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Target Recognition and Change Detection for High-Resolution Remote Sensing Images

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 25 March 2024 | Viewed by 537

Special Issue Editors

State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China
Interests: multi-temporal image processing and change detection; hyper-spectral image processing; high-resolution image understanding; satellite video tracking; multi-source data fusion; machine learning; computer vision; urban remote sensing
School of Information Engineering, Ningxia University, Yinchuan 750021, China
Interests: high spatial resolution remote sensing image classification; change detection; hyperspectral remote sensing image interpretation; machine learning
School of Electronic Information, Wuhan University, Wuhan 430072, China
Interests: object detection and change analysis; multi-sensor information fusion based remote sensing image interpretation
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Special Issue Information

Dear Colleagues,

The fast development of remote sensing platforms brings further improvement in the resolution of remote sensing images. High-resolution remote sensing images contain more detailed spatial, spectral, and temporal information of ground landscapes. Recognizing the targets and the changes from multi-source high-resolution remote sensing data has becomes an important topic for Earth observation techniques in many applications. However, this task still encounters several challenges: (1) From the aspect of the images: though high-resolution images make it possible to monitor more types of targets and changes with precise boundaries, the problem of spatial and spectral variability caused by different acquiring conditions (illumination, angle, atmosphere) and sensor characteristics is also severe. (2) From the aspect of the landscapes: the targets on the ground inherently show more complex shapes and higher diversities, which makes it difficult to model their information by traditional methods. (3) From the aspect of applications: manual labelling for training and verifying with high time- and labour-costs, has limited the universality of applying high-resolution remote sensing data to multiple fields. Therefore, it is a very interesting and crucial topic.

This Special Issue aims to focus on discussing the theoretical frontiers and technical problems in target recognition and change detection for high-resolution remote sensing images and provide a platform for researchers to show their recent contributions.

  • Target recognition and change detection with high spatial, spectral, and temporal resolution remote sensing images.
  • Advanced interpretation of high-resolution images by unsupervised, semi-supervised, weakly supervised, and self-supervised mechanisms.
  • Datasets and benchmarks for target recognition and change detection with high-resolution remote sensing images.
  • Advances in machine learning and deep learning techniques for high-resolution remote sensing image processing.
  • The applications of high-resolution remote sensing data in various fields. 

Prof. Dr. Chen Wu
Dr. Pengyuan Lv
Dr. Naoto Yokoya
Prof. Dr. Wen Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • target recognition
  • change detection
  • high-resolution remote sensing
  • deep learning
  • machine learning

Published Papers

This special issue is now open for submission.
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