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Advances in Change Detection and Analysis Using Multi-Source Remote Sensing Data

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

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 8796

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Dr. Fernando Vicente-Guijalba

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Remote Sensing Consultant, C/ Princep de Viana 7, Barcelona, 08001 Barcelona, Spain
Interests: SAR; interferometry; remote sensing; land cover; mapping; machine learning
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Special Issue Information

Dear Colleagues,

The editorial board of Remote Sensing has the pleasure to invite you to contribute to this Special Issue entitled “Advances in Change Detection and Analysis using Multi-Source Remote Sensing Data”.

The Earth’s surface certainly has an inherent dynamic and evolving nature. Although it has been modifying its morphology since it was early formed, anthropic contribution has incredibly boosted and diversified the variety of these changes. In particular, many indicators have already diagnosed how global-scale climate change will severely alter the surface of our planet. To track and minimize the foreseen consequences of these alterations, it is vital to push forward and develop advanced methodologies and techniques for change detection analysis through the exploitation of the extensive amount of Earth observation remote sensing data. Myriads of diverse technological solutions are currently observing every corner of the surface of the Earth with different perspectives and scales, both in the temporal and the spatial domains. Each of these sensors has access to very specific aspects and characteristics of the surface components, and therefore, they are able to track the dynamic components in distinctive ways. The simultaneous consideration of multiple complementary data is mandatory to obtain a feasible representation of the real nature of the dynamical changes on the surface.

Therefore, the objective of this Special Issue in the journal Remote Sensing is to create a follow-up analysis and an update of the state-of-the-art methodologies for multi-source data analysis and integration for combined change detection estimation. In this Special Issue, we expect to cover the latest methodological advances in this context and ultimately promote innovative approaches, such as advanced machine learning techniques, specifically designed for the analysis of change detection.

Dr. Fernando Vicente-Guijalba
Guest Editor

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

change detection;
surface dynamics;
multi-temporal data analysis;
advanced machine learning techniques;
active/passive sensors;
RF/optical sensors;
multi-source data assimilation;
multi-source data fusion.

Published Papers (4 papers)

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Research

15 pages, 3307 KiB

Open AccessArticle

Multi-Scale Feature Interaction Network for Remote Sensing Change Detection

by Chong Zhang, Yonghong Zhang and Haifeng Lin

Remote Sens. 2023, 15(11), 2880; https://doi.org/10.3390/rs15112880 - 1 Jun 2023

Cited by 3 | Viewed by 1233

Abstract

Change detection (CD) is an important remote sensing (RS) data analysis technology. Existing remote sensing change detection (RS-CD) technologies cannot fully consider situations where pixels between bitemporal images do not correspond well on a one-to-one basis due to factors such as seasonal changes and lighting conditions. Existing networks construct two identical feature extraction branches through convolution, which share weights. The two branches work independently and do not merge until the feature mapping is sent to the decoder head. This results in a lack of feature information interaction between the two images. So, directing attention to the change area is of research interest. In complex backgrounds, the loss of edge details is very important. Therefore, this paper proposes a new CD algorithm that extracts multi-scale feature information through the backbone network in the coding stage. According to the task characteristics of CD, two submodules (the Feature Interaction Module and Detail Feature Guidance Module) are designed to make the feature information between the bitemporal RS images fully interact. Thus, the edge details are restored to the greatest extent while fully paying attention to the change areas. Finally, in the decoding stage, the feature information of different levels is fully used for fusion and decoding operations. We build a new CD dataset to further verify and test the model’s performance. The generalization and robustness of the model are further verified by using two open datasets. However, due to the relatively simple construction of the model, it cannot handle the task of multi-classification CD well. Therefore, further research on multi-classification CD algorithms is recommended. Moreover, due to the high production cost of CD datasets and the difficulty in obtaining them in practical tasks, future research will look into semi-supervised or unsupervised related CD algorithms. Full article

(This article belongs to the Special Issue Advances in Change Detection and Analysis Using Multi-Source Remote Sensing Data)

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15 pages, 26848 KiB

Open AccessArticle

Analysis and Verification of Building Changes Based on Point Clouds from Different Sources and Time Periods

by Urszula Marmol and Natalia Borowiec

Remote Sens. 2023, 15(5), 1414; https://doi.org/10.3390/rs15051414 - 2 Mar 2023

Cited by 2 | Viewed by 2128

Abstract

Detecting changes in buildings over time is an important issue in monitoring urban areas, landscape changes, assessing natural disaster risks or updating geospatial databases. Three-dimensional (3D) information derived from dense image matching or laser data can effectively extract changes in buildings. This research proposes an automated method for detecting building changes in urban areas using archival aerial images and LiDAR data. The archival images, dating from 1970 to 1993, were subjected to a dense matching procedure to obtain point clouds. The LiDAR data came from 2006 and 2012. The proposed algorithm is based on height difference-generated nDSM. In addition, morphological filters and criteria considering area size and shape parameters were included. The study was divided into two sections: one concerned the detection of buildings from LiDAR data, an issue that is now widely known and used; the other concerned an attempt at automatic detection from archived aerial images. The automation of detection from archival data proved to be complex, so issues related to the generation of a dense point cloud from this type of data were discussed in detail. The study revealed problems of archival images related to the poor identification of ground control points (GCP), insufficient overlap between images or poor radiometric quality of the scanned material. The research showed that over the 50 years, the built-up area increased as many as three times in the analysed area. The developed method of detecting buildings calculated at a level of more than 90% in the case of the LiDAR data and 88% based on the archival data. Full article

(This article belongs to the Special Issue Advances in Change Detection and Analysis Using Multi-Source Remote Sensing Data)

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24 pages, 14504 KiB

Open AccessArticle

Global and Local Graph-Based Difference Image Enhancement for Change Detection

by Xiaolong Zheng, Dongdong Guan, Bangjie Li, Zhengsheng Chen and Lefei Pan

Remote Sens. 2023, 15(5), 1194; https://doi.org/10.3390/rs15051194 - 21 Feb 2023

Cited by 2 | Viewed by 1625

Abstract

Change detection (CD) is an important research topic in remote sensing, which has been applied in many fields. In the paper, we focus on the post-processing of difference images (DIs), i.e., how to further improve the quality of a DI after the initial DI is obtained. The importance of DIs for CD problems cannot be overstated, however few methods have been investigated so far for re-processing DIs after their acquisition. In order to improve the DI quality, we propose a global and local graph-based DI-enhancement method (GLGDE) specifically for CD problems; this is a plug-and-play method that can be applied to both homogeneous and heterogeneous CD. GLGDE first segments the multi-temporal images and DIs into superpixels with the same boundaries and then constructs two graphs for the DI with superpixels as vertices: one is the global feature graph that characterizes the association between the similarity relationships of connected vertices in the multi-temporal images and their changing states in a DI, the other is the local spatial graph that exploits the change information and contextual information of the DI. Based on these two graphs, a DI-enhancement model is built, which constrains the enhanced DI to be smooth on both graphs. Therefore, the proposed GLGDE can not only smooth the DI but also correct the it. By solving the minimization model, we can obtain an improved DI. The experimental results and comparisons on different CD tasks with six real datasets demonstrate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Advances in Change Detection and Analysis Using Multi-Source Remote Sensing Data)

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19 pages, 14388 KiB

Open AccessArticle

Using Machine-Learning for the Damage Detection of Harbour Structures

by Frederic Hake, Leonard Göttert, Ingo Neumann and Hamza Alkhatib

Remote Sens. 2022, 14(11), 2518; https://doi.org/10.3390/rs14112518 - 24 May 2022

Cited by 5 | Viewed by 2327

Abstract

The ageing infrastructure in ports requires regular inspection. This inspection is currently carried out manually by divers who sense the entire below-water infrastructure by hand. This process is cost-intensive as it involves a lot of time and human resources. To overcome these difficulties, we propose scanning the above and below-water port structure with a multi-sensor system, and by a fully automated process to classify the point cloud obtained into damaged and undamaged zones. We make use of simulated training data to test our approach because not enough training data with corresponding class labels are available yet. Accordingly, we build a rasterised height field of a point cloud of a sheet pile wall by subtracting a computer-aided design model. The latter is propagated through a convolutional neural network, which detects anomalies. We make use of two methods: the VGG19 deep neural network and local outlier factors. We showed that our approach can achieve a fully automated, reproducible, quality-controlled damage detection, which can analyse the whole structure instead of the sample-wise manual method with divers. We were able to achieve valuable results for our application. The accuracy of the proposed method is 98.8% following a desired recall of 95%. The proposed strategy is also applicable to other infrastructure objects, such as bridges and high-rise buildings. Full article

(This article belongs to the Special Issue Advances in Change Detection and Analysis Using Multi-Source Remote Sensing Data)

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