Updates of ‘Aw3d30’ Alos Global Digital Surface Model in Antarctica With Other Open Access Datasets

Takaku, Junichi; Tadono, Takeo; Doutsu, Masanori; Ohgushi, Fumi; Huang, Kai

doi:10.5194/isprs-archives-xliii-b4-2021-401-2021

Cited by 14 publications

(4 citation statements)

References 16 publications

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“…We choose AW3D30 DSM and DSM generated by ENVI as comparison data. Among them, AW3D30 (ALOS World 3D-30 m) is a DSM data set released by the Japan Aerospace Exploration Agency(JAXA), with a grid resolution of ~30 m. This DSM data set is obtained by using PRISM on the land observation satellite ALOS to image the same object using three cameras with different perspectives [34]. In addition, the other DSM for comparison is obtained through the photogrammetry extension module in ENVI-v5.6 [35].…”

Section: Discussionmentioning

confidence: 99%

A General Framework of Remote Sensing Epipolar Image Generation

Wang

Xiang

et al. 2021

Remote Sensing

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Epipolar images can improve the efficiency and accuracy of dense matching by restricting the search range of correspondences from 2-D to 1-D, which play an important role in 3-D reconstruction. As most of the satellite images in archives are incidental collections, which do not have rigorous stereo properties, in this paper, we propose a general framework to generate epipolar images for both in-track and cross-track stereo images. We first investigate the theoretical epipolar constraints of single-sensor and multi-sensor images and then introduce the proposed framework in detail. Considering large elevation changes in mountain areas, the publicly available digital elevation model (DEM) is applied to reduce the initial offsets of two stereo images. The left image is projected into the image coordinate system of the right image using the rational polynomial coefficients (RPCs). By dividing the raw images into several blocks, the epipolar images of each block are parallel generated through a robust feature matching method and fundamental matrix estimation, in which way, the horizontal disparity can be drastically reduced while maintaining negligible vertical disparity for epipolar blocks. Then, stereo matching using the epipolar blocks can be easily implemented and the forward intersection method is used to generate the digital surface model (DSM). Experimental results on several in-track and cross-track images, including optical-optical, SAR-SAR, and SAR-optical pairs, demonstrate the effectiveness of the proposed framework, which not only has obvious advantages in mountain areas with large elevation changes but also can generate high-quality epipolar images for flat areas. The generated epipolar images of a ZiYuan-3 pair in Songshan are further utilized to produce a high-precision DSM.

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Section: Discussionmentioning

confidence: 99%

A General Framework of Remote Sensing Epipolar Image Generation

Wang

Xiang

et al. 2021

Remote Sensing

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“…Thus, the selection of DEMs for non-glacier regions can be primarily based on quality, allowing for the fusion of the highest quality DEMs without undue concern for temporal variations. Therefore, several DEMs (including ASTER GDEM, AW3D30, COPDEM, TAN30, NASADEM, SRTM, and Multi-Error-Removed Improved-Terrain DEM (MERIT DEM) 11 – 13 , 16 , 89 , 90 were selected for pre-validation for further consideration.…”

Section: Methodsmentioning

confidence: 99%

“…For example, the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM) 9 have been employed to assess the recent elevation increase at the QTP northeastern border 10 . The Advanced Land Observing Satellite (ALOS) World 3D - 30 m (AW3D30) 11 , Shuttle Radar Topography Mission (SRTM) 12 , NASADEM 13 , 14 , and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) 15 , 16 have contributed to estimating QTP ice thickness 17 . The AW3D30 and SRTM have enabled the delineation of the QTP’s endorheic basins through automated method 18 .…”

Section: Background and Summarymentioning

confidence: 99%

Error-Reduced Digital Elevation Model of the Qinghai-Tibet Plateau using ICESat-2 and Fusion Model

Zhang,

Guo,

Yuan

et al. 2024

Sci Data

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The Qinghai-Tibet Plateau (QTP) holds significance for investigating Earth’s surface processes. However, due to rugged terrain, forest canopy, and snow accumulation, open-access Digital Elevation Models (DEMs) exhibit considerable noise, resulting in low accuracy and pronounced data inconsistency. Furthermore, the glacier regions within the QTP undergo substantial changes, necessitating updates. This study employs a fusion of open-access DEMs and high-accuracy photons from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2). Additionally, snow cover and canopy heights are considered, and an ensemble learning fusion model is presented to harness the complementary information in the multi-sensor elevation observations. This innovative approach results in the creation of HQTP30, the most accurate representation of the 2021 QTP terrain. Comparative analysis with high-resolution imagery, UAV-derived DEMs, control points, and ICESat-2 highlights the advantages of HQTP30. Notably, in non-glacier regions, HQTP30 achieved a Mean Absolute Error (MAE) of 0.71 m, while in glacier regions, it reduced the MAE by 4.35 m compared to the state-of-the-art Copernicus DEM (COPDEM), demonstrating its versatile applicability.

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“…The terrain height distribution is a combination of a fixed value of 0 km for the ocean and a uniform distribution for land. The ratios of ocean and land are 43 % and 57 %, respectively, as derived from data from the Advanced Land Observing Satellite (ALOS) project (AW3D30 v4.0) (e.g., Takaku et al, 2021). Distribution A is illustrated in Fig.…”

Section: Input Variablementioning

confidence: 99%

Air mass factor calculation using deep neural network technique for tropospheric NO₂ retrieval from space

Xu,

Sato,

Nakamura

et al. 2024

Preprint

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Abstract. We performed a feasibility study on using deep neural network (DNN) techniques to calculate the air mass factor (AMF) for the satellite remote sensing of tropospheric nitrogen dioxide NO2. Satellite remote sensing in the UV and visible wavelength ranges is widely used to study the emission and distribution of tropospheric NO2, which is one of the most crucial gases in both climate change and air pollution. One of the largest sources of uncertainty in NO2 satellite retrievals is the AMF, especially when enhanced trace gas concentrations are present in the lower troposphere. Computing the AMF is a very time-consuming task that is usually performed using radiative transfer models. In general, the practical use of such models for satellite remote sensing is limited by the available computational power. We constructed two DNN models to calculate the tropospheric AMF (Trop-AMF-Net) and the altitude-dependent box-AMF (Box-AMF-Net). Trop-AMF-Net consists of five multilayer perceptrons and a linear transform. Box-AMF-Net is an encoder-decoder framework that combines Trop-AMF-Net with a long short-term memory network. We prepared two test datasets, one of which reflects the actual observed NO2 measurement pattern and the other of which assumes a uniform distribution. For the former, we assumed that the NO2 observation was performed by the Global Observing SATellite for Greenhouse Gases and Water Cycle (GOSAT-GW), which is planned for launch in 2024. For this test dataset, Trop-AMF-Net could reproduce the tropospheric AMFs with root-mean-squared percentage errors (RMSPE) of 0.121 % and 0.156 % when the model was trained using the same and uniform distributions, respectively. The RMSPEs of the box-AMFs in the troposphere predicted by Box-AMF-Net are 0.284 % for the test and training datasets when the observed pattern was used. The computation time for 10,000 samples using a combination of a central processing unit and graphics processing unit is 3.7 seconds. The RMSPE and computation time are approximately 30 times smaller and two times shorter compared to those of the commonly used look-up table and interpolation technique. Our feasibility study also highlights the importance of training the model with a dataset that is consistent with the test use.

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Updates of ‘Aw3d30’ Alos Global Digital Surface Model in Antarctica With Other Open Access Datasets

Cited by 14 publications

References 16 publications

A General Framework of Remote Sensing Epipolar Image Generation

A General Framework of Remote Sensing Epipolar Image Generation

Error-Reduced Digital Elevation Model of the Qinghai-Tibet Plateau using ICESat-2 and Fusion Model

Air mass factor calculation using deep neural network technique for tropospheric NO₂ retrieval from space

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Updates of ‘Aw3d30’ Alos Global Digital Surface Model in Antarctica With Other Open Access Datasets

Cited by 14 publications

References 16 publications

A General Framework of Remote Sensing Epipolar Image Generation

A General Framework of Remote Sensing Epipolar Image Generation

Error-Reduced Digital Elevation Model of the Qinghai-Tibet Plateau using ICESat-2 and Fusion Model

Air mass factor calculation using deep neural network technique for tropospheric NO2 retrieval from space

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Product

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Air mass factor calculation using deep neural network technique for tropospheric NO₂ retrieval from space