Geometry-Based Global Alignment for GSMS Remote Sensing Images

Dan Zeng,Zhijiang Zhang,Shuying Li,Shiming Ge,Rui Fang

doi:10.3390/rs9060587

Abstract

Alignment of latitude and longitude for all pixels is important for geo-stationary meteorological satellite (GSMS) images. To align landmarks and non-landmarks in the GSMS images, we propose a geometry-based global alignment method. Firstly, the Global Self-consistent, Hierarchical, High-resolution Geography (GSHHG) database and GSMS images are expressed as feature maps by geometric coding. According to the geometric and gradient similarity of feature maps, initial feature matching is obtained. Then, neighborhood spatial consistency based local geometric refinement algorithm is utilized to remove outliers. Since the earth is not a standard sphere, polynomial fitting models are used to describe the global relationship between latitude, longitude and coordinates for all pixels in the GSMS images. Finally, with registered landmarks and polynomial fitting models, the latitude and longitude of each pixel in the GSMS images can be calculated. Experimental results show that the proposed method globally align the GSMS images with high accuracy, recall and significantly low computation complexity.

Highlights

In many applications, such as weather forecast, environmental monitoring and so on, determining the latitude and longitude of each pixel in the geo-stationary meteorological satellite (GSMS) images is of great importance
The shorelines of the GSHHG database correspond to the edges of the GSMS images [31], which means that shorelines can be used to simplify alignment of GSHHG and GSMS images
The remote sensing images used in this experiment are from the FengyunII D meteorological satellite whose sub-satellite point is near (86◦ E, 0◦ N)

Summary

Introduction

In many applications, such as weather forecast, environmental monitoring and so on, determining the latitude and longitude of each pixel in the GSMS images is of great importance. The GSMS images have the characteristics of round-the-clock, all-weather, long range and high-resolution, which bring new challenges to practical applications. Remote sensing images matching algorithms are usually divided into two categories: area-based methods and feature-based methods [1,2]. Area-based matching algorithm establishes correspondence between two images by similarity measurements based on correlation functions. A rough-location method [5] was proposed to locate the remote image with specific physiognomy. By matching the remote sensing image and the digital map, researchers can roughly locate the remote images and the location error is less than 10 km. The GSMS images are generally polluted by illumination, scale variation, cloud influence and other factors, and those algorithms do not work well

Methods

Results

Conclusion