Seamless Mosaicking of UAV-Based Push-Broom Hyperspectral Images for Environment Monitoring

Lina Yi,Xiao Xu,Guifeng Zhang,Wenji Guo,Jing M Chen,Xing Ming

doi:10.3390/rs13224720

Abstract

This paper proposes a systematic image mosaicking methodology to produce hyperspectral image for environment monitoring using an emerging UAV-based push-broom hyperspectral imager. The suitability of alternative methods in each step is assessed by experiments of an urban scape, a river course and a forest study area. First, the hyperspectral image strips were acquired by sequentially stitching the UAV images acquired by push-broom scanning along each flight line. Next, direct geo-referencing was applied to each image strip to get initial geo-rectified result. Then, with ground control points, the curved surface spline function was used to transform the initial geo-rectified image strips to improve their geometrical accuracy. To further remove the displacement between pairs of image strips, an improved phase correlation (IPC) and a SIFT and RANSAC-based method (SR) were used in image registration. Finally, the weighted average and the best stitching image fusion method were used to remove the spectral differences between image strips and get the seamless mosaic. Experiment results showed that as the GCPs‘ number increases, the mosaicked image‘s geometrical accuracy increases. In image registration, there exists obvious edge information that can be accurately extracted from the urban scape and river course area; comparative results can be achieved by the IPC method with less time cost. However, for the ground objects with complex texture like forest, the edges extracted from the image is prone to be inaccurate and result in the failure of the IPC method, and only the SR method can get a good result. In image fusion, the best stitching fusion method can get seamless results for all three study areas. Whereas, the weighted average fusion method was only useful in eliminating the stitching line for the river course and forest areas but failed for the urban scape area due to the spectral heterogeneity of different ground objects. For different environment monitoring applications, the proposed methodology provides a practical solution to seamlessly mosaic UAV-based push-broom hyperspectral images with high geometrical accuracy and spectral fidelity.

Highlights

Hyperspectral images have hundreds of narrow and nearly continuous spectral bands that represent the reflectance signals of the observed objects [1]
Because few distinct ground control points (GCPs) can be extracted in the reference image in the river course area, the river course area is chosen as the representative study area to show the results of the different geometric rectification methods with or without
Because a large portion of the river course area is dominated by water, the ground control points were derived from google earth (GE) images rather than from field surveys in the research

Summary

Introduction

Hyperspectral images have hundreds of narrow and nearly continuous spectral bands that represent the reflectance signals of the observed objects [1]. Fine spatial and spectral resolution creates problems with the signal-to-noise ratio of the sensor used. The number of spectral bands had to be reduced in order to get smaller pixel size as reading the data from the sensor takes some time and using many spectral bands would have resulted in pixels elongated towards the flight direction because plane speed cannot be reduced below a certain critical limit [2]. Recent developments in the miniaturization of electro-optical sensors and unmanned aerial vehicles (UAVs) have established a new era of hyperspectral imaging [3]. Due to the high flexibility of UAV flight planning, hyperspectral images taken by UAVs can be widely used in environment monitoring applications [4,5,6,7,8,9,10,11], such as precision agriculture, species classification [11,12,13], surface parameter retrieval [14,15], etc

Methods

Results

Discussion

Conclusion