Journal ArticleDOI
Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data
TLDR
A multilevel DL architecture that targets land cover and crop type classification from multitemporal multisource satellite imagery outperforms the one with MLPs allowing us to better discriminate certain summer crop types.Abstract:
Deep learning (DL) is a powerful state-of-the-art technique for image processing including remote sensing (RS) images. This letter describes a multilevel DL architecture that targets land cover and crop type classification from multitemporal multisource satellite imagery. The pillars of the architecture are unsupervised neural network (NN) that is used for optical imagery segmentation and missing data restoration due to clouds and shadows, and an ensemble of supervised NNs. As basic supervised NN architecture, we use a traditional fully connected multilayer perceptron (MLP) and the most commonly used approach in RS community random forest, and compare them with convolutional NNs (CNNs). Experiments are carried out for the joint experiment of crop assessment and monitoring test site in Ukraine for classification of crops in a heterogeneous environment using nineteen multitemporal scenes acquired by Landsat-8 and Sentinel-1A RS satellites. The architecture with an ensemble of CNNs outperforms the one with MLPs allowing us to better discriminate certain summer crop types, in particular maize and soybeans, and yielding the target accuracies more than 85% for all major crops (wheat, maize, sunflower, soybeans, and sugar beet).read more
Citations
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Journal ArticleDOI
Deep learning in agriculture: A survey
TL;DR: A survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges indicates that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.
Journal ArticleDOI
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Xiao Xiang Zhu,Devis Tuia,Lichao Mou,Gui-Song Xia,Liangpei Zhang,Feng Xu,Friedrich Fraundorfer +6 more
TL;DR: The challenges of using deep learning for remote-sensing data analysis are analyzed, recent advances are reviewed, and resources are provided that hope will make deep learning in remote sensing seem ridiculously simple.
Journal ArticleDOI
Deep learning in remote sensing applications: A meta-analysis and review
TL;DR: 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.
Journal ArticleDOI
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
TL;DR: In this article, the authors provide a thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain.
Proceedings ArticleDOI
DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images
Ilke Demir,Krzysztof Koperski,David Lindenbaum,Guan Pang,Jing Huang,Saikat Basu,Forest Hughes,Devis Tuia,Ramesh Raska +8 more
TL;DR: The DeepGlobe 2018 Satellite Image Understanding Challenge is presented, which includes three public competitions for segmentation, detection, and classification tasks on satellite images, and characteristics of each dataset are analyzed, and evaluation criteria for each task are defined.
References
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Posted Content
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martín Abadi,Ashish Agarwal,Paul Barham,Eugene Brevdo,Zhifeng Chen,Craig Citro,Greg S. Corrado,Andy Davis,Jeffrey Dean,Matthieu Devin,Sanjay Ghemawat,Ian Goodfellow,Andrew Harp,Geoffrey Irving,Michael Isard,Yangqing Jia,Rafal Jozefowicz,Lukasz Kaiser,Manjunath Kudlur,Josh Levenberg,Dan Mané,Rajat Monga,Sherry Moore,Derek G. Murray,Chris Olah,Mike Schuster,Jonathon Shlens,Benoit Steiner,Ilya Sutskever,Kunal Talwar,Paul A. Tucker,Vincent Vanhoucke,Vijay K. Vasudevan,Fernanda B. Viégas,Oriol Vinyals,Pete Warden,Martin Wattenberg,Martin Wicke,Yuan Yu,Xiaoqiang Zheng +39 more
TL;DR: The TensorFlow interface and an implementation of that interface that is built at Google are described, which has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields.
Journal ArticleDOI
Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services
Matthias Drusch,U. Del Bello,S. Carlier,O. Colin,V. Fernandez,F. Gascon,Bianca Hoersch,Claudia Isola,P. Laberinti,P. Martimort,Aime Meygret,Francois Spoto,O. Sy,Franco Marchese,Pier Bargellini +14 more
TL;DR: An overview of the GMES Sentinel-2 mission including a technical system concept overview, image quality, Level 1 data processing and operational applications is provided.
Journal ArticleDOI
Deep Learning-Based Classification of Hyperspectral Data
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