Special Issue "Image Change Detection Research in Remote Sensing"

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

Deadline for manuscript submissions: 30 November 2022.

Special Issue Editors

Dr. Damian Wierzbicki
E-Mail Website
Guest Editor
Institute of Geospatial Engineering and Geodesy, Faculty of Civil Engineering and Geodesy, Military University of Technology, 00-908 Warsaw, Poland
Interests: photogrammetry; remote sensing; UAV; dense image matching; deep learning; image quality; image classification
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Dr. Kamil Krasuski
E-Mail Website
Guest Editor
Institute of Navigation, Military University of Aviation, 08-521 Dęblin, Poland
Interests: GPS; GLONASS; Galileo; SBAS; GBAS; accuracy; EGNOS; aircraft position; GNSS satellite positioning; accuracy analysis; elements of exterior orientation; UAV positioning; UAV orientation; UAV navigation; flight parameters of UAV
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will focus on new change detection trends in remote sensing.

Change detection based on modern image processing algorithms and remote sensing data is important for monitoring changes on the Earth’s surface. Change detection is used both in military (e.g., imagery intelligence) and civilian areas. Examples of civilian applications include urban planning, environmental monitoring, precision agriculture, monitoring of land changes, and analysis of the movement of objects. In recent years, with the intensive development of many remote sensing platforms and deep learning algorithms, research into new methods of change detection has become increasingly important. The possibility of integrating data from many sources (e.g., radar and optical data), as well as the analysis of time series of navigation data, also play an important role.

Modern Remote Sensing software also offers many possibilities; thanks to the intensive development of change detection algorithms, this software allows the implementation of many remote sensing studies based not only on images obtained in the visible range, but also multispectral images, radar data, and laser scanning data. An interesting research issue also relates to problems in the implementation of deep learning methods for change detection, object tracking, and image understanding.

Thanks to the increasing availability of multi-source image data and new data processing methods based often on artificial intelligence, the proposed Special Issue of Remote Sensing will discuss the latest achievements and development directions of change detection methods and their practical application.

Dr. Damian Wierzbicki
Dr. Kamil Krasuski
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 papers will be 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 2400 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.


  • UAV, aerial, and satellite data fusion
  • Radar and optical data fusion
  • Image matching and co-registration
  • Multi-temporal data classification
  • Land use change
  • Deep learning for change detection
  • Deep learning for time-series analysis
  • Deep learning for image processing and classification
  • Deep learning for image understanding
  • 3D change detection
  • GNSS and image data fusion for change detection
  • Image scene analysis
  • Image quality assessment
  • Artificial intelligence
  • Digital terrain model (DTM)
  • Digital surface model (DSM)
  • Multitemporal
  • Multispectral images
  • Unsupervised classification

Published Papers (1 paper)

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Looking for Change? Roll the Dice and Demand Attention
Remote Sens. 2021, 13(18), 3707; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183707 (registering DOI) - 16 Sep 2021
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Change detection, i.e., the identification per pixel of changes for some classes of interest from a set of bi-temporal co-registered images, is a fundamental task in the field of remote sensing. It remains challenging due to unrelated forms of change that appear at [...] Read more.
Change detection, i.e., the identification per pixel of changes for some classes of interest from a set of bi-temporal co-registered images, is a fundamental task in the field of remote sensing. It remains challenging due to unrelated forms of change that appear at different times in input images. Here, we propose a deep learning framework for the task of semantic change detection in very high-resolution aerial images. Our framework consists of a new loss function, a new attention module, new feature extraction building blocks, and a new backbone architecture that is tailored for the task of semantic change detection. Specifically, we define a new form of set similarity that is based on an iterative evaluation of a variant of the Dice coefficient. We use this similarity metric to define a new loss function as well as a new, memory efficient, spatial and channel convolution Attention layer: the FracTAL. We introduce two new efficient self-contained feature extraction convolution units: the CEECNet and FracTALResNet units. Further, we propose a new encoder/decoder scheme, a network macro-topology, that is tailored for the task of change detection. The key insight in our approach is to facilitate the use of relative attention between two convolution layers in order to fuse them. We validate our approach by showing excellent performance and achieving state-of-the-art scores (F1 and Intersection over Union-hereafter IoU) on two building change detection datasets, namely, the LEVIRCD (F1: 0.918, IoU: 0.848) and the WHU (F1: 0.938, IoU: 0.882) datasets. Full article
(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing)
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