ISPRS Journal of Photogrammetry and Remote Sensing

Aims & Scope

The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) is the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS).

The Journal provides a channel of communication for scientists and professionals in all countries working in the many disciplines that employ photogrammetry, remote sensing, spatial information systems, computer vision, and related fields.

The Journal is designed to serve as a source reference and archive of advancements in these disciplines.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	12.2

SJR (SCImago Journal Rank)

Year	Value
2024	3.480

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	204

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, Earth and Planetary Sciences, Engineering, Physics and Astronomy and Social Sciences, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Random forest in remote sensing: A review of applications and future directions

  Citation: 5181

  Authors: Mariana, Lucian

• Object based image analysis for remote sensing

  Citation: 3418

  Authors: T.

• Support vector machines in remote sensing: A review

  Citation: 2676

  Authors: Giorgos, Jungho, Caesar

• An assessment of the effectiveness of a random forest classifier for land-cover classification

  Citation: 2269

  Authors: V.F., B., J., M., J.P.

• Unmanned aerial systems for photogrammetry and remote sensing: A review

  Citation: 2113

  Authors: I., P.

• Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information

  Citation: 1899

  Authors: Ursula C., Peter, Gregor, Iris, Markus

• Deep learning in remote sensing applications: A meta-analysis and review

  Citation: 1778

  Authors: Lei, Yu, Xueliang, Yuanxin, Gaofei, Brian Alan

• Object detection in optical remote sensing images: A survey and a new benchmark

  Citation: 1590

  Authors: Ke, Gang, Gong, Liqiu, Junwei

• Global land cover mapping at 30 m resolution: A POK-based operational approach

  Citation: 1562

  Authors: Jun, Jin, Anping, Xin, Lijun, Xuehong, Chaoying, Gang, Shu, Miao, Weiwei, Xiaohua, Jon

• ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data

  Citation: 1481

  Authors: Foivos I., François, Peter, Chen

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