Hyperspectral sensors mounted in unmanned aerial vehicles offer new opportunities to explore high-resolution multitemporal spectral analysis in remote sensing applications. Nevertheless, the use of hyperspectral data still poses challenges mainly in postprocessing to correct from high geometric deformation of images. In general, the acquisition of high-quality hyperspectral imagery is achieved through a time-consuming and complex processing workflow. However, this effort is mandatory when using hyperspectral imagery in a multisensor data fusion perspective, such as with thermal infrared imagery or photogrammetric point clouds. Push-broom hyperspectral sensors provide high spectral resolution data, but its scanning acquisition architecture imposes more challenges to create geometrically accurate mosaics from multiple hyperspectral swaths. In this article, an efficient method is presented to correct geometrical distortions on hyperspectral swaths from push-broom sensors by aligning them with an RGB photogrammetric orthophoto mosaic. The proposed method is based on an iterative approach to align hyperspectral swaths with an RGB photogrammetric orthophoto mosaic. Using as input preprocessed hyperspectral swaths, apart from the need of introducing some control points, the workflow is fully automatic and consists of: adaptive swath subdivision into multiple fragments; detection of significant image features; estimation of valid matches between individual swaths and the RGB orthophoto mosaic; and calculation of the best geometric transformation model to the retrieved matches. As a result, geometrical distortions of hyperspectral swaths are corrected and an orthomosaic is generated. This methodology provides an expedite solution able to produce a hyperspectral mosaic with an accuracy ranging from two to five times the ground sampling distance of the high-resolution RGB orthophoto mosaic, enabling the hyperspectral data integration with data from other sensors for multiple applications.

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使用推扫式传感器生成基于无人机的高光谱马赛克的有效方法

安装在无人机上的高光谱传感器为探索遥感应用中的高分辨率多时态光谱分析提供了新的机会。尽管如此，高光谱数据的使用仍然主要在后处理中提出挑战，以纠正图像的高几何变形。通常，高质量高光谱图像的获取是通过耗时且复杂的处理工作流程来实现的。然而，在多传感器数据融合视角中使用高光谱图像时，这项工作是强制性的，例如使用热红外图像或摄影测量点云。推扫式高光谱传感器提供高光谱分辨率数据，但其扫描采集架构对从多个高光谱带创建几何精确的马赛克提出了更多挑战。在本文中，提出了一种有效的方法，通过将推扫式传感器的高光谱带与 RGB 摄影测量正射影像马赛克对齐来校正几何失真。所提出的方法基于将高光谱条带与 RGB 摄影测量正射影像马赛克对齐的迭代方法。使用预处理的高光谱条带作为输入，除了需要引入一些控制点外，工作流程是全自动的，包括：自适应条带细分为多个片段；检测重要的图像特征；估计单个条带和 RGB 正射影像马赛克之间的有效匹配；以及对检索到的匹配项的最佳几何变换模型的计算。结果，高光谱条带的几何失真得到纠正并生成正射马赛克。