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Target Recognition and Change Detection for High-Resolution Remote Sensing Images

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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: 15 April 2025 | Viewed by 13797

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Special Issue Editors

Prof. Dr. Chen Wu

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Guest Editor

State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China
Interests: multi-temporal image processing and change detection; hyper-spectral image processing; high-resolution image understanding; satellite video tracking; multi-source data fusion; machine learning; computer vision; urban remote sensing

Dr. Pengyuan Lv

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Guest Editor

School of Information Engineering, Ningxia University, Yinchuan 750021, China
Interests: high spatial resolution remote sensing image classification; change detection; hyperspectral remote sensing image interpretation; machine learning

Dr. Naoto Yokoya

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Guest Editor

Department of Complexity Science and Engineering, The University of Tokyo, Chiba-ken 277-8561, Japan
Interests: image processing; data fusion; machine learning for understanding remote sensing images; applications to disaster management and environmental monitoring
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Wen Yang

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Guest Editor

School of Electronic Information, Wuhan University, Wuhan 430072, China
Interests: objection detection and recognition; multimodal image registration; cross-modal geo-localization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The fast development of remote sensing platforms brings further improvement in the resolution of remote sensing images. High-resolution remote sensing images contain more detailed spatial, spectral, and temporal information of ground landscapes. Recognizing the targets and the changes from multi-source high-resolution remote sensing data has becomes an important topic for Earth observation techniques in many applications. However, this task still encounters several challenges: (1) From the aspect of the images: though high-resolution images make it possible to monitor more types of targets and changes with precise boundaries, the problem of spatial and spectral variability caused by different acquiring conditions (illumination, angle, atmosphere) and sensor characteristics is also severe. (2) From the aspect of the landscapes: the targets on the ground inherently show more complex shapes and higher diversities, which makes it difficult to model their information by traditional methods. (3) From the aspect of applications: manual labelling for training and verifying with high time- and labour-costs, has limited the universality of applying high-resolution remote sensing data to multiple fields. Therefore, it is a very interesting and crucial topic.

This Special Issue aims to focus on discussing the theoretical frontiers and technical problems in target recognition and change detection for high-resolution remote sensing images and provide a platform for researchers to show their recent contributions.

Target recognition and change detection with high spatial, spectral, and temporal resolution remote sensing images.
Advanced interpretation of high-resolution images by unsupervised, semi-supervised, weakly supervised, and self-supervised mechanisms.
Datasets and benchmarks for target recognition and change detection with high-resolution remote sensing images.
Advances in machine learning and deep learning techniques for high-resolution remote sensing image processing.
The applications of high-resolution remote sensing data in various fields.

Prof. Dr. Chen Wu
Dr. Pengyuan Lv
Dr. Naoto Yokoya
Prof. Dr. Wen Yang
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.

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Keywords

target recognition
change detection
high-resolution remote sensing
deep learning
machine learning

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Published Papers (10 papers)

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Research

22 pages, 5345 KiB

Open AccessArticle

Building Change Detection Network Based on Multilevel Geometric Representation Optimization Using Frame Fields

by Fachuan He, Hao Chen, Shuting Yang and Zhixiang Guo

Remote Sens. 2024, 16(22), 4223; https://doi.org/10.3390/rs16224223 - 13 Nov 2024

Viewed by 725

Abstract

To address the challenges of accurately segmenting irregular building boundaries in complex urban environments faced by existing remote sensing change detection methods, this paper proposes a building change detection network based on multilevel geometric representation optimization using frame fields called BuildingCDNet. The proposed method employs a multi-scale feature aggregation encoder–decoder architecture, leveraging contextual information to capture the characteristics of buildings of varying sizes in the imagery. Cross-attention mechanisms are incorporated to enhance the feature correlations between the change pairs. Additionally, the frame field is introduced into the network to model the complex geometric structure of the building target. By learning the local orientation information of the building structure, the frame field can effectively capture the geometric features of complex building features. During the training process, a multi-task learning strategy is used to align the predicted frame field with the real building outline, while learning the overall segmentation, edge outline, and corner point features of the building. This improves the accuracy of the building polygon representation. Furthermore, a discriminative loss function is constructed through multi-task learning to optimize the polygonal structured information of the building targets. The proposed method achieves state-of-the-art results on two commonly used datasets. Full article

(This article belongs to the Special Issue Target Recognition and Change Detection for High-Resolution Remote Sensing Images)

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20 pages, 3298 KiB

Open AccessArticle

Deep Hybrid Fusion Network for Inverse Synthetic Aperture Radar Ship Target Recognition Using Multi-Domain High-Resolution Range Profile Data

by Jie Deng and Fulin Su

Remote Sens. 2024, 16(19), 3701; https://doi.org/10.3390/rs16193701 - 4 Oct 2024

Viewed by 698

Abstract

Most existing target recognition methods based on high-resolution range profiles (HRRPs) use data from only one domain. However, the information contained in HRRP data from different domains is not exactly the same. Therefore, in the context of inverse synthetic aperture radar (ISAR), this paper proposes an advanced deep hybrid fusion network to utilize HRRP data from different domains for ship target recognition. First, the proposed network simultaneously processes time-domain HRRP and its corresponding time–frequency (TF) spectrogram through two branches to obtain initial features from the two HRRP domains. Next, a feature alignment module is used to make the fused features more discriminative regarding the target. Finally, a decision fusion module is designed to further improve the model’s prediction performance. We evaluated our approach using both simulated and measured data, encompassing ten different ship target types. Our experimental results on the simulated and measured datasets showed an improvement in recognition accuracy of at least 4.22% and 2.82%, respectively, compared to using single-domain data. Full article

(This article belongs to the Special Issue Target Recognition and Change Detection for High-Resolution Remote Sensing Images)

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18 pages, 5824 KiB

Open AccessArticle

Feature-Selection-Based Unsupervised Transfer Learning for Change Detection from VHR Optical Images

by Qiang Chen, Peng Yue, Yingjun Xu, Shisong Cao, Lei Zhou, Yang Liu and Jianhui Luo

Remote Sens. 2024, 16(18), 3507; https://doi.org/10.3390/rs16183507 - 21 Sep 2024

Viewed by 760

Abstract

Accurate understanding of urban land use change information is of great significance for urban planning, urban monitoring, and disaster assessment. The use of Very-High-Resolution (VHR) remote sensing images for change detection on urban land features has gradually become mainstream. However, most existing transfer learning-based change detection models compute multiple deep image features, leading to feature redundancy. Therefore, we propose a Transfer Learning Change Detection Model Based on Change Feature Selection (TL-FS). The proposed method involves using a pretrained transfer learning model framework to compute deep features from multitemporal remote sensing images. A change feature selection algorithm is then designed to filter relevant change information. Subsequently, these change features are combined into a vector. The Change Vector Analysis (CVA) is employed to calculate the magnitude of change in the vector. Finally, the Fuzzy C-Means (FCM) classification is utilized to obtain binary change detection results. In this study, we selected four VHR optical image datasets from Beijing-2 for the experiment. Compared with the Change Vector Analysis and Spectral Gradient Difference, the TL-FS method had maximum increases of 26.41% in the F1-score, 38.04% in precision, 29.88% in recall, and 26.15% in the overall accuracy. The results of the ablation experiments also indicate that TL-FS could provide clearer texture and shape detections for dual-temporal VHR image changes. It can effectively detect complex features in urban scenes. Full article

(This article belongs to the Special Issue Target Recognition and Change Detection for High-Resolution Remote Sensing Images)

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20 pages, 3402 KiB

Open AccessArticle

A CNN- and Transformer-Based Dual-Branch Network for Change Detection with Cross-Layer Feature Fusion and Edge Constraints

by Xiaofeng Wang, Zhongyu Guo and Ruyi Feng

Remote Sens. 2024, 16(14), 2573; https://doi.org/10.3390/rs16142573 - 13 Jul 2024

Cited by 2 | Viewed by 1333

Abstract

Change detection aims to identify the difference between dual-temporal images and has garnered considerable attention over the past decade. Recently, deep learning methods have shown robust feature extraction capabilities and have achieved improved detection results; however, they exhibit limitations in preserving clear boundaries for the identified regions, which is attributed to the inadequate contextual information aggregation capabilities of feature extraction, and fail to adequately constrain the delineation of boundaries. To address this issue, a novel dual-branch feature interaction backbone network integrating the CNN and Transformer architectures to extract pixel-level change information was developed. With our method, contextual feature aggregation can be achieved by using a cross-layer feature fusion module, and a dual-branch upsampling module is employed to incorporate both spatial and channel information, enhancing the precision of the identified change areas. In addition, a boundary constraint is incorporated, leveraging an MLP module to consolidate fragmented edge information, which increases the boundary constraints within the change areas and minimizes boundary blurring effectively. Quantitative and qualitative experiments were conducted on three benchmarks, including LEVIR-CD, WHU Building, and the xBD natural disaster dataset. The comprehensive results show the superiority of the proposed method compared with previous approaches. Full article

(This article belongs to the Special Issue Target Recognition and Change Detection for High-Resolution Remote Sensing Images)

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21 pages, 6147 KiB

Open AccessArticle

SDRnet: A Deep Fusion Network for ISAR Ship Target Recognition Based on Feature Separation and Weighted Decision

by Jie Deng and Fulin Su

Remote Sens. 2024, 16(11), 1920; https://doi.org/10.3390/rs16111920 - 27 May 2024

Cited by 1 | Viewed by 1003

Abstract

Existing methods for inverse synthetic aperture radar (ISAR) target recognition typically rely on a single high-resolution radar signal type, such as ISAR images or high-resolution range profiles (HRRPs). However, ISAR images and HRRP data offer representations of targets across different aspects, each containing valuable information crucial for radar target recognition. Moreover, the process of generating ISAR images inherently facilitates the acquisition of HRRP data, ensuring timely data collection. Therefore, to fully leverage the different information from both HRRP data and ISAR images and enhance ISAR ship target recognition performance, we propose a novel deep fusion network named the Separation-Decision Recognition network (SDRnet). First, our approach employs a convolutional neural network (CNN) to extract initial feature vectors from ISAR images and HRRP data. Subsequently, a feature separation module is employed to derive a more robust target representation. Finally, we introduce a weighted decision module to enhance overall predictive performance. We validate our method using simulated and measured data containing ten categories of ship targets. The experimental results confirm the effectiveness of our approach in improving ISAR ship target recognition. Full article

(This article belongs to the Special Issue Target Recognition and Change Detection for High-Resolution Remote Sensing Images)

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32 pages, 10464 KiB

Open AccessArticle

The Cost of Urban Renewal: Annual Construction Waste Estimation via Multi-Scale Target Information Extraction and Attention-Enhanced Networks in Changping District, Beijing

by Lei Huang, Shaofu Lin, Xiliang Liu, Shaohua Wang, Guihong Chen, Qiang Mei and Zhe Fu

Remote Sens. 2024, 16(11), 1889; https://doi.org/10.3390/rs16111889 - 24 May 2024

Cited by 1 | Viewed by 1585

Abstract

Construction waste is an inevitable byproduct of urban renewal, causing severe pressure on the environment, health, and ecology. Accurately estimating the production of construction waste is crucial for assessing the consumption of urban renewal. However, traditional manual estimation methods rely heavily on statistical data and historical experience, which lack flexibility in practical applications and are time-consuming and labor-intensive. In addition, their accuracy and timeliness need to be improved urgently. Fortunately, with the advantages of high-resolution remote sensing images (HRSIs) such as strong timeliness, large amounts of information, and macroscopic observations, they are suitable for the large-scale dynamic change detection of construction waste. However, the existing deep learning models have a relatively poor ability to extract and fuse features for small and multi-scale targets, and it is difficult to deal with irregularly shaped and fragmented detection areas. Therefore, this study proposes a Multi-scale Target Attention-Enhanced Network (MT-AENet), which is used to dynamically track and detect changes in buildings and construction waste disposal sites through HRSIs and accurately estimate the annual production of urban construction waste. The MT-AENet introduces a novel encoder–decoder architecture. In the encoder, ResNet-101 is utilized to extract high-level semantic features. A depthwise separable-atrous spatial pyramid pooling (DS-ASPP) module with different dilation rates is constructed to address insufficient receptive fields, resolving the issue of discontinuous holes when extracting large targets. A dual-attention mechanism module (DAMM) is employed to better preserve positional and channel details. In the decoder, multi-scale feature fusion (MS-FF) is utilized to capture contextual information, integrating shallow and intermediate features of the backbone network, thereby enhancing extraction capabilities in complex scenes. The MT-AENet is used to extract buildings and construction waste at different periods in the study area, and the actual production and landfill volume of construction waste are calculated based on area changes, indirectly measuring the rate of urban construction waste resource conversion. The experimental results in Changping District, Beijing demonstrate that the MT-AENet outperforms existing baseline networks in extracting buildings and construction waste. The results of this study are validated according to government statistical standards, providing a promising direction for efficiently analyzing the consumption of urban renewal. Full article

(This article belongs to the Special Issue Target Recognition and Change Detection for High-Resolution Remote Sensing Images)

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27 pages, 7580 KiB

Open AccessArticle

Change Detection Based on Existing Vector Polygons and Up-to-Date Images Using an Attention-Based Multi-Scale ConvTransformer Network

by Shengli Wang, Yihu Zhu, Nanshan Zheng, Wei Liu, Hua Zhang, Xu Zhao and Yongkun Liu

Remote Sens. 2024, 16(10), 1736; https://doi.org/10.3390/rs16101736 - 14 May 2024

Viewed by 2246

Abstract

Vector polygons represent crucial survey data, serving as a cornerstone of national geographic censuses and forming essential data sources for detecting geographical changes. The timely update of these polygons is vital for governmental decision making and various industrial applications. However, the manual intervention required to update existing vector polygons using up-to-date high-resolution remote sensing (RS) images poses significant challenges and incurs substantial costs. To address this, we propose a novel change detection (CD) method for land cover vector polygons leveraging high-resolution RS images and deep learning techniques. Our approach begins by employing the boundary-preserved masking Simple Linear Iterative Clustering (SLIC) algorithm to segment RS images. Subsequently, an adaptive cropping approach automatically generates an initial sample set, followed by denoising using the efficient Visual Transformer and Class-Constrained Density Peak-Based (EViTCC-DP) method, resulting in a refined training set. Finally, an enhanced attention-based multi-scale ConvTransformer network (AMCT-Net) conducts fine-grained scene classification, integrating change rules and post-processing methods to identify changed vector polygons. Notably, our method stands out by employing an unsupervised approach to denoise the sample set, effectively transforming noisy samples into representative ones without requiring manual labeling, thus ensuring high automation. Experimental results on real datasets demonstrate significant improvements in model accuracy, with accuracy and recall rates reaching 92.08% and 91.34%, respectively, for the Nantong dataset, and 93.51% and 92.92%, respectively, for the Guantan dataset. Moreover, our approach shows great potential in updating existing vector data while effectively mitigating the high costs associated with acquiring training samples. Full article

(This article belongs to the Special Issue Target Recognition and Change Detection for High-Resolution Remote Sensing Images)

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17 pages, 7615 KiB

Open AccessArticle

Instantaneous Frequency Extraction for Nonstationary Signals via a Squeezing Operator with a Fixed-Point Iteration Method

by Zhen Li, Zhaoqi Gao, Fengyuan Sun, Jinghuai Gao and Wei Zhang

Remote Sens. 2024, 16(8), 1412; https://doi.org/10.3390/rs16081412 - 16 Apr 2024

Viewed by 1029

Abstract

The instantaneous frequency (IF) is an important feature for the analysis of nonstationary signals. For IF estimation, the time–frequency representation (TFR)-based algorithm is used in a common class of methods. TFR-based methods always need the representation concentrated around the “true” IFs and the number of components within the signal. In this paper, we propose a novel method to adaptively estimate the IFs of nonstationary signals, even for weak components of the signals. The proposed technique is not based on the TFR: it is based on the frequency estimation operator (FEO), and the short-time Fourier transform (STFT) is used as its basis. As we know, the FRO is an exact estimation of the IF for weak frequency-modulated (FM) signals, but is not appropriate for strong FM modes. Through theoretical derivation, we determine that the fixed points of the FEOwith respect to the frequency are equivalent to the ridge of the STFT spectrum. Furthermore, the IF of the linear chirp signals is just the fixed points of the FEO. Therefore, we apply the fixed-point algorithm to the FEO to realize the precise and reliable estimation of the IF, even for highly FM signals. Finally, the results using synthetic and real signals show the utility of the proposed method for IF estimation and that it is more robust than the compared method. It should be noted that the proposed method employing the FEO only computes the first-order differential of the STFT for the chirp-like signals, while it can provide a result derived using the second-order estimation operator. Moreover, this new method is effective for the IF estimation of weak components within a signal. Full article

(This article belongs to the Special Issue Target Recognition and Change Detection for High-Resolution Remote Sensing Images)

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23 pages, 2284 KiB

Open AccessArticle

MDANet: A High-Resolution City Change Detection Network Based on Difference and Attention Mechanisms under Multi-Scale Feature Fusion

by Shanshan Jiang, Haifeng Lin, Hongjin Ren, Ziwei Hu, Liguo Weng and Min Xia

Remote Sens. 2024, 16(8), 1387; https://doi.org/10.3390/rs16081387 - 14 Apr 2024

Cited by 6 | Viewed by 1554

Abstract

In the domains of geographic information systems and remote sensing image analysis, change detection is vital for examining surface variations in high-resolution remote sensing pictures. However, the intricate texture characteristics and rich details found in high-resolution remote sensing photos are difficult for conventional change detection systems to deal with. Target misdetection, missed detections, and edge blurring are further problems with current deep learning-based methods. This research proposes a high-resolution city change detection network based on difference and attention mechanisms under multi-scale feature fusion (MDANet) to address these issues and improve the accuracy of change detection. First, to extract features from dual-temporal remote sensing pictures, we use the Siamese architecture as the encoder network. The Difference Feature Module (DFM) is employed to learn the difference information between the dual-temporal remote sensing images. Second, the extracted difference features are optimized with the Attention Refinement Module (ARM). The Cross-Scale Fusion Module (CSFM) combines and enhances the optimized attention features, effectively capturing subtle differences in remote sensing images and learning the finer details of change targets. Finally, thorough tests on the BTCDD dataset, LEVIR-CD dataset, and CDD dataset show that the MDANet algorithm performs at a cutting-edge level. Full article

(This article belongs to the Special Issue Target Recognition and Change Detection for High-Resolution Remote Sensing Images)

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18 pages, 1580 KiB

Open AccessArticle

SFDA-CD: A Source-Free Unsupervised Domain Adaptation for VHR Image Change Detection

by Jingxuan Wang and Chen Wu

Remote Sens. 2024, 16(7), 1274; https://doi.org/10.3390/rs16071274 - 4 Apr 2024

Viewed by 1252

Abstract

Deep models may have disappointing performance in real applications due to the domain shifts in data distributions between the source and target domain. Although a few unsupervised domain adaptation methods have been proposed to make the pre-train models effective on target domain datasets, constraints like data privacy, security, and transmission limits restrict access to VHR remote sensing images, making existing unsupervised domain adaptation methods almost ineffective in specific change detection areas. Therefore, we propose a source-free unsupervised domain adaptation change detection structure to complete specific change detection tasks, using only the pre-trained source model and unlabelled target data. The GAN-based source generation component is designed to generate synthetic source data, which, to some extent, reflects the distribution of the source domain. Moreover, these data can be utilised in model knowledge transfer. The model adaptation component facilitates knowledge transfer between models by minimising the differences between deep features, using AAM (Attention Adaptation Module) to extract the difference between high-level features, meanwhile we proposed ISM (Intra-domain Self-supervised Module) to train target model in a self-supervised strategy in order to improve the knowledge adaptation. Our SFDA-CD framework demonstrates superior accuracy over existing unsupervised domain adaptation change detection methods, which has 0.6% cIoU and 1.5% F1 score up in cross-regional tasks and 1.4% cIoU and 1.9% F1 score up in cross-scenario tasks, proving that it can effectively reduce the domain shift between the source and target domains even without access to source data. Additionally, it can facilitate knowledge transfer from the source model to the target model. Full article

(This article belongs to the Special Issue Target Recognition and Change Detection for High-Resolution Remote Sensing Images)

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