图像融合、识别与导向过程在遥感、军事和医学等领域有很重要的应用，关于它的研究符合国家重大需求。本项目拟研究图像融合、识别与导向中的数学理论与方法. 主要内容有：.1. 因为配准是融合的基础，而以前的配准模型对特定的保几何特征的图像配准不适合，本项目拟建立新的遥感和医学等领域中特定(局部刚性、局部体积)约束保持的弹性配准变分模型的数学理论. .2. 建立BV2、BGV2框架下水平集形状与多尺度保真、曲率与光谱保持的新模型和分数阶Pansharping融合模型，提高多源融合图像空间、光谱分辨率. .3. 用小波、PDE及其它数学理论处理退化、非线性情形下, 算法的收敛、误差估计、稳定性. .4. 针对实际问题中多模态高维大规模图像, 研究深度学习体系下像素、特征和决策级图像融合识别最优并行算法, 为建立针对遥感领域完整的图像信息的识别、预测、解译及实时监测系统提供强有力的理论支撑.

Image fusion, recognition and orientation, which is applied in remote sensing, military, medicine and other fields, is in line with the national needs. The project intends to study the mathematical theories and methods in image fusion, recognition and orientation, which is including:. 1. Due to fusion is based on registration, previously registration model is not suitable for specific fidelity geometric image registration, the project is ready to build new mathematical theories about of elastic registration variation model with some specific (rigidity, volume) restriction,which is from the image fusion of remote sensing and medical field..2. Creating a new level set geometry, multi-scale fidelity, curvature and spectral preserving fidelity model and a fractional Pansharping model to improve the spatial-spectral resolution of multi-source image fusion, under the framework of BV2 and BGV2; .3.Using some wavelet frame, PDE, and other mathematical and statistical theories to deal with parallel algorithms, convergence, error estimates and stability in the volatility and nonlinear case; .4. The project research pixel level, feature-level and decision-level image fusion optimal parallel algorithm in deep learning, for multi-mode, high-dimensional and large-scale image data. As a result, we could provide a strong theoretical support for the identification, prediction, interpretation of complete image information in remote sensing and real-time monitoring system.