A Convolutional Neural Network Approach for Assisting Avalanche Search and Rescue Operations with UAV Imagery

Bejiga, Mesay Belete; Zeggada, Abdallah; Nouffidj, Abdelhamid; Melgani, Farid

doi:10.3390/rs9020100

Cited by 198 publications

(106 citation statements)

References 33 publications

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“…Deep learning has been extensively used in the literature for a range of different applications such as vehicle detection [45,46], investigated avalanche search and rescue operations with Unmanned Areal Vehicles (UAV), change detection [47,48]. In this scheme, high level features are learned from low level ones where the features derived can be formulated for pattern recognition classification [49].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

et al. 2017

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Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1) The mixing process should occur at macroscopic level and (2) Photons must interact with single material before reaching the sensor. However, these assumptions do not always hold and more complex nonlinear models are required. This study proposes a new hybrid method for switching between linear and nonlinear spectral unmixing of hyperspectral data based on artificial neural networks. The neural networks was trained with parameters within a window of the pixel under consideration. These parameters are computed to represent the diversity of the neighboring pixels and are based on the Spectral Angular Distance, Covariance and a non linearity parameter. The endmembers were extracted using Vertex Component Analysis while the abundances were estimated using the method identified by the neural networks (Vertex Component Analysis, Fully Constraint Least Square Method, Polynomial Post Nonlinear Mixing Model or Generalized Bilinear Model). Results show that the hybrid method performs better than each of the individual techniques with high overall accuracy, while the abundance estimation error is significantly lower than that obtained using the individual methods. Experiments on both synthetic dataset and real hyperspectral images demonstrated that the proposed hybrid switch method is efficient for solving spectral unmixing of hyperspectral images as compared to individual algorithms.

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Section: Introductionmentioning

confidence: 99%

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

et al. 2017

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“…A quick response to localize bodies after shipwrecks is crucial to save lives, and both unmanned aerial vehicles (UAVs) and remotely piloted aircraft (RPA) offer an important advantage when compared to satellite monitoring for this task, as they are able to monitor specific areas by means of trajectory planning in real time. This is a relevant feature in emergencies (Erdelj, Natalizio, Chowdhury, & Akyildiz, ; Voyles & Choset, ; Zheng, Hu, & Xu, ), control tasks of people on a border area (Minaeian, Liu, & Son, ), and disasters of all kinds, like assisting avalanche search and rescue operations (Bejiga, Zeggada, Nouffidj, & Melgani, ; Silvagni, Tonoli, Zenerino, & Chiaberge, ), monitoring after earthquakes (Lei et al, ), rescue in wilderness (Goodrich, Morse, Engh, Cooper, & Adams, ), and sea robot‐assisted inspection (Lindemuth et al, ), among others.…”

Section: Introductionmentioning

confidence: 99%

Detection of bodies in maritime rescue operations using unmanned aerial vehicles with multispectral cameras

Gallego

Pertusa

Gil

et al. 2018

Journal of Field Robotics

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In this study, we use unmanned aerial vehicles equipped with multispectral cameras to search for bodies in maritime rescue operations. A series of flights were performed in open‐water scenarios in the northwest of Spain, using a certified aquatic rescue dummy in dangerous areas and real people when the weather conditions allowed it. The multispectral images were aligned and used to train a convolutional neural network for body detection. An exhaustive evaluation was performed to assess the best combination of spectral channels for this task. Three approaches based on a MobileNet topology were evaluated, using (a) the full image, (b) a sliding window, and (c) a precise localization method. The first method classifies an input image as containing a body or not, the second uses a sliding window to yield a class for each subimage, and the third uses transposed convolutions returning a binary output in which the body pixels are marked. In all cases, the MobileNet architecture was modified by adding custom layers and preprocessing the input to align the multispectral camera channels. Evaluation shows that the proposed methods yield reliable results, obtaining the best classification performance when combining green, red‐edge, and near‐infrared channels. We conclude that the precise localization approach is the most suitable method, obtaining a similar accuracy as the sliding window but achieving a spatial localization close to 1 m. The presented system is about to be implemented for real maritime rescue operations carried out by Babcock Mission Critical Services Spain.

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“…In [72,73], the use of pretrained CNN models for feature extraction is worth noting again. In both cases, the well-known Inception model [74] was used.…”

Section: With Image Sensorsmentioning

confidence: 99%

“…Deep learning techniques applied on images taken from UAVs have also gained a lot of importance in monitoring and search and rescue applications, such as jellyfish monitoring [70], road traffic monitoring from UAVs [71], assisting avalanche search and rescue operations with UAV imagery [72], and terrorist identification [73]. In [72,73], the use of pretrained CNN models for feature extraction is worth noting again.…”

Section: With Image Sensorsmentioning

confidence: 99%

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A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles

et al. 2017

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Deep learning is recently showing outstanding results for solving a wide variety of robotic tasks in the areas of perception, planning, localization, and control. Its excellent capabilities for learning representations from the complex data acquired in real environments make it extremely suitable for many kinds of autonomous robotic applications. In parallel, Unmanned Aerial Vehicles (UAVs) are currently being extensively applied for several types of civilian tasks in applications going from security, surveillance, and disaster rescue to parcel delivery or warehouse management. In this paper, a thorough review has been performed on recent reported uses and applications of deep learning for UAVs, including the most relevant developments as well as their performances and limitations. In addition, a detailed explanation of the main deep learning techniques is provided. We conclude with a description of the main challenges for the application of deep learning for UAV-based solutions.

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A Convolutional Neural Network Approach for Assisting Avalanche Search and Rescue Operations with UAV Imagery

Cited by 198 publications

References 33 publications

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

Detection of bodies in maritime rescue operations using unmanned aerial vehicles with multispectral cameras

A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles

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