Complex Approach of High-Resolution Multispectral Data Engineering for Deep Neural Network Processing

被引:0
作者
Hnatushenko, Volodymyr [1 ]
Zhernovyi, Vadym [1 ]
机构
[1] Oles Gonchar Dnipro Natl Univ, Dept Comp Sci & Informat Technol, Dnipro, Ukraine
来源
LECTURE NOTES IN COMPUTATIONAL INTELLIGENCE AND DECISION MAKING | 2020年 / 1020卷
关键词
Remote sensing; Deep learning; Image processing; Datasets; Region proposals;
D O I
10.1007/978-3-030-26474-1_46
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:659 / 672
页数:14
相关论文
共 22 条
  • [1] Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces
    Alcantarilla, Pablo F.
    Nuevo, Jesus
    Bartoli, Adrien
    [J]. PROCEEDINGS OF THE BRITISH MACHINE VISION CONFERENCE 2013, 2013,
  • [2] [Anonymous], 2016, P AS C COMP VIS
  • [3] [Anonymous], 2016, PROC CVPR IEEE, DOI DOI 10.1109/CVPR.2016.90
  • [4] Audebert N, 2018, DEEP LEARNING REMOTE
  • [5] SPRING: Integrating remote sensing and GIS by object-oriented data modelling
    Camara, G
    Souza, RCM
    Freitas, UM
    Garrido, J
    [J]. COMPUTERS & GRAPHICS, 1996, 20 (03) : 395 - 403
  • [6] Deng J., 2009, CVPR
  • [7] Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning
    Han, Junwei
    Zhang, Dingwen
    Cheng, Gong
    Guo, Lei
    Ren, Jinchang
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2015, 53 (06): : 3325 - 3337
  • [8] Henderson F.M., 1998, Principles and Applications of Imaging Radar. Manual of Remote Sensing, VVolume 2
  • [9] REMOTE SENSING IMAGE FUSION USING ICA AND OPTIMIZED WAVELET TRANSFORM
    Hnatushenko, V. V.
    Vasyliev, V. V.
    [J]. XXIII ISPRS CONGRESS, COMMISSION VII, 2016, 41 (B7): : 653 - 659
  • [10] Hordiiuk DM, 2017, 2017 IEEE INTERNATIONAL YOUNG SCIENTISTS FORUM ON APPLIED PHYSICS AND ENGINEERING (YSF), P363, DOI 10.1109/YSF.2017.8126648