A Review on Deep Learning in UAV Remote Sensing
作者： Lúcio André de Castro Jorge , Jonathan Li , José Marcato Junior , Jonathan de Andrade Silva , Edson Takashi Matsubara
DOI:
关键词:
摘要: 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, many others. In the remote sensing field, surveys literature revisions specifically involving DNNs algorithms' applications have been conducted in attempt to summarize amount of information produced its subfields. Recently, Unmanned Aerial Vehicles (UAV) based dominated aerial research. However, a revision that combines both "deep learning" "UAV sensing" thematics has not yet conducted. The motivation our work was present comprehensive review fundamentals Learning (DL) applied UAV-based imagery. We focused mainly on describing classification regression techniques used recent UAV-acquired data. For that, total 232 papers published international scientific journal databases examined. gathered material evaluated their characteristics regarding application, sensor, technique used. relate how DL presents promising results potential tasks associated image Lastly, we project future perspectives, commentating prominent paths be explored UAV field. Our consists friendly-approach introduce, commentate, state-of-the-art algorithms diverse subfields sensing, grouping it environmental, urban, agricultural contexts.
arxiv.org
harvard.edu
arxiv-vanity.com参考文章(186)
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, Distilling the Knowledge in a Neural Network arXiv: Machine Learning. ,(2015)
Ilya Sutskever, Geoffrey E. Hinton, Alex Krizhevsky, Ruslan R. Salakhutdinov, Nitish Srivastava, Improving neural networks by preventing co-adaptation of feature detectors arXiv: Neural and Evolutionary Computing. ,(2012)
Otavio A. B. Penatti, Keiller Nogueira, Jefersson A. dos Santos, Do deep features generalize from everyday objects to remote sensing and aerial scenes domains computer vision and pattern recognition. pp. 44- 51 ,(2015) , 10.1109/CVPRW.2015.7301382
Qin Zou, Lihao Ni, Tong Zhang, Qian Wang, Deep Learning Based Feature Selection for Remote Sensing Scene Classification IEEE Geoscience and Remote Sensing Letters. ,vol. 12, pp. 2321- 2325 ,(2015) , 10.1109/LGRS.2015.2475299
Giorgio Licciardi, Prashanth Reddy Marpu, Jocelyn Chanussot, Jon Atli Benediktsson, Linear Versus Nonlinear PCA for the Classification of Hyperspectral Data Based on the Extended Morphological Profiles IEEE Geoscience and Remote Sensing Letters. ,vol. 9, pp. 447- 451 ,(2012) , 10.1109/LGRS.2011.2172185
Wolfgang Balzer, Masanobu Takahashi, Jun Ohta, Kazuo Kyuma, Weight quantization in Boltzmann machines Neural Networks. ,vol. 4, pp. 405- 409 ,(1991) , 10.1016/0893-6080(91)90077-I
Emile Fiesler, Amar Choudry, H. John Caulfield, Weight discretization paradigm for optical neural networks Optical Interconnections and Networks. ,vol. 1281, pp. 164- 173 ,(1990) , 10.1117/12.20700
Sepp Hochreiter, Jürgen Schmidhuber, Long short-term memory Neural Computation. ,vol. 9, pp. 1735- 1780 ,(1997) , 10.1162/NECO.1997.9.8.1735
Guofeng Sheng, Wen Yang, Tao Xu, Hong Sun, High-resolution satellite scene classification using a sparse coding based multiple feature combination International Journal of Remote Sensing. ,vol. 33, pp. 2395- 2412 ,(2012) , 10.1080/01431161.2011.608740
Jürgen Schmidhuber, Deep learning in neural networks Neural Networks. ,vol. 61, pp. 85- 117 ,(2015) , 10.1016/J.NEUNET.2014.09.003