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Remote Sensing
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Image Change Detection Research in Remote Sensing II
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Image Change Detection Research in Remote Sensing II

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 2092

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
Special Issues, Collections and Topics in MDPI journals
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, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is the second Special Issue concerning the contributions of Image Change Detection Research in Remote Sensing.

Satellite, aerial, and UAV imaging are constantly evolving, and 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 the 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.

In this Special Issue, recent advances in image change detection in remote sensing will be presented.  Papers incorporating novel and interesting techniques to study image change detection, as well as some interesting applications, will be considered. Short communications about specific technical issues and well-prepared review papers are also welcomed.

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 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

  • 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
  • IMINT
  • artificial intelligence
  • digital terrain model (DTM)
  • digital surface model (DSM)
  • multitemporal
  • multispectral images
  • unsupervised classification

Published Papers (2 papers)

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Research

20 pages, 12768 KiB  
Article
Enhanced Graph Structure Representation for Unsupervised Heterogeneous Change Detection
by Yuqi Tang, Xin Yang, Te Han, Fangyan Zhang, Bin Zou and Huihui Feng
Remote Sens. 2024, 16(4), 721; https://doi.org/10.3390/rs16040721 - 18 Feb 2024
Viewed by 750
Abstract
Heterogeneous change detection (CD) is widely applied in various fields such as urban planning, environmental monitoring, and disaster management. It enhances the accuracy and comprehensiveness of surface change monitoring by integrating multi-sensor remote sensing data. Scholars have proposed many graph-based methods to address [...] Read more.
Heterogeneous change detection (CD) is widely applied in various fields such as urban planning, environmental monitoring, and disaster management. It enhances the accuracy and comprehensiveness of surface change monitoring by integrating multi-sensor remote sensing data. Scholars have proposed many graph-based methods to address the issue of incomparable heterogeneous images caused by imaging differences. However, these methods often overlook the influence of changes in vertex status on the graph structure, which limits their ability to represent image structural features. To tackle this problem, this paper presents an unsupervised heterogeneous CD method based on enhanced graph structure representation (EGSR). This method enhances the representation capacity of the graph structure for image structural features by measuring the unchanged probabilities of vertices, thereby making it easier to detect changes in heterogeneous images. Firstly, we construct the graph structure using image superpixels and measure the structural graph differences of heterogeneous images in the same image domain. Then, we calculate the unchanged probability of each vertex in the structural graph and reconstruct the graph structure using this probability. To accurately represent the graph structure, we adopt an iterative framework for enhancing the representation of the graph structure. Finally, at the end of the iteration, the final change map (CM) is obtained by binary segmentation of the graph vertices based on their unchanged probabilities. The effectiveness of this method is validated through experiments on four sets of heterogeneous image datasets and two sets of homogeneous image datasets. Full article
(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing II)
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19 pages, 6953 KiB  
Article
MSGFNet: Multi-Scale Gated Fusion Network for Remote Sensing Image Change Detection
by Yukun Wang, Mengmeng Wang, Zhonghu Hao, Qiang Wang, Qianwen Wang and Yuanxin Ye
Remote Sens. 2024, 16(3), 572; https://doi.org/10.3390/rs16030572 - 02 Feb 2024
Viewed by 796
Abstract
Change detection (CD) stands out as a pivotal yet challenging task in the interpretation of remote sensing images. Significant developments have been witnessed, particularly with the rapid advancements in deep learning techniques. Nevertheless, challenges such as incomplete detection targets and unsmooth boundaries remain [...] Read more.
Change detection (CD) stands out as a pivotal yet challenging task in the interpretation of remote sensing images. Significant developments have been witnessed, particularly with the rapid advancements in deep learning techniques. Nevertheless, challenges such as incomplete detection targets and unsmooth boundaries remain as most CD methods suffer from ineffective feature fusion. Therefore, this paper presents a multi-scale gated fusion network (MSGFNet) to improve the accuracy of CD results. To effectively extract bi-temporal features, the EfficientNetB4 model based on a Siamese network is employed. Subsequently, we propose a multi-scale gated fusion module (MSGFM) that comprises a multi-scale progressive fusion (MSPF) unit and a gated weight adaptive fusion (GWAF) unit, aimed at fusing bi-temporal multi-scale features to maintain boundary details and detect completely changed targets. Finally, we use the simple yet efficient UNet structure to recover the feature maps and predict results. To demonstrate the effectiveness of the MSGFNet, the LEVIR-CD, WHU-CD, and SYSU-CD datasets were utilized, and the MSGFNet achieved F1 scores of 90.86%, 92.46%, and 80.39% on the three datasets, respectively. Furthermore, the low computational costs and small model size have validated the superior performance of the MSGFNet. Full article
(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing II)
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