Unmanned Aerial Vehicle for Remote Sensing Applications - A Review
By H. Yao, Rongjun Qin, Xiaoyu Chen. Remote Sensing, 2019
598 citations recorded by the source corpus.
The unmanned aerial vehicle (UAV) sensors and platforms nowadays are being used in almost every application (e.g., agriculture, forestry, and mining) that needs observed information from the top or oblique views. While they intend to be a general remote sensing (RS) tool, the relevant RS data processing and analysis methods are still largely ad-hoc to applications. Although the obvious advantages of UAV data are their high spatial resolution and flexibility in acquisition and sensor integration, there is in general a lack of systematic analysis on how these characteristics alter solutions for typical RS tasks such as land-cover classification, change detection, and thematic mapping. For instance, the ultra-high-resolution data (less than 10 cm of Ground Sampling Distance (GSD)) bring more unwanted classes of objects (e.g., pedestrian and cars) in land-cover classification; the often available 3D data generated from photogrammetric images call for more advanced techniques for geometric and spectral analysis. In this paper, we perform a critical review on RS tasks that involve UAV data and their derived products as their main sources including raw perspective images, digital surface models, and orthophotos. In particular, we focus on solutions that address the “new” aspects of the UAV data including (1) ultra-high resolution; (2) availability of coherent geometric and spectral data; and (3) capability of simultaneously using multi-sensor data for fusion. Based on these solutions, we provide a brief summary of existing examples of UAV-based RS in agricultural, environmental, urban, and hazards assessment applications, etc., and by discussing their practical potentials, we share our views in their future research directions and draw conclusive remarks.
Subfields: Computer Vision and Pattern Recognition, Environmental Engineering, Media Technology
View Paper: https://www.semanticscholar.org/paper/fd24709c111c64199b5a3cc55c2d9a99e776057d
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Citations in the corpus
- Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A survey (2021, methodology): “For UAV data, several representative surveys have been published in the literature, including those on UAV image processing and application [36], [37] as well as the UAV system [38].”
- CMTFNet: CNN and Multiscale Transformer Fusion Network for Remote-Sensing Image Semantic Segmentation (2023, background): “It provides support for a variety of remote-sensing image application scenarios, including urban planning [1], [2], [3], land cover classification [4], [5], [6], land planning [7], and road extraction [8].”