Hyperspectral imaging is beneficial for non-destructive agricultural inspections, and three-dimensional reconstruction modeling is a powerful tool for inspecting the phenotype of plants. This study proposes an approach to combine threedimensional reconstruction modeling and hyperspectral images into four-dimensional data. This data not only contains the three-dimensional structural information of an interesting object but also includes the spectral information of every point on the surface of this object. Firstly, the hyperspectral and visible images of an interesting object are acquired from hyperspectral and visible cameras. Secondly, high-resolution visible images are used to reconstruct a three-dimensional surface model of an interesting object. Thirdly, matching hyperspectral images with visible images establishes the correspondence between hyperspectral images and the three-dimensional model. Furthermore, the biomarker index can be derived from hyperspectral data. The biomarker index can be transformed into surface textures and combined with the three-dimensional model to form a three-dimensional biomarker model.
Image geo-localization estimates an image's global position by comparing it with a large-scale image database containing known positions. This localization technology can serve as an alternative positioning method for unmanned aerial vehicles (UAV) in situations where a global position system is unavailable. Feature-based image-matching methods typically involve descriptors constructed from pixel-level key points in the images. The number of descriptors in one image can be substantial. Filtering and comparing these large quantities of descriptors for image matching would be quite time-consuming. Due to the large scale of satellite images, matching them with aerial images using this method can be challenging to achieve in real-time. Thus, this paper proposes a semantic matching-based approach for real-time image geo-localization. The types, quantities, and geometric information of objects in satellite images are extracted and used as sematic-level descriptors. The sematic-level descriptors of an aerial image captured by UAV are extracted by an object recognition model. The quantity of semantic-level descriptors is orders of magnitude less than pixel-level descriptors. The location of the aerial image can be rapidly determined by matching the semantic-level descriptors between the aerial image and satellite images. In the experiments, the speeds of matching an aerial image with satellite images using the semantic matching and a feature-based matching method were 0.194 seconds per image and 125.68 seconds per image, respectively. Using semantic matching methods is 648 times faster than using feature matching methods. The results demonstrate that the proposed semantic matching methods have the potential for real-time image geo-localization.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.