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A registration algorithm based on optical flow modification for multi-temporal remote sensing images covering the complex-terrain region

  • role： First author第一作者
  • Affiliation：

    School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China

  • Email：ruitaofeng@whu.edu.cn
  • Introduction：冯蕊涛，1992年生，女，博士研究生，研究方向为遥感影像几何处理与应用。E-mail: ruitaofeng@whu.edu.cn
FENG Ruitao1,  
  • Affiliation：

    School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China

    Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China

    Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China

DU Qingyun123,  
  • Affiliation：

    Geomatics Center of Guangxi, Nanning 530023, China

LUO Heng5,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China

    Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China

    Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China

  • Email：shenhf@whu.edu.cn
  • Introduction：沈焕锋，1980年生，男，教授，研究方向为遥感信息处理与地学应用。E-mail: shenhf@whu.edu.cn
SHEN Huanfeng123*,  
  • Affiliation：

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

LI Xinghua4,  
  • Affiliation：

    Geomatics Center of Guangxi, Nanning 530023, China

LIU Bo5

resumen

Image registration is a process of geometric alignment of two or more images acquired at different time, different sensors or under different conditions (weather, illumination, camera position and angle, etc.). Remote sensing image registration is an important prerequisite for subsequent processing, such as image fusion, image stitching, long time sequence analysis etc., and it is one of spotlights in the field of remote sensing information processing. High-precision registration of multi-temporal remote sensing images covering complex-terrain region is always a problem to break through. The conventional registration algorithms guiding by the transformation model, is enable to take the pixel-level geometric distortion into consideration, which means that the displacements of a pair of corresponding pixels is different from that of the other pair.Under this circumstance, the global or local mapping function could not describe the geometric deformation between two images covering the complex-terrain region. Optical flow estimation calculates per-pixel displacements considering the very local distortions, even the pixel-level deformation in the computer vision field, providing a feasible and creative solution. It estimates displacement in x- and y-directions for a pair of corresponding pixels under the intensity and gradient consistency constraints, with resistant to the change of illumination. However, it is sensitive to land cover changes, which often lead to abnormal optical flow field and further affect the registered image after the coordinate transformation and resampling. To this end, a registration algorithm based on the optical flow modification for multi-temporal remote sensing images covering the complex-terrain region is proposed. On the preliminary optical flow field, Laplace of Gaussian operator is employed to detect the abnormal optical flow in Munsell color system. With the mask of abnormal optical flow based on the detection results, the Delaunay triangle curved surface interpolation is utilized to correct them, which is calculated by the around accurate pixel displacements. The coordinates in the sensed image are transformed, and the new pixel value is put on the corresponding pixel with the specified resampling method. Ultimately, the aligned image is generated. Experiments based on multi-temporal remote sensing images covering the complex-terrain region with land cover changes demonstrate that the proposed method achieves high-fidelity and high-precision registration compared with the results of the conventional methods.Nevertheless, for registration of the image with sub-meter spatial resolution or image registration of different sensors, the difference of imaging angle, imaging mechanism, noise type etc. have an impact on the accuracy of the proposed algorithm. These remote sensing images are important data guarantee for fine research of earth surface and disaster assessment under poor imaging conditions in disaster region. How to realize high fidelity and high efficiency registration of the ultra-high resolution image or the multi-model image, is a problem that needs an in-depth study for us. In our future work, based on the proposed algorithm in this paper, research will be carried out specifically for the aforementioned problem. The aligned complex topographic region images will be applied to disaster monitoring, assessment, land use change analysis and other fields for an assessment to further improve our proposed method.

palabra clave

remote sensing;complex-terrain region;optical flow algorithm;Laplace of Gaussian;registration;Delaunay triangle curved surface interpolation

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