A Framework for Remote Sensing Images Processing Using Deep Learning Techniques

Deep learning (DL) techniques are becoming increasingly important to solve a number of image processing tasks. Among common algorithms, convolutional neural network- and recurrent neural network-based systems achieve state-of-the-art results on satellite and aerial imagery in many applications. While these approaches are subject to scientific interest, there is currently no operational and generic implementation available at the user level for the remote sensing (RS) community. In this letter, we present a framework enabling the use of DL techniques with RS images and geospatial data. Our solution takes roots in two extensively used open-source libraries, the RS image processing library Orfeo ToolBox and the high-performance numerical computation library TensorFlow. It can apply deep nets without restriction on image size and is computationally efficient, regardless of hardware configuration.

[1]  Uwe Stilla,et al.  Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks , 2016, IEEE Geoscience and Remote Sensing Letters.

[2]  Junwei Han,et al.  A Survey on Object Detection in Optical Remote Sensing Images , 2016, ArXiv.

[3]  Jefersson Alex dos Santos,et al.  Towards better exploiting convolutional neural networks for remote sensing scene classification , 2016, Pattern Recognit..

[4]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[5]  Davide Cozzolino,et al.  Pansharpening by Convolutional Neural Networks , 2016, Remote. Sens..

[6]  Paolo Napoletano,et al.  Visual descriptors for content-based retrieval of remote-sensing images , 2016, ArXiv.

[7]  Jamie Sherrah,et al.  Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery , 2016, ArXiv.

[8]  David Morin,et al.  Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series , 2017, Remote. Sens..

[9]  Rémi Cresson,et al.  A Generic Framework for the Development of Geospatial Processing Pipelines on Clusters , 2016, IEEE Geoscience and Remote Sensing Letters.

[10]  Gabriele Moser,et al.  Multimodal Classification of Remote Sensing Images: A Review and Future Directions , 2015, Proceedings of the IEEE.

[11]  Gang Wang,et al.  Deep Learning-Based Classification of Hyperspectral Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[12]  Haipeng Wang,et al.  SAR target recognition based on deep learning , 2014, 2014 International Conference on Data Science and Advanced Analytics (DSAA).

[13]  Ruggero G. Pensa,et al.  M3Fusion: A Deep Learning Architecture for Multi-{Scale/Modal/Temporal} satellite data fusion , 2018, ArXiv.

[14]  Dino Ienco,et al.  Land Cover Classification via Multitemporal Spatial Data by Deep Recurrent Neural Networks , 2017, IEEE Geoscience and Remote Sensing Letters.

[15]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[16]  Gui-Song Xia,et al.  Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery , 2015, Remote. Sens..

[17]  Patrick Weber,et al.  OpenStreetMap: User-Generated Street Maps , 2008, IEEE Pervasive Computing.

[18]  Pierre Alliez,et al.  High-Resolution Aerial Image Labeling With Convolutional Neural Networks , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Michael J Ackerman,et al.  Engineering and algorithm design for an image processing Api: a technical report on ITK--the Insight Toolkit. , 2002, Studies in health technology and informatics.

[20]  Wei Li,et al.  Transferred Deep Learning for Anomaly Detection in Hyperspectral Imagery , 2017, IEEE Geoscience and Remote Sensing Letters.

[21]  Advances in Very-High-Resolution Remote Sensing , .

[22]  Pierre Alliez,et al.  Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Julien Michel,et al.  Orfeo ToolBox: open source processing of remote sensing images , 2017, Open Geospatial Data, Software and Standards.

[24]  Dino Ienco,et al.  Deep Recurrent Neural Networks for Winter Vegetation Quality Mapping via Multitemporal SAR Sentinel-1 , 2018, IEEE Geoscience and Remote Sensing Letters.

[25]  David A. Clausi,et al.  Sea Ice Concentration Estimation During Melt From Dual-Pol SAR Scenes Using Deep Convolutional Neural Networks: A Case Study , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Xiao Xiang Zhu,et al.  Deep learning in remote sensing: a review , 2017, ArXiv.

[27]  Michele Volpi,et al.  Dense Semantic Labeling of Subdecimeter Resolution Images With Convolutional Neural Networks , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Shawn D. Newsam,et al.  Bag-of-visual-words and spatial extensions for land-use classification , 2010, GIS '10.