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3D Scene Reconstruction, Modeling and Analysis Using Remote Sensing

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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 (31 March 2025) | Viewed by 9186

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Special Issue Editors

Dr. Ganchao Liu

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Guest Editor

School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, China
Interests: remote sensing; computer vision

Prof. Dr. Yaxiong Chen

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Guest Editor

School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China
Interests: multimodal remote sensing interpretation

Dr. Qingyu Li

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Guest Editor

Data Science in Earth Observation, Technische Universität München (TUM), 80333 Munich, Germany
Interests: remote sensing image understanding; remote sensing application; urban analysis; deep learning algorithms
Special Issues, Collections and Topics in MDPI journals

Dr. Cong Zhang

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Guest Editor

Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: remote sensing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of remote sensing has witnessed remarkable advancements in recent years, particularly in the domain of 3D scene reconstruction, modeling, and analysis. These developments have opened new possibilities for understanding and interpreting our complex three-dimensional world. However, challenges remain in effectively processing and analyzing the vast amounts of 3D data acquired through various remote sensing techniques. This Special Issue aims to explore cutting-edge methodologies and applications in 3D scene reconstruction, modeling, and analysis using remote sensing data.

This Special Issue seeks to cover a wide range of topics related to 3D scene reconstruction, modeling, and analysis using remote sensing, including but not limited to:

Three-dimensional reconstruction techniques using multi-source remote sensing data fusion;
stereo mapping techniques for high-resolution satellite imagery;
Three-dimensional urban modeling from remote sensing;
Four-dimensional scene reconstruction and change detection using time-series remote sensing data;
High-precision 3D reconstruction combining remote sensing and ground observation data;
Three-dimensional modeling of surface deformation based on SAR interferometry;
Three-dimensional scene understanding from remote sensing images;
Application of semantic segmentation in 3D scene reconstruction from remote sensing images;
Collaborative 3D reconstruction from multi-platform remote sensing data;
Global-scale 3D terrain reconstruction and update using remote sensing data

Dr. Ganchao Liu
Prof. Dr. Yaxiong Chen
Dr. Qingyu Li
Dr. Cong Zhang
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

3D scene reconstruction
remote sensing
multi-source data fusion
deep learning
high-resolution satellite imagery
4D scene reconstruction
SAR interferometry
semantic segmentation
point cloud analysis
global 3D terrain reconstruction

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Published Papers (9 papers)

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Research

30 pages, 14034 KiB

Open AccessArticle

A Novel 3D Point Cloud Reconstruction Method for Single-Pass Circular SAR Based on Inverse Mapping with Target Contour Constraints

by Qiming Zhang, Jinping Sun, Fei Teng, Yun Lin and Wen Hong

Remote Sens. 2025, 17(7), 1275; https://doi.org/10.3390/rs17071275 - 3 Apr 2025

Viewed by 269

Abstract

Circular synthetic aperture radar (CSAR) is an advanced imaging mechanism with three-dimensional (3D) imaging capability, enabling the acquisition of omnidirectional scattering information for observation regions. The existing 3D point cloud reconstruction method for single-pass CSAR is capable of obtaining the 3D scattering points for targets by inversely mapping the projection points in multi-aspect sub-aperture images and subsequently voting on the scattering candidates. However, due to the influence of non-target background points in multi-aspect sub-aperture images, there are several false points in the 3D reconstruction result, which affect the quality of the produced 3D point cloud. In this paper, we propose a novel 3D point cloud reconstruction method for single-pass CSAR based on inverse mapping with target contour constraints. The proposed method can constrain the range and height of inverse mapping by extracting the contour information of targets from multi-aspect sub-aperture CSAR images, which contributes to improving the reconstruction quality of 3D point clouds for targets. The performance of the proposed method was verified based on X-band CSAR measured data sets. Full article

(This article belongs to the Special Issue 3D Scene Reconstruction, Modeling and Analysis Using Remote Sensing)

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26 pages, 10537 KiB

Open AccessArticle

SAMNet++: A Segment Anything Model for Supervised 3D Point Cloud Semantic Segmentation

by Mohsen Shahraki, Ahmed Elamin and Ahmed El-Rabbany

Remote Sens. 2025, 17(7), 1256; https://doi.org/10.3390/rs17071256 - 2 Apr 2025

Viewed by 560

Abstract

Segmentation of 3D point clouds is essential for applications such as environmental monitoring and autonomous navigation, where making accurate distinctions between different classes from high-resolution 3D datasets is critical. Segmenting 3D point clouds often requires a trade-off between preserving spatial information and achieving computational efficiency. In this paper, we present SAMNet++, a hybrid 3D segmentation model that integrates segment anything model (SAM) and adopted PointNet++ in a sequential two-stage pipeline. Firstly, SAM performs an initial unsupervised segmentation, which is then refined using adopted PointNet++ to improve the accuracy. The key innovations of SAMNet++ include its hybrid architecture, which combines SAM’s generalization with PointNet++’s local feature extraction, and a feature refinement strategy that enhances precision while reducing computational overhead. Additionally, SAMNet++ minimizes the reliance on extensive supervised training, while maintaining high accuracy. The proposed model is tested on three urban datasets, which are collected by an unmanned aerial vehicle (UAV). The proposed SAMNet++ model demonstrates high segmentation performance, achieving accuracy, precision, recall, and F1-score values above 0.97 across all classes on our experimental datasets. Furthermore, its mean intersection over union (mIoU) of 86.93% on a public benchmark dataset signifies a more balanced and precise segmentation across all classes, surpassing previous state-of-the-art methods. In addition to its improved accuracy, SAMNet++ showcases remarkable computational efficiency, requiring almost half the processing time of standard PointNet++ and nearly one-sixteenth of the time needed by the original PointNet algorithm. Full article

(This article belongs to the Special Issue 3D Scene Reconstruction, Modeling and Analysis Using Remote Sensing)

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22 pages, 2288 KiB

Open AccessArticle

Central Pixel-Based Dual-Branch Network for Hyperspectral Image Classification

by Dandan Ma, Shijie Xu, Zhiyu Jiang and Yuan Yuan

Remote Sens. 2025, 17(7), 1255; https://doi.org/10.3390/rs17071255 - 2 Apr 2025

Viewed by 402

Abstract

Hyperspectral image classification faces significant challenges in effectively extracting and integrating spectral-spatial features from high-dimensional data. Recent deep learning (DL) methods combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have demonstrated exceptional performance. However, two critical challenges may cause degradation in the classification accuracy of these methods: interference from irrelevant information within the observed region, and the potential loss of useful information due to local spectral variability within the same class. To address these issues, we propose a central pixel-based dual-branch network (CPDB-Net) that synergistically integrates CNN and ViT for robust feature extraction. Specifically, the central spectral feature extraction branch based on CNN serves as a strong prior to reinforce the importance of central pixel features in classification. Additionally, the spatial branch based on ViT incorporates a novel frequency-aware HiLo attention, which can effectively separate high and low frequencies, alleviating the problem of local spectral variability and enhancing the ability to extract global features. Extensive experiments on widely used HSI datasets demonstrate the superiority of our method. Our CPDB-Net achieves the highest overall accuracies of 92.67%, 97.48%, and 95.02% on the Indian Pines, Pavia University, and Houston 2013 datasets, respectively, outperforming recent representative methods and confirming its effectiveness. Full article

(This article belongs to the Special Issue 3D Scene Reconstruction, Modeling and Analysis Using Remote Sensing)

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18 pages, 39910 KiB

Open AccessArticle

DyGS-SLAM: Realistic Map Reconstruction in Dynamic Scenes Based on Double-Constrained Visual SLAM

by Fan Zhu, Yifan Zhao, Ziyu Chen, Chunmao Jiang, Hui Zhu and Xiaoxi Hu

Remote Sens. 2025, 17(4), 625; https://doi.org/10.3390/rs17040625 - 12 Feb 2025

Viewed by 1266

Abstract

Visual SLAM is widely applied in robotics and remote sensing. The fusion of Gaussian radiance fields and Visual SLAM has demonstrated astonishing efficacy in constructing high-quality dense maps. While existing methods perform well in static scenes, they are prone to the influence of dynamic objects in real-world dynamic environments, thus making robust tracking and mapping challenging. We introduce DyGS-SLAM, a Visual SLAM system that employs dual constraints to achieve high-fidelity static map reconstruction in dynamic environments. We extract ORB features within the scene, and use open-world semantic segmentation models and multi-view geometry to construct dual constraints, forming a zero-shot dynamic information elimination module while recovering backgrounds occluded by dynamic objects. Furthermore, we select high-quality keyframes and use them for loop closure detection and global optimization, constructing a foundational Gaussian map through a set of determined point clouds and poses and integrating repaired frames for rendering new viewpoints and optimizing 3D scenes. Experimental results on the TUM RGB-D, Bonn, and Replica datasets, as well as real scenes, demonstrate that our method has excellent localization accuracy and mapping quality in dynamic scenes. Full article

(This article belongs to the Special Issue 3D Scene Reconstruction, Modeling and Analysis Using Remote Sensing)

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23 pages, 2311 KiB

Open AccessArticle

Semi-Supervised Change Detection with Data Augmentation and Adaptive Thresholding for High-Resolution Remote Sensing Images

by Wuxia Zhang, Xinlong Shu, Siyuan Wu and Songtao Ding

Remote Sens. 2025, 17(2), 178; https://doi.org/10.3390/rs17020178 - 7 Jan 2025

Viewed by 984

Abstract

Change detection (CD) is an important research direction in the field of remote sensing, which aims to analyze the changes in the same area over different periods and is widely used in urban planning and environmental protection. While supervised learning methods in change detection have demonstrated substantial efficacy, they are often hindered by the rising costs associated with data annotation. Semi-supervised methods have attracted increasing interest, offering promising results with limited data labeling. These approaches typically employ strategies such as consistency regularization, pseudo-labeling, and generative adversarial networks. However, they usually face the problems of insufficient data augmentation and unbalanced quality and quantity of pseudo-labeling. To address the above problems, we propose a semi-supervised change detection method with data augmentation and adaptive threshold updating (DA-AT) for high-resolution remote sensing images. Firstly, a channel-level data augmentation (CLDA) technique is designed to enhance the strong augmentation effect and improve consistency regularization so as to address the problem of insufficient feature representation. Secondly, an adaptive threshold (AT) is proposed to dynamically adjust the threshold during the training process to balance the quality and quantity of pseudo-labeling so as to optimize the self-training process. Finally, an adaptive class weight (ACW) mechanism is proposed to alleviate the impact of the imbalance between the changed classes and the unchanged classes, which effectively enhances the learning ability of the model for the changed classes. We verify the effectiveness and robustness of the proposed method on two high-resolution remote sensing image datasets, WHU-CD and LEVIR-CD. We compare our method to five state-of-the-art change detection methods and show that it achieves better or comparable results. Full article

(This article belongs to the Special Issue 3D Scene Reconstruction, Modeling and Analysis Using Remote Sensing)

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21 pages, 4833 KiB

Open AccessArticle

An Effective 3D Instance Map Reconstruction Method Based on RGBD Images for Indoor Scene

by Heng Wu, Yanjie Liu, Chao Wang and Yanlong Wei

Remote Sens. 2025, 17(1), 139; https://doi.org/10.3390/rs17010139 - 3 Jan 2025

Cited by 1 | Viewed by 833

Abstract

To enhance the intelligence of robots, constructing accurate object-level instance maps is essential. However, the diversity and clutter of objects in indoor scenes present significant challenges for instance map construction. To tackle this issue, we propose a method for constructing object-level instance maps based on RGBD images. First, we utilize the advanced visual odometer ORB-SLAM3 to estimate the poses of image frames and extract keyframes. Next, we perform semantic and geometric segmentation on the color and depth images of these keyframes, respectively, using semantic segmentation to optimize the geometric segmentation results and address inaccuracies in the target segmentation caused by small depth variations. The segmented depth images are then projected into point cloud segments, which are assigned corresponding semantic information. We integrate these point cloud segments into a global voxel map, updating each voxel’s class using color, distance constraints, and Bayesian methods to create an object-level instance map. Finally, we construct an ellipsoids scene from this map to test the robot’s localization capabilities in indoor environments using semantic information. Our experiments demonstrate that this method accurately and robustly constructs the environment, facilitating precise object-level scene segmentation. Furthermore, compared to manually labeled ellipsoidal maps, generating ellipsoidal maps from extracted objects enables accurate global localization. Full article

(This article belongs to the Special Issue 3D Scene Reconstruction, Modeling and Analysis Using Remote Sensing)

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29 pages, 11116 KiB

Open AccessArticle

Displacement Estimation Performance of a Cost-Effective 2D-LiDAR-Based Retaining Wall Displacement Monitoring System

by Jun-Sang Kim and Young Suk Kim

Remote Sens. 2024, 16(24), 4644; https://doi.org/10.3390/rs16244644 - 11 Dec 2024

Viewed by 859

Abstract

Monitoring the displacement of retaining walls is essential for maintaining their stability. Traditional displacement monitoring by inclinometer is costly and time-consuming, owing to the need for manual measurements. A recently developed 2D-LiDAR-based retaining wall displacement monitoring system offers advantages over traditional methods, such as easy installation and dismantling, as well as the cost-effective monitoring of three-dimensional displacement compared to terrestrial laser scanners (TLSs). However, a previous study did not account for the actual deformation of the retaining wall, potentially compromising the reliability of the displacement estimation. This study aims to assess the displacement estimation performance of the system by using a retaining wall that simulates real-world deformations, considering key parameters related to the displacement estimation algorithm and the quality of point cloud data. Using the multiple model-to-model cloud comparison algorithm and a developed algorithm for filtering duplicate point cloud data, the system’s average performance across various deformation types yielded mean absolute error (MAE),

{M A E}_{D_{m a x}}

, and compound error values of 1.7, 2.2, and 2.0 mm, respectively. The results demonstrate that even a 2D-LiDAR, which has lower precision than a TLS, can effectively monitor retaining wall displacement through the post-processing of point cloud data. Full article

(This article belongs to the Special Issue 3D Scene Reconstruction, Modeling and Analysis Using Remote Sensing)

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24 pages, 47033 KiB

Open AccessArticle

Hybrid Denoising Algorithm for Architectural Point Clouds Acquired with SLAM Systems

by Antonella Ambrosino, Alessandro Di Benedetto and Margherita Fiani

Remote Sens. 2024, 16(23), 4559; https://doi.org/10.3390/rs16234559 - 5 Dec 2024

Viewed by 1206

Abstract

The sudden development of systems capable of rapidly acquiring dense point clouds has underscored the importance of data processing and pre-processing prior to modeling. This work presents the implementation of a denoising algorithm for point clouds acquired with LiDAR SLAM systems, aimed at optimizing data processing and the reconstruction of surveyed object geometries for graphical rendering and modeling. Implemented in a MATLAB environment, the algorithm utilizes an approximate modeling of a reference surface with Poisson’s model and a statistical analysis of the distances between the original point cloud and the reconstructed surface. Tested on point clouds from historically significant buildings with complex geometries scanned with three different SLAM systems, the results demonstrate a satisfactory reduction in point density to approximately one third of the original. The filtering process effectively removed about 50% of the points while preserving essential details, facilitating improved restitution and modeling of architectural and structural elements. This approach serves as a valuable tool for noise removal in SLAM-derived datasets, enhancing the accuracy of architectural surveying and heritage documentation. Full article

(This article belongs to the Special Issue 3D Scene Reconstruction, Modeling and Analysis Using Remote Sensing)

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22 pages, 7929 KiB

Open AccessArticle

Remote Sensing LiDAR and Hyperspectral Classification with Multi-Scale Graph Encoder–Decoder Network

by Fang Wang, Xingqian Du, Weiguang Zhang, Liang Nie, Hu Wang, Shun Zhou and Jun Ma

Remote Sens. 2024, 16(20), 3912; https://doi.org/10.3390/rs16203912 - 21 Oct 2024

Cited by 2 | Viewed by 1638

Abstract

The rapid development of sensor technology has made multi-modal remote sensing data valuable for land cover classification due to its diverse and complementary information. Many feature extraction methods for multi-modal data, combining light detection and ranging (LiDAR) and hyperspectral imaging (HSI), have recognized the importance of incorporating multiple spatial scales. However, effectively capturing both long-range global correlations and short-range local features simultaneously on different scales remains a challenge, particularly in large-scale, complex ground scenes. To address this limitation, we propose a multi-scale graph encoder–decoder network (MGEN) for multi-modal data classification. The MGEN adopts a graph model that maintains global sample correlations to fuse multi-scale features, enabling simultaneous extraction of local and global information. The graph encoder maps multi-modal data from different scales to the graph space and completes feature extraction in the graph space. The graph decoder maps the features of multiple scales back to the original data space and completes multi-scale feature fusion and classification. Experimental results on three HSI-LiDAR datasets demonstrate that the proposed MGEN achieves considerable classification accuracies and outperforms state-of-the-art methods. Full article

(This article belongs to the Special Issue 3D Scene Reconstruction, Modeling and Analysis Using Remote Sensing)

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