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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 7101

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

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Published Papers (5 papers)

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28 pages, 15893 KiB  
Article
A Novel UNet 3+ Change Detection Method Considering Scale Uncertainty in High-Resolution Imagery
by Ting Bai, Qing An, Shiquan Deng, Pengfei Li, Yepei Chen, Kaimin Sun, Huajian Zheng and Zhina Song
Remote Sens. 2024, 16(11), 1846; https://doi.org/10.3390/rs16111846 - 22 May 2024
Cited by 1 | Viewed by 921
Abstract
The challenge of detecting changes in high-resolution remote sensing imagery often stems from the difficulties in effectively extracting features and constructing appropriate change detection models considering the scale characteristics of ground objects. To solve these issues, we propose a novel UNet 3+ change [...] Read more.
The challenge of detecting changes in high-resolution remote sensing imagery often stems from the difficulties in effectively extracting features and constructing appropriate change detection models considering the scale characteristics of ground objects. To solve these issues, we propose a novel UNet 3+ change detection method that considers the scale characteristics inherent in various land-cover change types. Our method includes three key steps: a multi-scale segmentation method, a class-specific UNet 3+ method, and an object-oriented change detection method based on UNet 3+. To verify the effectiveness of this method, we select two datasets for experiments and compare our proposed method with the UNet 3+ single-scale sampling method, the class-specific UNet 3+ single-scale sampling method, and the UNet 3+ multi-scale hierarchical sampling method. Our experimental results show that our proposed method has higher overall accuracy and F1, lower missed detection rate and false detection rate, and can detect more changes in ground features than other methods. To verify the scalability of this method, we compare this method with traditional change detection methods such as PCA-k-means, OCVA, a single-scale sampling method based on random forest, and a class-specific object-based method. Experimental results and accuracy indexes show that our proposed method better considers the scale characteristics of ground objects and achieves higher accuracy. Additionally, we compared our proposed method with other DLCD methods including LamboiseNet, BIT, CDNet, FCSiamConc, and FCSiamDiff. Our results show that our proposed method effectively considers edge information and has an acceptable time consumption. Our approach not only considers the full-scale characteristics of the feature extraction but also the scale characteristics of the change detection model. In addition, it considers a more practical feature extraction unit (object), making it more accurate. Full article
(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing II)
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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
Cited by 4 | Viewed by 1527
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 - 2 Feb 2024
Cited by 2 | Viewed by 1832
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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17 pages, 8306 KiB  
Technical Note
Improve Adversarial Robustness of AI Models in Remote Sensing via Data-Augmentation and Explainable-AI Methods
by Sumaiya Tasneem and Kazi Aminul Islam
Remote Sens. 2024, 16(17), 3210; https://doi.org/10.3390/rs16173210 - 30 Aug 2024
Viewed by 814
Abstract
Artificial intelligence (AI) has made remarkable progress in recent years in remote sensing applications, including environmental monitoring, crisis management, city planning, and agriculture. However, the critical challenge in utilizing AI models in real-world remote sensing applications is maintaining their robustness and reliability, particularly [...] Read more.
Artificial intelligence (AI) has made remarkable progress in recent years in remote sensing applications, including environmental monitoring, crisis management, city planning, and agriculture. However, the critical challenge in utilizing AI models in real-world remote sensing applications is maintaining their robustness and reliability, particularly against adversarial attacks. In adversarial attacks, attackers manipulate benign data to create a perturbation to mislead AI models into predicting incorrect decisions, posing a catastrophic threat to the security of their applications, particularly in crucial decision-making contexts. These attacks pose a significant threat to the integrity and comprehensiveness of AI models in remote sensing applications, as they can lead to inaccurate decisions with substantial consequences. In this paper, we propose to develop an adversarial robustness technique that will ensure the AI model’s accurate prediction in the presence of adversarial perturbation. In this work, we address these challenges by developing a better adversarial training approach using explainable AI method-guided features and data augmentation techniques to strengthen the AI model prediction in remote sensing data against adversarial attacks. The proposed approach achieved the best adversarial robustness against Project Gradient Descent (PGD) attacks in EuroSAT and AID datasets and showed transferability of robustness against unseen attacks. Full article
(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing II)
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14 pages, 4313 KiB  
Technical Note
Hyperspectral Imaging Spectroscopy for Non-Destructive Determination of Grape Berry Total Soluble Solids and Titratable Acidity
by Hongyi Lyu, Miles Grafton, Thiagarajah Ramilan, Matthew Irwin and Eduardo Sandoval
Remote Sens. 2024, 16(10), 1655; https://doi.org/10.3390/rs16101655 - 7 May 2024
Viewed by 1119
Abstract
Wine grape quality heavily influences the price received for a product. Hyperspectral imaging has the potential to provide a non-destructive technique for predicting various enological parameters. This study aims to explore the feasibility of applying hyperspectral imaging to measure the total soluble solids [...] Read more.
Wine grape quality heavily influences the price received for a product. Hyperspectral imaging has the potential to provide a non-destructive technique for predicting various enological parameters. This study aims to explore the feasibility of applying hyperspectral imaging to measure the total soluble solids (TSS) and titratable acidity (TA) in wine grape berries. A normalized difference spectral index (NDSI) spectral preprocessing method was built and compared with the conventional preprocessing method: multiplicative scatter correction and Savitzky–Golay smoothing (MSC+SG). Different machine learning models were built to examine the performance of the preprocessing methods. The results show that the NDSI preprocessing method demonstrated better performance than the MSC+SG preprocessing method in different classification models, with the best model correctly classifying 93.8% of the TSS and 84.4% of the TA. In addition, the TSS can be predicted with moderate performance using support vector regression (SVR) and MSC+SG preprocessing with a root mean squared error (RMSE) of 0.523 °Brix and a coefficient of determination (R2) of 0.622, and the TA can be predicted with moderate performance using SVR and NDSI preprocessing (RMSE = 0.19%, R2 = 0.525). This study demonstrates that hyperspectral imaging data and NDSI preprocessing have the potential to be a method for grading wine grapes for producing quality wines. Full article
(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing II)
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