Requirement-driven remote sensing metadata planning and online acquisition method for large-scale heterogeneous data

Remote sensing data acquisition is one of the most essential processes in the field of Earth observation. However, traditional methods to acquire data do not satisfy the requirements of current applications because large-scale data processing is required. To address this issue, this paper proposes a...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Shuang Wang, Guoqing Li, Wenyang Yu, Yue Ma
Formato: article
Lenguaje:EN
Publicado: Taylor & Francis Group 2021
Materias:
Acceso en línea:https://doaj.org/article/2bcb382bb6784eaab579eca35fa6c037
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario:Remote sensing data acquisition is one of the most essential processes in the field of Earth observation. However, traditional methods to acquire data do not satisfy the requirements of current applications because large-scale data processing is required. To address this issue, this paper proposes a data acquisition framework that carries out remote sensing metadata planning and then realizes the online acquisition of large amounts of data. Firstly, this paper establishes a unified metadata cataloging model and realizes the catalog of metadata in a local database. Secondly, a coverage calculation model is presented, which can show users the data coverage information in a selected geographical region under the data requirements of a specific application. Finally, according to the data retrieval results and the coverage calculation, a machine-to-machine interface is provided to acquire target remote sensing data. Experiments were conducted to verify the availability and practicality of the proposed framework, and the results show the strengths and powerful capabilities of our framework by overcoming deficiencies in traditional methods. It also achieved the online automatic acquisition of large-scale heterogeneous remote sensing data, which can provide guidance for remote sensing data acquisition strategies.