Abstract
A lot of terabytes of complex geospatial data are acquired every day, and it is used in almost every field of science and solves such problems as vegetation health monitoring, disaster management, surveillance, etc. In order to solve mentioned problems this data usually requires multiple steps of pre-processing before inferencing via machine learning algorithms. These steps may include such families of algorithms as image tiling or data augmentation. However, various studies focused on the basic concepts and research on techniques for remote sensing very high-resolution data pre-processing is in scarce.
The current article proposes an approach for data engineering to improve results of processing via the deep learning techniques. The algorithm and dataset are developed, they combine image-tiling techniques and satellite imagery properties. A suggested solution is tested on featured deep convolutional neural networks, such as FuseNet and region-based Mask R-CNN. Described approach for data engineering demonstrates segmentation quality increase for 6%, which is a notable improvement, considering a number of objects of interest in modern high-resolution satellite imagery.
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References
Audebert N, Le Saux B, Lefèvre S (2018) Deep learning for remote sensing - an introduction. IRISA - Université Bretagne Sud, Atelier DLT Sageo, ONERA
Hordiiuk DM, Hnatushenko VV (2017) Neural network and local laplace filter methods applied to very high-resolution remote sensing imagery in urban damage detection. In: 2017 IEEE international young scientists forum on applied physics and engineering (YSF), Lviv, pp 363–366. https://doi.org/10.1109/YSF.2017.8126648
Zhang J (2010) Multi-source remote sensing data fusion: status and trends. Int J Image Data Fusion 1(1):5–24
Henderson FM, Lewis AJ (1998) Principles and applications of imaging radar. Manual of remote sensing, vol 2, 3rd edn. American Geophysical Union, Washington, DC
Câmara G et al (1996) SPRING: integrating remote sensing and GIS by object-oriented data modelling. Comput Graph 20(3):395–403
Xin H, Zhang L (2013) An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery. IEEE Trans Geosci Remote Sens 51(1):257–272
Jia X, Richards JA (1999) Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification. IEEE Trans Geosci Remote Sens 37(1):538–542
Alcantarilla PF, Nuevo J, Bartoli A (2011) Fast explicit diffusion for accelerated features in nonlinear scale spaces. IEEE Trans Patt Anal Mach Intell 34(7):1281–1298
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems
Kaiming H et al (2017) Mask R-CNN. In: 2017 IEEE international conference on computer vision (ICCV). IEEE
Szegedy C et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition
Wassenberg J (2011) Efficient algorithms for large-scale image analysis. KIT Scientific Publishing, Karlsruhe Schriftenreihe automatische Sichtprufung und Bildverarbeitung
Hnatushenko VV, Vasyliev VV (2016) Remote sensing image fusion using ICA and optimized wavelet transform. In: International archives of the photogrammetry, remote sensing and spatial information sciences, vol XLI-B7, XXIII ISPRS Congress, Prague, Czech Republic, pp 653–659
Awesome satellite imagery datasets. GitHub. https://github.com/chrieke/awesome-satellite-imagery-datasets. Accessed 10 Mar 2019
Szabó S, Gacsi Z, Boglárka B (2016) Specific features of NDVI, NDWI and MNDWI as reflected in land cover categories. Acta Geographica Debrecina Landscape Environ 10(3–4):194–202
Longbotham N et al (2014) Prelaunch assessment of worldview-3 information content. In: 2014 6th workshop on hyperspectral image and signal processing: evolution in remote sensing (WHISPERS). IEEE
Samsudin HS, Shafri HZM, Hamedianfar A (2016) Development of spectral indices for roofing material condition status detection using field spectroscopy and WorldView-3 data. J Appl Remote Sens 10(2):025021
Han J, Zhang D, Cheng G, Guo L, Ren J (2015) Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning. IEEE Trans Geosci Remote Sens 53(6):3325–3337
Lu X, Yuan Y, Zheng X (2017) Joint dictionary learning for multispectral change detection. IEEE Trans Cybern 47(4):884–897
He K et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition
Deng J et al (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE
Hazirbas C et al (2016) Fusenet: incorporating depth into semantic segmentation via fusion-based CNN architecture. In: Asian conference on computer vision. Springer, Cham
Microsoft computer generated building footprints for Canada. https://github.com/Microsoft/CanadianBuildingFootprints. Accessed 8 Mar 2019
Hnatushenko VV, Kashtan VJ, Shedlovska YI (2017) Processing technology of multispectral remote sensing images. In: IEEE international young scientists forum on applied physics and engineering YSF-2017, Lviv, Ukraine, pp 355–358
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Hnatushenko, V., Zhernovyi, V. (2020). Complex Approach of High-Resolution Multispectral Data Engineering for Deep Neural Network Processing. In: Lytvynenko, V., Babichev, S., Wójcik, W., Vynokurova, O., Vyshemyrskaya, S., Radetskaya, S. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2019. Advances in Intelligent Systems and Computing, vol 1020. Springer, Cham. https://doi.org/10.1007/978-3-030-26474-1_46
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DOI: https://doi.org/10.1007/978-3-030-26474-1_46
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