A Review on Deep Learning in UAV Remote Sensing

@article{Osco2021ARO,
  title={A Review on Deep Learning in UAV Remote Sensing},
  author={Lucas Prado Osco and Jos{\'e} Marcato Junior and Ana Paula Marques Ramos and L{\'u}cio Abdr{\'e} de Castro Jorge and Sarah Narges Fatholahi and Jonathan de Andrade Silva and Edson Takashi Matsubara and Hemerson Pistori and Wesley Nunes Gonçalves and Jonathan Li},
  journal={Int. J. Appl. Earth Obs. Geoinformation},
  year={2021},
  volume={102},
  pages={102456}
}
Deep Neural Networks (DNNs) learn representation from data with an impressive capability, and brought important breakthroughs for processing images, time-series, natural language, audio, video, and many others. In the remote sensing field, surveys and literature revisions specifically involving DNNs algorithms’ applications have been conducted in an attempt to summarize the amount of information produced in its subfields. Recently, Unmanned Aerial Vehicles (UAV) based applications have… Expand

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References

SHOWING 1-10 OF 215 REFERENCES
A review of deep learning methods for semantic segmentation of remote sensing imagery
TLDR
A summary of the fundamental deep neural network architectures and the most recent developments of deep learning methods for semantic segmentation of remote sensing imagery including non-conventional data such as hyperspectral images and point clouds are reviewed. Expand
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
TLDR
This work focuses on theories, tools, and challenges for the RS community, and focuses on unsolved challenges and opportunities as they relate to inadequate data sets, big data, and human-understandable solutions for modeling physical phenomena. Expand
A Study on the Detection of Cattle in UAV Images Using Deep Learning
TLDR
Results revealed that many CNN architectures are robust enough to reliably detect animals in aerial images even under far from ideal conditions, indicating the viability of using UAVs for cattle monitoring. Expand
Deep learning for remote sensing image classification: A survey
TLDR
A systematic review of pixel‐wise and scene‐wise RS image classification approaches that are based on the use of DL and a comparative analysis regarding the performances of typical DL‐based RS methods are provided. Expand
Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement
TLDR
This paper provides a comprehensive review of deep-learning methods for the enhancement of remote sensing observations, focusing on critical tasks including single and multi-band super-resolution, denoising, restoration, pan-sharpening, and fusion, among others. Expand
Deep learning in environmental remote sensing: Achievements and challenges
TLDR
The potential of DL in environmental remote sensing, including land cover mapping, environmental parameter retrieval, data fusion and downscaling, and information reconstruction and prediction, will be analyzed and a typical network structure will be introduced. Expand
Road Extraction from Unmanned Aerial Vehicle Remote Sensing Images Based on Improved Neural Networks
TLDR
Improved neural networks are helpful in reducing the network size and developing the precision needed for road extraction in Unmanned Aerial Vehicle (UAV) remote sensing images. Expand
Deep learning in remote sensing applications: A meta-analysis and review
TLDR
This review covers nearly every application and technology in the field of remote sensing, ranging from preprocessing to mapping, and a conclusion regarding the current state-of-the art methods, a critical conclusion on open challenges, and directions for future research are presented. Expand
Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs
TLDR
Three state-of-the-art object detection methods were evaluated: Faster Region-based Convolutional Neural Network (Faster R-CNN), YOLOv3 and RetinaNet, and delivered average precision around 92% with an associated processing times below 30 miliseconds. Expand
Landscape Classification with Deep Neural Networks
TLDR
This work presents an efficient approach to train/apply DCNNs with/on sets of photographic images, using a powerful graphical method called a conditional random field (CRF) to generate DCNN training and testing data using minimal manual supervision, and synthesizes the findings to examine the general effectiveness of transfer learning to landscape-scale image classification. Expand
...
1
2
3
4
5
...