Remote Sensing - Journal - MOST Wiedzy

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Remote Sensing

ISSN:

2072-4292

Disciplines
(Field of Science):

  • Automation, electronic and electrical engineering (Engineering and Technology)
  • Information and communication technology (Engineering and Technology)
  • Civil engineering and transport (Engineering and Technology)
  • Environmental engineering, mining and energy (Engineering and Technology)
  • Forestry (Agricultural sciences)
  • Agriculture and horticulture (Agricultural sciences)
  • Social and economic geography and spatial management (Social studies)
  • Law (Social studies)
  • Earth and related environmental sciences (Natural sciences)

Ministry points: Help

Ministry points
2021 100 Ministry Scored Journals List 2019
Ministry points
Year Points List
2021 100 Ministry Scored Journals List 2019
2020 100 Ministry Scored Journals List 2019
2019 100 Ministry Scored Journals List 2019
2018 35 A
2017 35 A
2016 35 A
2015 35 A
2014 35 A
2013 35 A

Model:

Open Access

Punkty CiteScore:

Punkty CiteScore
2019 6.1
Punkty CiteScore
Year Points
2019 6.1
2018 5.6
2017 5.3
2016 5.2
2015 4.3
2014 3.9
2013 3.7
2012 2.7
2011 1.1

Impact Factor:

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Publishing policy:

Pre-print
author's version of the article before the review
Post-print
author's version of the article after the review

Status table SHERPA RoMEO

Status table SHERPA RoMEO
RoMEO color Archiving policy
Green can archive pre-prints and post-prints or a version of the publisher
Blue can archive post-prints
Yellow can archive pre-prints
White can not archive any materials
Gray unknown

Filters

total: 24

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Catalog Journals

2021
  • Cascade Object Detection and Remote Sensing Object Detection Method Based on Trainable Activation Function
    Publication
    • S. Shivappriya
    • M. Priyadarsini
    • A. Stateczny
    • C. Puttamadappa
    • B. Parameshachari

    - Remote Sensing - 2021

    Object detection is an important process in surveillance system to locate objects and it is considered as major application in computer vision. The Convolution Neural Network (CNN) based models have been developed by many researchers for object detection to achieve higher performance. However, existing models have some limitations such as overfitting problem and lower efficiency in small object detection. Object detection in remote...

    Full text available

2020
2019
2018
2016

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