基于空间上下文迁移推理的土地利用图斑变化检测方法研究

项目名称： 基于空间上下文迁移推理的土地利用图斑变化检测方法研究

项目编号： No.41501397

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 天文学、地球科学

项目作者： 陈静波

作者单位： 中国科学院遥感与数字地球研究所

项目金额： 20万元

中文摘要： 图斑变化检测是土地利用动态遥感监测的核心工作之一。本项目将特征迁移和案例推理结合，重点研究基于迁移推理的复合地物分类方法，实现历史图斑指导下的现势图斑分类后变化检测。首先，提取由纹理和空间指数构成的图斑空间上下文特征，针对复合地物纹理精确提取的难点，研究图斑有意义对象约束的局部描述子兴趣点空间分布优化方法、基于空间共现核的纹理直方图空间信息编码方法；其次，围绕利用历史图斑案例指导现势图斑分类的域适应问题，研究顾及时空域相似性、特征稳定性的特征迁移方法，提取时空不变的空间上下文特征；最后，构建新增建设用地检测代价最小约束下的案例推理模型以增强分类模型的适用性，研究基于特征相关性的缺失特征插值方法以增强模型的鲁棒性，并通过分类后比较检测变化图斑。通过本项目的研究，建立基于空间上下文迁移推理的图斑变化检测方法体系，提高图斑变化检测的精度和自动化程度。

中文关键词： 土地利用；图斑；空间上下文；迁移学习；案例推理

英文摘要： Land-use parcel change detection is one of the core works in land-use remote sensing monitoring. To extract current changed parcels under guides of historical ones by post-classification change detection, this research combines feature transfer and case-based reasoning and puts emphasis on composite object classification method based on transfer reasoning. Firstly, spatial context including texture and spatial metrics is extracted to characterize parcel. In view of the precision problem in composite object texture extraction, optimization method for spatial distribution of local interest points under constraint of meaningful segments, and spatial information encoding method of texture histogram based on spatial co-occurrence kernel are researched. Secondly, to solve the domain adaption problem encountered when classifying current parcels under the guides of historical ones, feature transfer method which takes domain similarity and feature stability into consideration simultaneously is researched to extract space-time invariant spatial context. Finally, applicability of parcel classification model is enhanced by imposing cost minimization constraint on case-based reasoning model to benefit newly-increasing construction extraction, and model robustness is enhanced by interpolating missing feature using correlated ones. Then, changed parcel is extracted by post-classification comparison. This research will help establish the methodology of land-use parcel change detection based on transfer reasoning of spatial context, and will prompt accuracy and automaticity of parcel change detection.

英文关键词： Land-use;Parcel;Spatial context;Transfer learning;Case-based reasoning