Remote sensing image fusion on 3D scenarios: A review of applications for agriculture and forestry
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2022-06-20Referencia bibliográfica
Juan M. Jurado... [et al.]. Remote sensing image fusion on 3D scenarios: A review of applications for agriculture and forestry, International Journal of Applied Earth Observation and Geoinformation, Volume 112, 2022, 102856, ISSN 1569-8432, [https://doi.org/10.1016/j.jag.2022.102856]
Patrocinador
European Commission 1381202-GEU PYC20-RE-005-UJA IEG-2021; Junta de Andalucia 1381202-GEU PYC20-RE-005-UJA IEG-2021; Instituto de Estudios Gienneses; European Commission; Spanish Government UIDB/04033/2020; DATI-Digital Agriculture Technologies; Portuguese Foundation for Science and Technology 1381202-GEU FPU19/00100Resumen
Three-dimensional (3D) image mapping of real-world scenarios has a great potential to provide the user with a
more accurate scene understanding. This will enable, among others, unsupervised automatic sampling of
meaningful material classes from the target area for adaptive semi-supervised deep learning techniques. This
path is already being taken by the recent and fast-developing research in computational fields, however, some
issues related to computationally expensive processes in the integration of multi-source sensing data remain.
Recent studies focused on Earth observation and characterization are enhanced by the proliferation of Unmanned
Aerial Vehicles (UAV) and sensors able to capture massive datasets with a high spatial resolution. In this scope,
many approaches have been presented for 3D modeling, remote sensing, image processing and mapping, and
multi-source data fusion. This survey aims to present a summary of previous work according to the most relevant
contributions for the reconstruction and analysis of 3D models of real scenarios using multispectral, thermal and
hyperspectral imagery. Surveyed applications are focused on agriculture and forestry since these fields
concentrate most applications and are widely studied. Many challenges are currently being overcome by recent
methods based on the reconstruction of multi-sensorial 3D scenarios. In parallel, the processing of large image
datasets has recently been accelerated by General-Purpose Graphics Processing Unit (GPGPU) approaches that
are also summarized in this work. Finally, as a conclusion, some open issues and future research directions are
presented.