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Image Change Detection Research in Remote Sensing

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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 (30 November 2022) | Viewed by 31320

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Special Issue Editors

Dr. Damian Wierzbicki

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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 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 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 (13 papers)

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20 pages, 12427 KiB

Open AccessEditor’s ChoiceArticle

TChange: A Hybrid Transformer-CNN Change Detection Network

by Yupeng Deng, Yu Meng, Jingbo Chen, Anzhi Yue, Diyou Liu and Jing Chen

Remote Sens. 2023, 15(5), 1219; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15051219 - 22 Feb 2023

Cited by 8 | Viewed by 3068

Abstract

Change detection is employed to identify regions of change between two different time phases. Presently, the CNN-based change detection algorithm is the mainstream direction of change detection. However, there are two challenges in current change detection methods: (1) the intrascale problem: CNN-based change detection algorithms, due to the local receptive field limitation, can only fuse pairwise characteristics in a local range within a single scale, causing incomplete detection of large-scale targets. (2) The interscale problem: Current algorithms generally fuse layer by layer for interscale communication, with one-way flow of information and long propagation links, which are prone to information loss, making it difficult to take into account both large targets and small targets. To address the above issues, a hybrid transformer–CNN change detection network (TChange) for very-high-spatial-resolution (VHR) remote sensing images is proposed. (1) Change multihead self-attention (Change MSA) is built for global intrascale information exchange of spatial features and channel characteristics. (2) An interscale transformer module (ISTM) is proposed to perform direct interscale information exchange. To address the problem that the transformer tends to lose high-frequency features, the use of deep edge supervision is proposed to replace the commonly utilized depth supervision. TChange achieves state-of-the-art scores on the WUH-CD and LEVIR-CD open-source datasets. Furthermore, to validate the effectiveness of Change MSA and the ISTM proposed by TChange, we construct a change detection dataset, TZ-CD, that covers an area of 900

{km}^{2}

and contains numerous large targets and weak change targets. Full article

(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing)

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25 pages, 15095 KiB

Open AccessArticle

TSCNet: Topological Structure Coupling Network for Change Detection of Heterogeneous Remote Sensing Images

by Xianghai Wang, Wei Cheng, Yining Feng and Ruoxi Song

Remote Sens. 2023, 15(3), 621; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030621 - 20 Jan 2023

Cited by 3 | Viewed by 2631

Abstract

With the development of deep learning, convolutional neural networks (CNNs) have been successfully applied in the field of change detection in heterogeneous remote sensing (RS) images and achieved remarkable results. However, most of the existing methods of heterogeneous RS image change detection only extract deep features to realize the whole image transformation and ignore the description of the topological structure composed of the image texture, edge, and direction information. The occurrence of change often means that the topological structure of the ground object has changed. As a result, these algorithms severely limit the performance of change detection. To solve these problems, this paper proposes a new topology-coupling-based heterogeneous RS image change detection network (TSCNet). TSCNet transforms the feature space of heterogeneous images using an encoder–decoder structure and introduces wavelet transform, channel, and spatial attention mechanisms. The wavelet transform can obtain the details of each direction of the image and effectively capture the image’s texture features. Unnecessary features are suppressed by allocating more weight to areas of interest via channels and spatial attention mechanisms. As a result of the organic combination of a wavelet, channel attention mechanism, and spatial attention mechanism, the network can focus on the texture information of interest while suppressing the difference of images from different domains. On this basis, a bitemporal heterogeneous RS image change detection method based on the TSCNet framework is proposed. The experimental results on three public heterogeneous RS image change detection datasets demonstrate that the proposed change detection framework achieves significant improvements over the state-of-the-art methods. Full article

(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing)

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25 pages, 25232 KiB

Open AccessArticle

Change Alignment-Based Image Transformation for Unsupervised Heterogeneous Change Detection

by Kuowei Xiao, Yuli Sun and Lin Lei

Remote Sens. 2022, 14(21), 5622; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215622 - 07 Nov 2022

Cited by 1 | Viewed by 1903

Abstract

Change detection (CD) with heterogeneous images is currently attracting extensive attention in remote sensing. In order to make heterogeneous images comparable, the image transformation methods transform one image into the domain of another image, which can simultaneously obtain a forward difference map (FDM) and backward difference map (BDM). However, previous methods only fuse the FDM and BDM in the post-processing stage, which cannot fundamentally improve the performance of CD. In this paper, a change alignment-based change detection (CACD) framework for unsupervised heterogeneous CD is proposed to deeply utilize the complementary information of the FDM and BDM in the image transformation process, which enhances the effect of domain transformation, thus improving CD performance. To reduce the dependence of the transformation network on labeled samples, we propose a graph structure-based strategy of generating prior masks to guide the network, which can reduce the influence of changing regions on the transformation network in an unsupervised way. More importantly, based on the fact that the FDM and BDM are representing the same change event, we perform change alignment during the image transformation, which can enhance the image transformation effect and enable FDM and BDM to effectively indicate the real change region. Comparative experiments are conducted with six state-of-the-art methods on five heterogeneous CD datasets, showing that the proposed CACD achieves the best performance with an average overall accuracy (OA) of

95.9 %

on different datasets and at least

6.8 %

improvement in the kappa coefficient. Full article

(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing)

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22 pages, 29780 KiB

Open AccessArticle

A Region-Based Feature Fusion Network for VHR Image Change Detection

by Pan Chen, Cong Li, Bing Zhang, Zhengchao Chen, Xuan Yang, Kaixuan Lu and Lina Zhuang

Remote Sens. 2022, 14(21), 5577; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215577 - 04 Nov 2022

Cited by 7 | Viewed by 1501

Abstract

Deep learning (DL)-based architectures have shown a strong capacity to identify changes. However, existing change detection (CD) networks still suffer from limited applicability when it comes to multi-scale targets and spatially misaligned objects. For the sake of tackling the above problems, a region-based feature fusion network (RFNet) for CD of very high spatial resolution (VHR) remote sensing images is proposed. RFNet uses a fully convolutional Siamese network backbone where a multi-stage feature interaction module (MFIM) is embedded in the dual encoder and a series of region-based feature fusion modules (RFFMs) is used to generate change information. The MFIM fuses features in different stages to enhance the interaction of multi-scale information and help the network better distinguish complex ground objects. The RFFM is built based on region similarity (RSIM), which measures the similarity of bitemporal features with neighborhoods. The RFFM can reduce the impact of spatially offset bitemporal targets and accurately identify changes in bitemporal images. We also design a deep supervise strategy by directly introducing RSIM into loss calculation and shortening the error propagation distance. We validate RFNet with two popular CD datasets: the SECOND dataset and the WHU dataset. The qualitative and quantitative comparison results demonstrate the high capacity and strong robustness of RFNet. We also conduct robustness experiments and the results demonstrate that RFNet can deal with spatially shifted bitemporal images. Full article

(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing)

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20 pages, 21164 KiB

Open AccessArticle

Multimodal Satellite Image Time Series Analysis Using GAN-Based Domain Translation and Matrix Profile

by Anamaria Radoi

Remote Sens. 2022, 14(15), 3734; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153734 - 04 Aug 2022

Cited by 1 | Viewed by 2115

Abstract

The technological development of the remote sensing domain led to the acquisition of satellite image time series (SITS) for Earth Observation (EO) by a variety of sensors. The variability in terms of the characteristics of the satellite sensors requires the existence of algorithms that allow the integration of multiple modalities and the identification of anomalous spatio-temporal evolutions caused by natural hazards. The unsupervised analysis of multimodal SITS proposed in this paper follows a two-step methodology: (i) inter-modality translation and (ii) the identification of anomalies in a change-detection framework. Inter-modality translation is achieved by means of a Generative Adversarial Network (GAN) architecture, whereas, for the identification of anomalies caused by natural hazards, we adapt the task to a similarity search in SITS. In this regard, we provide an extension of the matrix profile concept, which represents an answer to identifying differences and to discovering novelties in time series. Furthermore, the proposed inter-modality translation allows the usage of standard unsupervised clustering approaches (e.g., K-means using the Dynamic Time Warping measure) for mono-modal SITS analysis. The effectiveness of the proposed methodology is shown in two use-case scenarios, namely flooding and landslide events, for which a joint acquisition of Sentinel-1 and Sentinel-2 images is performed. Full article

(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing)

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21 pages, 5772 KiB

Open AccessArticle

Novel Higher-Order Clique Conditional Random Field to Unsupervised Change Detection for Remote Sensing Images

by Weiqi Fu, Pan Shao, Ting Dong and Zhewei Liu

Remote Sens. 2022, 14(15), 3651; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153651 - 29 Jul 2022

Cited by 7 | Viewed by 1213

Abstract

Change detection (CD) is one of the most important topics in remote sensing. In this paper, we propose a novel higher-order clique conditional random field model to unsupervised CD for remote sensing images (termed HOC²RF), by defining a higher-order clique potential. The clique potential, constructed based on a well-designed higher-order clique of image objects, takes the interaction between the neighboring objects in both feature and location spaces into account. HOC²RF consists of five principle steps: (1) Two difference images with complementary change information are produced by change vector analysis and using the spectral correlation mapper, which describe changes from the perspective of the vector magnitude and angle, respectively. (2) The fuzzy partition matrix of each difference image is calculated by fuzzy clustering, and the fused partition matrix is obtained by fusing the calculated partition matrices with evidence theory. (3) An object-level map is created by segmenting the difference images with an adaptive morphological reconstruction based watershed algorithm. (4) The energy function of the proposed HOC²RF, composed of unary, pairwise, and higher-order clique potentials, is computed based on the difference images, the fusion partition matrix, and the object-level map. (5) The energy function is minimized by the graph cut algorithm to achieve the binary CD map. The proposed HOC²RF CD approach combines the complementary change information extracted from the perspectives of vector magnitude and angle, and synthetically exploits the pixel-level and object-level spatial correlation of images. The main contributions of this article include: (1) proposing the idea of using the interaction between neighboring objects in both feature and location spaces to enhance the CD performance; and (2) presenting a method to construct a higher-order clique of objects, developing a higher-order clique potential function, and proposing a novel CD method HOC²RF. In the experiments on three real remote sensing images, the Kappa coefficient/overall accuracy values of the proposed HOC²RF are 0.9655/0.9967, 0.9518/0.9910, and 0.7845/0.9651, respectively, which are superior to some state-of-the-art CD methods. The experimental results confirm the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing)

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23 pages, 4472 KiB

Open AccessArticle

Shuffle-CDNet: A Lightweight Network for Change Detection of Bitemporal Remote-Sensing Images

by Fengzhi Cui and Jie Jiang

Remote Sens. 2022, 14(15), 3548; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153548 - 24 Jul 2022

Cited by 5 | Viewed by 2158

Abstract

Change detection is an important task in remote-sensing image analysis. With the widespread development of deep learning in change detection, most of the current methods improve detection performance by making the network deeper and wider, but ignore the inference time and computational costs of the network. Therefore, this paper proposes a lightweight change-detection network called Shuffle-CDNet. It accepts the six-channel image that concatenates the bitemporal images by channel as the input, and it adopts the backbone network with channel shuffle operation and depthwise separable convolution layers. The classifier uses a lightweight atrous spatial pyramid pooling (Light-ASPP) module to reduce computational costs. The edge-information feature extracted by a lightweight branch is integrated with the shallow and deep features extracted by the backbone network, and the spatial and channel attention mechanisms are introduced to enhance the expression of features. At the same time, logit knowledge distillation and data augmentation techniques are used in the training phase to improve detection performance. Experimental results showed that the proposed method achieves a better balance in computational efficiency and detection performance compared with other advanced methods. Full article

(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing)

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32 pages, 8207 KiB

Open AccessArticle

Hybrid Variability Aware Network (HVANet): A Self-Supervised Deep Framework for Label-Free SAR Image Change Detection

by Jian Wang, Yinghua Wang and Hongwei Liu

Remote Sens. 2022, 14(3), 734; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030734 - 04 Feb 2022

Cited by 7 | Viewed by 2048

Abstract

Synthetic aperture radar (SAR) image change detection (CD) aims to automatically recognize changes over the same geographic region by comparing prechange and postchange SAR images. However, the detection performance is usually subject to several restrictions and problems, including the absence of labeled SAR samples, inherent multiplicative speckle noise, and class imbalance. More importantly, for bitemporal SAR images, changed regions tend to present highly variable sizes, irregular shapes, and different textures, typically referred to as hybrid variabilities, further bringing great difficulties to CD. In this paper, we argue that these internal hybrid variabilities can also be used for learning stronger feature representation, and we propose a hybrid variability aware network (HVANet) for completely unsupervised label-free SAR image CD by taking inspiration from recent developments in deep self-supervised learning. First, since different changed regions may exhibit hybrid variabilities, it is necessary to enrich distinguishable information within the input features. To this end, in shallow feature extraction, we generalize the traditional spatial patch (SP) feature to allow for each pixel in bitemporal images to be represented at diverse scales and resolutions, called extended SP (ESP). Second, with the carefully customized ESP features, HVANet performs local spatial structure information extraction and multiscale–multiresolution (MS-MR) information encoding simultaneously through a local spatial stream and a scale-resolution stream, respectively. Intrinsically, HVANet projects the ESP features into a new high-level feature space, where the change identification becomes easier. Third, to train the framework effectively, a self-supervision layer is attached to the top of the HVANet to enable the two-stream feature learning and recognition of changed pixels in the corresponding feature space, in a self-supervised manner. Experimental results on three low/medium-resolution SAR datasets demonstrate the effectiveness and superiority of the proposed framework in unsupervised SAR CD tasks. Full article

(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing)

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18 pages, 5856 KiB

Open AccessArticle

Feature Decomposition-Optimization-Reorganization Network for Building Change Detection in Remote Sensing Images

by Yuanxin Ye, Liang Zhou, Bai Zhu, Chao Yang, Miaomiao Sun, Jianwei Fan and Zhitao Fu

Remote Sens. 2022, 14(3), 722; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030722 - 03 Feb 2022

Cited by 10 | Viewed by 2441

Abstract

Building change detection plays an imperative role in urban construction and development. Although the deep neural network has achieved tremendous success in remote sensing image building change detection, it is still fraught with the problem of generating broken detection boundaries and separation of dense buildings, which tends to produce saw-tooth boundaries. In this work, we propose a feature decomposition-optimization-reorganization network for building change detection. The main contribution of the proposed network is that it performs change detection by respectively modeling the main body and edge features of buildings, which is based on the characteristics that the similarity between the main body pixels is strong but weak between the edge pixels. Firstly, we employ a siamese ResNet structure to extract dual-temporal multi-scale difference features on the original remote sensing images. Subsequently, a flow field is built to separate the main body and edge features. Thereafter, a feature optimization module is designed to refine the main body and edge features using the main body and edge ground truth. Finally, we reorganize the optimized main body and edge features to obtain the output results. These constitute a complete end-to-end building change detection framework. The publicly available building dataset LEVIR-CD is employed to evaluate the change detection performance of our network. The experimental results show that the proposed method can accurately identify the boundaries of changed buildings, and obtain better results compared with the current state-of-the-art methods based on the U-Net structure or by combining spatial-temporal attention mechanisms. Full article

(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing)

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18 pages, 13427 KiB

Open AccessArticle

Feature-Based Approach to Change Detection of Small Objects from High-Resolution Satellite Images

by Junghoon Seo, Wonkyu Park and Taejung Kim

Remote Sens. 2022, 14(3), 462; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030462 - 19 Jan 2022

Cited by 3 | Viewed by 2277

Abstract

This paper proposes a new approach to small-object change detection from high-resolution satellite images. We propose using feature points that can be quickly extracted from satellite images as a suitable unit of change for small objects and to reduce false alarms. We can perform feature-based change detection by extracting features from previous and recent images and by estimating change based on change magnitude of the features. We estimate the magnitude by calculating pixel-based change magnitude, and counting the ratio of changed pixels around the extracted features. We apply feature matching and determine matched features as unchanged ones. The remaining feature points are judged as changed or unchanged based on their change magnitude. We tested our approach with three Kompsat-3A image sets with a ground sampling distance of 50 cm. We showed that our approach outperformed the pixel-based approach by producing a higher precision of 88.7% and an accuracy of 86.1% at a fixed false alarm rate of 10%. Our approach is unique in the sense that the feature-based approach applying computer vision methods is newly proposed for change detection. We showed that our feature-based approach was less noisy than pixel-based approaches. We also showed that our approach could compensate for the disadvantages of supervised object-based approaches by successfully reducing the number of change candidates. Our approach, however, could not handle featureless objects, and may increase the number of undetected objects. Future studies will handle this issue by devising more intelligent schemes for merging pixel-based and feature-based change detection results. Full article

(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing)

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17 pages, 5183 KiB

Open AccessArticle

Change Detection for Heterogeneous Remote Sensing Images with Improved Training of Hierarchical Extreme Learning Machine (HELM)

by Te Han, Yuqi Tang, Xin Yang, Zefeng Lin, Bin Zou and Huihui Feng

Remote Sens. 2021, 13(23), 4918; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234918 - 03 Dec 2021

Cited by 10 | Viewed by 1969

Abstract

To solve the problems of susceptibility to image noise, subjectivity of training sample selection, and inefficiency of state-of-the-art change detection methods with heterogeneous images, this study proposes a post-classification change detection method for heterogeneous images with improved training of hierarchical extreme learning machine (HELM). After smoothing the images to suppress noise, a sample selection method is defined to train the HELM for each image, in which the feature extraction is respectively implemented for heterogeneous images and the parameters need not be fine-tuned. Then, the multi-temporal feature maps extracted from the trained HELM are segmented to obtain classification maps and then compared to generate a change map with changed types. The proposed method is validated experimentally by using one set of synthetic aperture radar (SAR) images obtained from Sentinel-1, one set of optical images acquired from Google Earth, and two sets of heterogeneous SAR and optical images. The results show that compared to state-of-the-art change detection methods, the proposed method can improve the accuracy of change detection by more than 8% in terms of the kappa coefficient and greatly reduce run time regardless of the type of images used. Such enhancement reflects the robustness and superiority of the proposed method. Full article

(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing)

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40 pages, 27651 KiB

Open AccessArticle

Looking for Change? Roll the Dice and Demand Attention

by Foivos I. Diakogiannis, François Waldner and Peter Caccetta

Remote Sens. 2021, 13(18), 3707; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183707 - 16 Sep 2021

Cited by 45 | Viewed by 3602

Abstract

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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15 pages, 4935 KiB

Open AccessTechnical Note

Change Detection for High-Resolution Remote Sensing Images Based on a Multi-Scale Attention Siamese Network

by Jiankang Li, Shanyou Zhu, Yiyao Gao, Guixin Zhang and Yongming Xu

Remote Sens. 2022, 14(14), 3464; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143464 - 19 Jul 2022

Cited by 8 | Viewed by 2391

Abstract

To address the problems in remote sensing image change detection such as missed detection of features at different scales and incomplete region detection, this paper proposes a high-resolution remote sensing image change detection model (Multi-scale Attention Siamese Network, MASNet) based on a Siamese network and multi-scale attention mechanism. The MASNet model took the Siamese structure of the ResNet-50 network to extract features of different simultaneous images and then applied the attention module to feature maps of different scales to generate multi-scale feature representations. Meanwhile, an improved contrastive loss function was adopted to enhance the learning of change features and improving the imbalance problem between unchanged and changed samples. Furthermore, to address the current time-consuming and laborious phenomenon of manually annotating datasets, we provided a change detection dataset from Yunnan Province in China (YNCD) that contains 1540 pairs of 256 × 256 bi-temporal images with a spatial resolution of 1 m. Then, model training and change detection applications were studied by expanding a small number of experimental area samples into the existing public datasets. The results showed that the overall accuracy of the MASNet model for change detection in the experimental area is 95.34%, precision rate is 79.78%, recall rate is 81.52%, and F1 score is 80.64%, which are better than those of six comparative models (FC-EF, FC-Siam-Diff, FC-Siam-Conc, PAN, MANet, and STANet). This verifies the effectiveness of the MASNet model as well as the feasibility of change detection by expanding existing public datasets. Full article

(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing)

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