样本迁移方法可以充分利用先验几何约束和光谱属性，从参考地表覆盖产品中快速获得可靠的训练样本，多时相分类精度均高于89%，满足大区域多时相地表覆盖的分类需求，为地理环境演变建模提供了有效支持。

Abstract:

Reconstructing historical land cover dynamics by time series remote sensing images is the basis of geographic scene sensing and modeling. However， classification of historical archived remote sensing images is quite difficult due to the limited training samples and low interpretation capability for multi-temporal images. A spatio-temporal training sample transfer method from existing land cover products and corresponding remote sensing images was proposed based on geometric and attribute constrains. The transferred training samples were then embedded into a multi-temporal land cover classification framework to solve the problem of high sample labeling cost in multi-temporal image classification. The multi-temporal classification results in the Taihu Basin show that the proposed unsupervised sample transfer method can make full use of the prior geometric constraints and spectral properties of the products and images， and the classification accuracy in large scale area is over 89%. The results demonstrate that the proposed method is effective for land cover updating and geographic environment evolution modelling.