Special Issue "Remote Sensing Image Fusion and Modeling"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: closed (20 December 2021) | Viewed by 1499

Special Issue Editors

Prof. Dr. Jean Sequeira
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Guest Editor
Computer Science Department, Aix-Marseille University, 860 Allée du Garlaban, 13360 Roquevaire, France
Interests: image analysis; pattern recognition; geometrical modeling; visualization
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Prof. Dr. Xingfa Gu
E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: quantitative remote sensing; radiometric calibration; atmospheric correction; aerosol remote sensing
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Dr. Sébastien Mavromatis
E-Mail Website
Guest Editor
Polytech Marseille Sud, Aix-Marseille Université, 13288 Marseille CEDEX 09, France
Interests: image analysis; pattern recognition; visualization; remote science; data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

More than one thousand satellites are dedicated to Earth observation, providing a constant and huge flow of “remote sensing” images. Extraction of information from these images enables a better understanding of spatial and temporal evolution of natural and artificial phenomena. However, these images require being geographically registered in order to be efficiently used in a GIS (geographic information system). This process is known as “image fusion and modeling”. For example, when having many images of the same area, we can use a “super-resolution” algorithm in order to produce an image with a better resolution, or we can produce additional knowledge using a “deep learning” process. It is also interesting to mix information from images of different modalities and use a 3D support through a DEM (digital elevation model). Applications are numerous and deal with fields such as environment monitoring, urban planning, forestry, water management, agriculture, and several other ones, this list not being limited. Submissions will deal with methodology (algorithms, systems, etc.) and with applications.

Prof. Dr. Jean Sequeira
Prof. Dr. Xingfa Gu
Dr. Sébastien Mavromatis
Guest Editors

Manuscript Submission Information

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Keywords

  • Image analysis
  • Image registration
  • GIS (geographic information system)
  • 2D and 3D geometrical modeling
  • Multimodality
  • Extraction of information from images
  • Big data
  • Super-resolution

Published Papers (1 paper)

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Research

Article
Remote Sensing Road Extraction by Road Segmentation Network
Appl. Sci. 2021, 11(11), 5050; https://doi.org/10.3390/app11115050 - 29 May 2021
Cited by 1 | Viewed by 919
Abstract
Road extraction from remote sensing images has attracted much attention in geospatial applications. However, the existing methods do not accurately identify the connectivity of the road. The identification of the road pixels may be interfered with by the abundant ground such as buildings, [...] Read more.
Road extraction from remote sensing images has attracted much attention in geospatial applications. However, the existing methods do not accurately identify the connectivity of the road. The identification of the road pixels may be interfered with by the abundant ground such as buildings, trees, and shadows. The objective of this paper is to enhance context and strip features of the road by designing UNet-like architecture. The overall method first enhances the context characteristics in the segmentation step and then maintains the stripe characteristics in a refinement step. The segmentation step exploits an attention mechanism to enhance the context information between the adjacent layers. To obtain the strip features of the road, the refinement step introduces the strip pooling in a refinement network to restore the long distance dependent information of the road. Extensive comparative experiments demonstrate that the proposed method outperforms other methods, achieving an overall accuracy of 98.25% on the DeepGlobe dataset, and 97.68% on the Massachusetts dataset. Full article
(This article belongs to the Special Issue Remote Sensing Image Fusion and Modeling)
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