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arXiv 2022年

作者： Albanwan, Hessah Qin, Rongjun Geospatial Data Analytics Laboratory The Ohio State University 218B Bolz Hall 2036 Neil Avenue ColumbusOH43210 United States Department of Civil Environmental and Geodetic Engineering The Ohio State University 218B Bolz Hall 2036 Neil Avenue ColumbusOH43210 United States Department of Electrical and Computer Engineering The Ohio State University 205 Dreese Lab 2036 Neil Avenue ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University United States

Deep learning (DL) stereo matching methods gained great attention in remote sensing satellite datasets. However, most of these existing studies conclude assessments based only on a few/single stereo images lacking a systematic evaluation on how robust DL methods are on satellite stereo images with varying radiometric and geometric configurations. This paper provides an evaluation of four DL stereo matching methods through hundreds of multi-date multi-site satellite stereo pairs with varying geometric configurations, against the traditional well-practiced Census-SGM (Semi-global matching), to comprehensively understand their accuracy, robustness, generalization capabilities, and their practical potential. The DL methods include a learning-based cost metric through convolutional neural networks (MC-CNN) followed by SGM, and three end-to-end (E2E) learning models using Geometry and Context Network (GCNet), Pyramid Stereo Matching Network (PSMNet), and LEAStereo. Our experiments show that E2E algorithms can achieve upper limits of geometric accuracies, while may not generalize well for unseen data. The learning-based cost metric and Census-SGM are rather robust and can consistently achieve acceptable results. All DL algorithms are robust to geometric configurations of stereo pairs and are less sensitive in comparison to the Census-SGM, while learning-based cost metrics can generalize on satellite images when trained on different datasets (airborne or ground-view). Copyright © 2022, The Authors. All rights reserved.

关键词： Satellites

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Large-scale and Efficient Texture Mapping Algorithm via Loopy Belief Propagation

arXiv

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arXiv 2023年

作者： Ling, Xiao Qin, Rongjun Geospatial Data Analytics Laboratory The Ohio State University 218B Bolz Hall 2036 Neil Avenue ColumbusOH43210 United States Department of Civil Environmental and Geodetic Engineering The Ohio State University 218B Bolz Hall 2036 Neil Avenue ColumbusOH43210 United States College of Astronautics Nanjing University Of Aeronautics And Astronautics 29 Jiangjun Avenue Jiangning District Jiangsu Nanjing211106 China Department of Electrical and Computer Engineering The Ohio State University 205 Dreese Lab 2036 Neil Avenue ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University United States

Texture mapping as a fundamental task in 3D modeling has been well established for well-acquired aerial assets under consistent illumination, yet it remains a challenge when it is scaled to large datasets with images under varying views and illuminations. A well-performed texture mapping algorithm must be able to efficiently select views, fuse and map textures from these views to mesh models, at the same time, achieve consistent radiometry over the entire model. Existing approaches achieve efficiency either by limiting the number of images to one view per face, or simplifying global inferences to only achieve local color consistency. In this paper, we break this tie by proposing a novel and efficient texture mapping framework that allows the use of multiple views of texture per face, at the same time to achieve global color consistency. The proposed method leverages a loopy belief propagation algorithm to perform an efficient and global-level probabilistic inferences to rank candidate views per face, which enables face-level multi-view texture fusion and blending. The texture fusion algorithm, being non-parametric, brings another advantage over typical parametric post color correction methods, due to its improved robustness to non-linear illumination differences. The experiments on three different types of datasets (i.e. satellite dataset, unmanned-aerial vehicle dataset and close-range dataset) show that the proposed method has produced visually pleasant and texturally consistent results in all scenarios, with an added advantage of consuming less running time as compared to the state of the art methods, especially for large-scale dataset such as satellite-derived models. Copyright © 2023, The Authors. All rights reserved.

关键词： Belief propagation

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A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images—Analysis Unit, Model Scalability and Transferability

arXiv

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arXiv 2022年

作者： Qin, Rongjun Liu, Tao Geospatial Data Analytics Lab The Ohio State University 218B Bolz Hall 2036 Neil Avenue ColumbusOH43210 United States Department of Civil Environmental and Geodetic Engineering The Ohio State University 2070 Neil Avenue ColumbusOH43210 United States Department of Electrical and Computer Engineering The Ohio State University 205 Dreese Labs 2015 Neil Avenue ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University Pomerene Hall 1760 Neil Ave ColumbusOH43210 United States College of Forest Resources and Environmental Science Michigan Technological University 1400 Townsend Drive HoughtonMI49931 United States Ecosystem Science Center Michigan Technological University 1400 Townsend Drive HoughtonMI49931 United States

As an important application in remote sensing, landcover classification remains one of the most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly increasing number of Deep Learning (DL) based landcover methods and training strategies are claimed to be the state-of-the-art, the already fragmented technical landscape of landcover mapping methods has been further complicated. Although there exists a plethora of literature review work attempting to guide researchers in making an informed choice of landcover mapping methods, the articles either focus on the review of applications in a specific area or revolve around general deep learning models, which lack a systematic view of the ever advancing landcover mapping methods. In addition, issues related to training samples and model transferability have become more critical than ever in an era dominated by data-driven approaches, but these issues were addressed to a lesser extent in previous review articles regarding remote sensing classification. Therefore, in this paper, we present a systematic overview of existing methods by starting from learning methods and varying basic analysis units for landcover mapping tasks, to challenges and solutions on three aspects of scalability and transferability with a remote sensing classification focus including (1) sparsity and imbalance of data;(2) domain gaps across different geographical regions;and (3) multi-source and multi-view fusion. We discuss in detail each of these categorical methods and draw concluding remarks in these developments and recommend potential directions for the continued endeavor. Copyright © 2022, The Authors. All rights reserved.

关键词： Remote sensing

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