图像拼接
高光谱成像
人工智能
计算机科学
计算机视觉
特征提取
特征(语言学)
模式识别(心理学)
遥感
地质学
哲学
语言学
作者
Yan Mo,Xudong Kang,Puhong Duan,Shutao Li
标识
DOI:10.1109/tgrs.2021.3123980
摘要
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.
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