Saliency Detection and Deep Learning-Based Wildfire Identification in UAV Imagery

Sensors
Yi ZhaoJie Zhang

Abstract

An unmanned aerial vehicle (UAV) equipped with global positioning systems (GPS) can provide direct georeferenced imagery, mapping an area with high resolution. So far, the major difficulty in wildfire image classification is the lack of unified identification marks, the fire features of color, shape, texture (smoke, flame, or both) and background can vary significantly from one scene to another. Deep learning (e.g., DCNN for Deep Convolutional Neural Network) is very effective in high-level feature learning, however, a substantial amount of training images dataset is obligatory in optimizing its weights value and coefficients. In this work, we proposed a new saliency detection algorithm for fast location and segmentation of core fire area in aerial images. As the proposed method can effectively avoid feature loss caused by direct resizing; it is used in data augmentation and formation of a standard fire image dataset 'UAV_Fire'. A 15-layered self-learning DCNN architecture named 'Fire_Net' is then presented as a self-learning fire feature exactor and classifier. We evaluated different architectures and several key parameters (drop out ratio, batch size, etc.) of the DCNN model regarding its validation accuracy. The proposed arc...Continue Reading

References

May 29, 2015·Nature·Yann LeCunGeoffrey Hinton
Sep 10, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Kaiming HeJian Sun
Jun 14, 2016·IEEE Transactions on Pattern Analysis and Machine Intelligence·Shaoqing RenJian Sun
Mar 2, 2017·Sensors·Carlos A Trasviña-MorenoArmando Trasviña-Castro

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Software Mentioned

Fire
Alexnet
Caffe
Net
Envisat

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