3D Reconstruction through Fusion of Cross-View Images

Qin, Rongjun; Song, Shuang; Ling, Xiao; Elhashash, Mostafa

doi:10.5772/intechopen.93099

Cited by 5 publications

(4 citation statements)

References 54 publications

(88 reference statements)

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“…Moreover, spectral enhancement sometimes have mixed results. In some applications, we do not see improvement for some reasons [66]. This implies that more research is needed to determine that under what conditions the EMAP-based methods can provide improvement and under what conditions not.…”

Section: Image Enhancementmentioning

confidence: 96%

“…It can be performed either using absolute radiometric normalization (ARN) or relative radiometric normalization (RRN) [62][63][64]. ARN requires prior knowledge of physical information related to the scene (e.g., weather conditions) for normalization [63,[65][66][67], while, RRN radiometrically normalizes the images based on a reference image using methods such as dark object subtraction (DOS), histogram matching (HM), simple regression (SR), pseudo-invariant features (PIF), iteratively re-weighted MAD transformation, etc. [62,64,68].…”

Section: Radiometric Correctionmentioning

confidence: 99%

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Recent Advances in Image Restoration with Applications to Real World Problems

Kwan¹

2020

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Chiman Kwan received his BSc in electronics from the Chinese University of Hong Kong in 1988, and his MS and Ph.D. degrees in electrical engineering from the University of Texas at Arlington in 1989 and 1993, respectively. Currently, he is the Chief Technology Officer of Signal Processing, Inc. and Applied Research LLC, working on projects related to control, signal processing, image processing, fault diagnostics and prognostics, and remote sensing. The past 30 years of research results with numerous collaborators have been published in the form of 1 book, 4 book chapters, 15 patents, 70+ invention disclosures, 360+ journal and conference papers, over 500+ technical reports, and 120 competitively selected projects from government and private companies totaling more than 36 million dollars.

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Section: Image Enhancementmentioning

confidence: 96%

Section: Radiometric Correctionmentioning

confidence: 99%

Recent Advances in Image Restoration with Applications to Real World Problems

Kwan¹

2020

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“…Research on the 3D information extraction of urban buildings can serve the research fields of urban climate [2][3][4][5], urban expansion [6,7], pollutant dispersion [8], urban 3D reconstruction [9][10][11][12], urban scene classification [13], energy consumption [14], and population assessment [15][16][17]. Therefore, large-scale and high-precision 3D information extraction of urban buildings is essential for a comprehensive understanding of urban development.…”

Section: Introductionmentioning

confidence: 99%

Developing a Method to Extract Building 3D Information from GF-7 Data

Wang

Meng

et al. 2021

Remote Sensing

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The three-dimensional (3D) information of buildings can describe the horizontal and vertical development of a city. The GaoFen-7 (GF-7) stereo-mapping satellite can provide multi-view and multi-spectral satellite images, which can clearly describe the fine spatial details within urban areas, while the feasibility of extracting building 3D information from GF-7 image remains understudied. This article establishes an automated method for extracting building footprints and height information from GF-7 satellite imagery. First, we propose a multi-stage attention U-Net (MSAU-Net) architecture for building footprint extraction from multi-spectral images. Then, we generate the point cloud from the multi-view image and construct normalized digital surface model (nDSM) to represent the height of off-terrain objects. Finally, the building height is extracted from the nDSM and combined with the results of building footprints to obtain building 3D information. We select Beijing as the study area to test the proposed method, and in order to verify the building extraction ability of MSAU-Net, we choose GF-7 self-annotated building dataset and a public dataset (WuHan University (WHU) Building Dataset) for model testing, while the accuracy is evaluated in detail through comparison with other models. The results are summarized as follows: (1) In terms of building footprint extraction, our method can achieve intersection-over-union indicators of 89.31% and 80.27% for the WHU Dataset and GF-7 self-annotated datasets, respectively; these values are higher than the results of other models. (2) The root mean square between the extracted building height and the reference building height is 5.41 m, and the mean absolute error is 3.39 m. In summary, our method could be useful for accurate and automatic 3D building information extraction from GF-7 satellite images, and have good application potential.

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“…This led to the development of classoriented fusion algorithms, as an example, (Albanwan and Qin, 2020) developed adaptive spatiotemporal fusion to impose different bandwidths based on the class of objects. On the other hand, other methods used the concept of k-median clustering to locate the cluster with the most consistent elevation points to reduce the number of outliers (Facciolo et al, 2017), where others used a pair ranking scheme based on a scoring technique to evaluate and sort stereo pairs by their quality and merge only the pairs with the best scores (Facciolo et al, 2017;Qin, 2019;Qin et al, 2020). There are many factors influencing the fusion outcomes including the type of input and the fusion algorithm.…”

Section: Introductionmentioning

confidence: 99%

Adaptive and Non-Adaptive Fusion Algorithms Analysis for Digital Surface Model Generated Using Census and Convolutional Neural Networks

Albanwan

Qin

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The digital surface models (DSM) fusion algorithms are one of the ongoing challenging problems to enhance the quality of 3D models, especially for complex regions with variable radiometric and geometric distortions like satellite datasets. DSM generation using Multiview stereo analysis (MVS) is the most common cost-efficient approach to recover elevations. Algorithms like Census-semi global matching (SGM) and Convolutional Neural Networks (MC-CNN) have been successfully implemented to generate the disparity and recover DSMs; however, their performances are limited when matching stereo pair images with ill-posed regions, low texture, dense texture, occluded, or noisy, which can yield missing or incorrect elevation values, in additions to fuzzy boundaries. DSM fusion algorithms have proven to tackle such problems, but their performance may vary based on the quality of the input and the type of fusion which can be classified into adaptive and non-adaptive. In this paper, we evaluate the performance of the adaptive and nonadaptive fusion methods using median filter, adaptive median filter, K-median clustering fusion, weighted average fusion, and adaptive spatiotemporal fusion for DSM generated using Census and MC-CNN. We perform our evaluation on 9 testing regions using stereo pair images from Worldview-3 satellite to generate DSMs using Census and MC-CNN. Our results show that adaptive fusion algorithms are more accurate than non-adaptive algorithms in predicting elevations due to their ability to learn from temporal and contextual information. Our results also show that MC-CNN produces better fusion results with a lower overall average RMSE than Census.

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3D Reconstruction through Fusion of Cross-View Images

Cited by 5 publications

References 54 publications

Recent Advances in Image Restoration with Applications to Real World Problems

Recent Advances in Image Restoration with Applications to Real World Problems

Developing a Method to Extract Building 3D Information from GF-7 Data

Adaptive and Non-Adaptive Fusion Algorithms Analysis for Digital Surface Model Generated Using Census and Convolutional Neural Networks

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