A Robust UAV Hyperspectral Image Stitching Method Based on Deep Feature Matching

Unmanned aerial vehicle (UAV) hyperspectral imaging has been extensively applied in various fields. However, due to the limited imaging width, hyperspectral images (HSIs) captured by UAV need to be stitched, so as to effectively cover the study area. In this article, an effective seamless stitching method with deep feature matching and elastic warp is proposed for HSIs, which consists of the following major steps. First, for each input HSI, a single-band gray-scale image is obtained by fusing the bands corresponding to the red, green, and blue wavelengths. Second, the feature points of each HSI are obtained with a robust VGG-style network and matched with a graph neural network. After point pairs are obtained, the next step is to estimate the transformation matrix of adjacent images, and a spectral correction method based on intrinsic decomposition is proposed to ensure the spectral consistency of adjacent images. In the final stage, a seam-cutting and multiscale blending strategy is adopted to ensure the spatial consistency of the stitching results. Experimental results on real HSIs show that the proposed method is superior to six representative image stitching approaches.

I. Introduction

Hyperspectral image (HSI) is a multiband image with a large number of bands that record the reflection characteristics of objects under different wavelengths [1], [2]. As a result, each type of object in HSIs has its unique spectral reflection curve. By taking this advantage, HSIs have been successfully applied to various applications, such as target detection [3], [4] and land-cover mapping [5], [6]. Hyperspectral remote sensing images are typically obtained by satellites equipped with hyperspectral imagers. However, the long revisit period and high altitude make it difficult for satellites to obtain high-quality multitemporal hyperspectral data, which severely limits its applications.