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Image Fusion in Remote Sensing: An Overview and Meta Analysis

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arXiv 2024年

作者： Albanwan, Hessah Qin, Rongjun Tang, Yang The Geospatial Data Analytics Lab Department of Civil Environment and Geodetic Engineering The Ohio State University Columbus United States The Geospatial Data Analytics Lab Department of Civil Environment and Geodetic Engineering The Department of Electrical and Computer Engineering Translational Data Analytics Institute The Ohio state University Columbus United States

Image fusion in Remote Sensing (RS) has been a consistent demand due to its ability to turn raw images of different resolutions, sources, and modalities into accurate, complete, and spatio-temporally coherent images. It greatly facilitates downstream applications such as pan-sharpening, change detection, land-cover classification, etc. Yet, image fusion solutions are highly disparate to various remote sensing problems and thus are often narrowly defined in existing reviews as topical applications, such as pan-sharpening, and spatial-temporal image fusion. Considering that image fusion can be theoretically applied to any gridded data through pixel-level operations, in this paper, we expanded its scope by comprehensively surveying relevant works with a simple taxonomy: 1) many-to-one image fusion;2) many-to-many image fusion. This simple taxonomy defines image fusion as a mapping problem that turns either a single or a set of images into another single or set of images, depending on the desired coherence, e.g., spectral, spatial/resolution coherence, etc. We show that this simple taxonomy, despite the significant modality difference it covers, can be presented by a conceptually easy framework. In addition, we provide a meta-analysis to review the major papers studying the various types of image fusion and their applications over the years (from the 1980s to date), covering 5,926 peer-reviewed papers. Finally, we discuss the main benefits and emerging challenges to provide open research directions and potential future works. Copyright © 2024, The Authors. All rights reserved.

关键词： Image fusion

On the Transferability of Learning Models for Semantic Segmentation for Remote Sensing data

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arXiv 2023年

作者： Qin, Rongjun Zhang, Guixiang Tang, Yang The Geospatial Data Analytics Lab Department of Civil Environment and Geodetic Engineering The Department of Electrical and Computer Engineering Translational Data Analytics Institute The Ohio state University Columbus United States The Geospatial Data Analytics Lab Department of Electrical and Computer Engineering The Ohio State University Columbus United States The Geospatial Data Analytics Lab Department of Civil Environment and Geodetic Engineering The Ohio State University Columbus United States

Recent deep learning-based methods outperform traditional learning methods on remote sensing (RS) semantic segmentation/classification tasks. However, they require large training datasets and are generally known for lack of transferability due to the highly disparate RS image content across different geographical regions. Yet, there is no comprehensive analysis of their transferability, i.e., to which extent a model trained on a source domain can be readily applicable to a target domain. Therefore, in this paper, we aim to investigate the raw transferability of traditional and deep learning (DL) models, as well as the effectiveness of domain adaptation (DA) approaches in enhancing the transferability of the DL models (adapted transferability). By utilizing four highly diverse RS datasets, we train six models with and without three DA approaches to analyze their transferability between these datasets quantitatively. Furthermore, we developed a straightforward method to quantify the transferability of a model using the spectral indices as a medium and have demonstrated its effectiveness in evaluating the model transferability at the target domain when the labels are unavailable. Our experiments yield several generally important yet not well-reported observations regarding the raw and adapted transferability. Moreover, our proposed label-free transferability assessment method is validated to be better than posterior model confidence. The findings can guide the future development of generalized RS learning models. The trained models are released under this link: https://***/GDAOSU/Transferability-Remote-Sensing. Copyright © 2023, The Authors. All rights reserved.

关键词： Remote sensing

Select-and-Combine (SAC): A Novel Multi-Stereo Depth Fusion Algorithm for Point Cloud Generation via Efficient Local Markov Netlets

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arXiv 2023年

作者： Elhashash, Mostafa Qin, Rongjun Geospatial Data Analytics Lab Department of Civil Environment and Geodetic Engineering Department of Electrical and Computer Engineering Translational Data Analytics Institute The Ohio state University Columbus United States

Many practical systems for image-based surface reconstruction employ a stereo/multi-stereo paradigm, due to its ability to scale for large scenes and its ease of implementation for out-of-core operations. In this process, multiple and abundant depth maps from stereo matching must be combined and fused into a single, consistent, and clean point cloud. However, the noises and outliers caused by stereo matching and the heterogenous geometric errors of the poses present a challenge for existing fusion algorithms, since they mostly assume Gaussian errors and predict fused results based on data from local spatial neighborhoods, which may inherit uncertainties from multiple depths resulting in lowered accuracy. In this paper, we propose a novel depth fusion paradigm, that instead of numerically fusing points from multiple depth maps, selects the best depth map per point, and combines them into a single and clean point cloud. This paradigm, called select-and-combine (SAC), is achieved through modeling the point level fusion using local Markov Netlets, a micro-network over point across neighboring views for depth/view selection, followed by a Netlets collapse process for point combination. The Markov Netlets are optimized such that they can inherently leverage spatial consistencies among depth maps of neighboring views, thus they can address errors beyond Gaussian ones. Our experiment results show that our approach outperforms existing depth fusion approaches by increasing the F1 score that considers both accuracy and completeness by 2.07% compared to the best existing method. Finally, our approach generates clearer point clouds that are 18% less redundant while with a higher accuracy before fusion. Copyright © 2023, The Authors. All rights reserved.

关键词： 3D modeling

An Adaptive and Image-guided Fusion for Stereo Satellite Image Derived Digital Surface Models

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Journal of Geodesy and Geoinformation Science 2022年第4期5卷 1-9页

作者： Hessah ALBANWAN Rongjun QIN Department of Civil Engineering Kuwait UniversityKuwait 72504Kuwait Geospatial Data Analytics Lab The Ohio State UniversityColumbus 43210USA Department of Civil Environmental and Geodetic EngineeringThe Ohio State UniversityColumbus 43210USA Department of Electrical and Computer Engineering The Ohio State UniversityColumbus 43210USA Translational Data Analytics Institute The Ohio State UniversityColumbus 43210USA

The accuracy of Digital Surface Models(DSMs)generated using stereo matching methods varies due to the varying acquisition conditions and configuration parameters of stereo *** has been a good practice to fuse these DSMs generated from various stereo pairs to achieve enhanced,in which multiple DSMs are combined through computational approaches into a single,more accurate,and complete ***,accurately characterizing detailed objects and their boundaries still present a challenge since most boundary-ware fusion methods still struggle to achieve sharpened depth discontinuities due to the averaging effects of different ***,we propose a simple and efficient adaptive image-guided DSM fusion method that applies k-means clustering on small patches of the orthophoto to guide the pixel-level fusion adapted to the most consistent and relevant elevation *** experiment results show that our proposed method has outperformed comparing methods in accuracy and the ability to preserve sharpened depth edges.

关键词： Digital Surface Model(DSM) DSM Fusion adaptive fusion satellite stereo images

A volumetric change detection framework using UAV oblique photogrammetry–a case study of ultra-high-resolution monitoring of progressive building collapse

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International Journal of Digital Earth 2021年第11期14卷 1705-1720页

作者： Ningli Xu Debao Huang Shuang Song Xiao Ling Chris Strasbaugh Alper Yilmaz Halil Sezen Rongjun Qin Geospatial Data Analytics Lab The Ohio State UniversityColumbusOHUSA Photogrammetric Computer Vision Lab The Ohio State UniversityColumbusOHUSA Department of Civil Environmental and Geodetic EngineeringThe Ohio State UniversityColumbusOHUSA Department of Electrical and Computer Engineering The Ohio State UniversityColumbusOHUSA Translational Data Analytics Institute The Ohio State UniversityColumbusOHUSA Engineering Technology Services The Ohio State UniversityColumbusOHUSA

In this paper,we present a case study that performs an unmanned aerial vehicle(UAV)based fine-scale 3D change detection and monitoring of progressive collapse performance of a building during a demolition ***-temporal oblique photogrammetry images are collected with 3D point clouds generated at different stages of the *** geometric accuracy of the generated point clouds has been evaluated against both airborne and terrestrial LiDAR point clouds,achieving an average distance of 12 cm and 16 cm for roof and façade *** propose a hierarchical volumetric change detection framework that unifies multi-temporal UAV images for pose estimation(free of ground control points),reconstruction,and a coarse-to-fine 3D density change *** work has provided a solution capable of addressing change detection on full 3D time-series datasets where dramatic scene content changes are presented *** change detection results on the building demolition event have been evaluated against the manually marked ground-truth changes and have achieved an F-1 score varying from 0.78 to 0.92,with consistently high precision(0.92–0.99).Volumetric changes through the demolition progress are derived from change detection and have been shown to favorably reflect the qualitative and quantitative building demolition progression.

关键词： 3D change detection multitemporal data registration oblique photogrammetry

Point Cloud Registration for LiDAR and Photogrammetric data: a Critical Synthesis and Performance Analysis on Classic and Deep Learning Algorithms

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arXiv 2023年

作者： Xu, Ningli Qin, Rongjun Song, Shuang Geospatial Data Analytics Lab The Ohio State University Columbus United States Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus United States Department of Electrical and Computer Engineering The Ohio State University Columbus United States Translational Data Analytics Institute The Ohio State University Columbus United States

Three-dimensional (3D) point cloud registration is a fundamental step for many 3D modeling and mapping applications. Existing approaches are highly disparate in the data source, scene complexity, and application, therefore the current practices in various point cloud registration tasks are still ad-hoc processes. Recent advances in computer vision and deep learning have shown promising performance in estimating rigid/similarity transformation between unregistered point clouds of complex objects and scenes. However, their performances are mostly evaluated using a limited number of datasets from a single sensor (e.g. Kinect or RealSense cameras), lacking a comprehensive overview of their applicability in photogrammetric 3D mapping scenarios. In this work, we provide a comprehensive review of the state-of-the-art (SOTA) point cloud registration methods, where we analyze and evaluate these methods using a diverse set of point cloud data from indoor to satellite sources. The quantitative analysis allows for exploring the strengths, applicability, challenges, and future trends of these methods. In contrast to existing analysis works that introduce point cloud registration as a holistic process, our experimental analysis is based on its inherent two-step process to better comprehend these approaches including feature/keypoint-based initial coarse registration and dense fine registration through cloud-to-cloud (C2C) optimization. More than ten methods, including classic handcrafted, deep-learning-based feature correspondence, and robust C2C methods were tested. We observed that the success rate of most of the algorithms are fewer than 40% over the datasets we tested and there are still are large margin of improvement upon existing algorithms concerning 3D sparse corresopondence search, and the ability to register point clouds with complex geometry and occlusions. With the evaluated statistics on three datasets, we conclude the best-performing methods for each step and provi

关键词： Iterative methods

Deep Learning Meets Satellite Images – An Evaluation on Handcrafted and Learning-based Features for Multi-date Satellite Stereo Images

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arXiv 2024年

作者： Song, Shuang Morelli, Luca Wu, Xinyi Qin, Rongjun Albanwan, Hessah Remondino, Fabio Geospatial Data Analytics Lab The Ohio State University Columbus United States Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus United States Department of Electrical and Computer Engineering The Ohio State University Columbus United States Translational Data Analytics Institute The Ohio State University Columbus United States Trento Italy Department of Civil Environmental and Mechanical Engineering University of Trento Italy Civil Engineering Department Kuwait University Kuwait

A critical step in the digital surface models (DSM) generation is feature matching. Off-track (or multi-date) satellite stereo images, in particular, can challenge the performance of feature matching due to spectral distortions between images, long baseline, and wide intersection angles. Feature matching methods have evolved over the years from handcrafted methods (e.g., SIFT) to learning-based methods (e.g., SuperPoint and SuperGlue). In this paper, we compare the performance of different features, also known as feature extraction and matching methods, applied to satellite imagery. A wide range of stereo pairs (~ 500) covering two separate study sites are used. SIFT, as a widely used classic feature extraction and matching algorithm, is compared with seven deep-learning matching methods: SuperGlue, LightGlue, LoFTR, ASpanFormer, DKM, GIM-LightGlue, and GIM-DKM. Results demonstrate that traditional matching methods are still competitive in this age of deep learning, although for particular scenarios learning-based methods are very promising. Copyright © 2024, The Authors. All rights reserved.

关键词： Satellite imagery

Point cloud registration for LiDAR and photogrammetric data: A critical synthesis and performance analysis on classic and deep learning algorithms

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ISPRS Open Journal of Photogrammetry and Remote Sensing 2023年 8卷

Three-dimensional (3D) point cloud registration is a fundamental step for many 3D modeling and mapping applications. Existing approaches are highly disparate in the data source, scene complexity, and application, therefore the current practices in various point cloud registration tasks are still ad-hoc processes. Recent advances in computer vision and deep learning have shown promising performance in estimating rigid/similarity transformation between unregistered point clouds of complex objects and scenes. However, their performances are mostly evaluated using a limited number of datasets from a single sensor (e.g. Kinect or RealSense cameras), lacking a comprehensive overview of their applicability in photogrammetric 3D mapping scenarios. In this work, we provide a comprehensive review of the state-of-the-art (SOTA) point cloud registration methods, where we analyze and evaluate these methods using a diverse set of point cloud data from indoor to satellite sources. The quantitative analysis allows for exploring the strengths, applicability, challenges, and future trends of these methods. In contrast to existing analysis works that introduce point cloud registration as a holistic process, our experimental analysis is based on its inherent two-step process to better comprehend these approaches including feature/keypoint-based initial coarse registration and dense fine registration through cloud-to-cloud (C2C) optimization. More than ten methods, including classic hand-crafted, deep-learning-based feature correspondence, and robust C2C methods were tested. We observed that the success rate of most of the algorithms are fewer than 40% over the datasets we tested and there are still are large margin of improvement upon existing algorithms concerning 3D sparse corresopondence search, and the ability to register point clouds with complex geometry and occlusions. With the evaluated statistics on three datasets, we conclude the best-performing methods for each step and prov

关键词： Deep learning Iterative closest point algorithm Point cloud registration Terrestrial laser scanning

A Review of Mobile Mapping Systems: From Sensors to Applications

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arXiv 2022年

作者： Elhashash, Mostafa Albanwan, Hessah Qin, Rongjun Geospatial Data Analytics Lab The Ohio State University Columbus United States Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus United States Department of Electrical and Computer Engineering The Ohio State University Columbus United States Translational Data Analytics Institute The Ohio State University Columbus United States

The evolution of mobile mapping systems (MMSs) has gained more attention in the past few decades. MMSs have been widely used to provide valuable assets in different applications. This has been facilitated by the wide availability of low-cost sensors, the advances in computational resources, the maturity of the mapping algorithms, and the need for accurate and on-demand geographic information system (GIS) data and digital maps. Many MMSs combine hybrid sensors to provide a more informative, robust, and stable solution by complementing each other. In this paper, we present a comprehensive review of the modern MMSs by focusing on 1) the types of sensors and platforms, where we discuss their capabilities, limitations, and also provide a comprehensive overview of recent MMS technologies available in the market, 2) highlighting the general workflow to process any MMS data, 3) identifying the different use cases of mobile mapping technology by reviewing some of the common applications, and 4) presenting a discussion on the benefits, challenges, and share our views on the potential research directions. Copyright © 2022, The Authors. All rights reserved.

关键词： Conformal mapping