Enhanced disparity maps from multi-view satellite images

Suliman, Alaeldin; Zhang, Yun; Al-Tahir, Raid

doi:10.1109/igarss.2016.7729608

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…To align the rows of the images with the epipolar direction, the rotation angle of the epipolar direction was determined by connecting a distinct elevated point to its corresponding matching points and then determining the epipolar angle relative to the ground X-axis as described in [36]. Thus, for this dataset, the rotation to the epipolar direction was found to be 76.4231 degrees.…”

Section: Epipolarity and Inter-proportionality Resultsmentioning

confidence: 99%

Disparity-Based Generation of Line-of-Sight DSM for Image-Elevation Co-Registration to Support Building Detection in Off-Nadir VHR Satellite Images

Suliman¹,

Zhang²

2018

JGIS

Self Cite

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The integration of optical images and elevation data is of great importance for 3D-assisted mapping applications. Very high resolution (VHR) satellite images provide ideal geo-data for mapping building information. Since buildings are inherently elevated objects, these images need to be co-registered with their elevation data for reliable building detection results. However, accurate co-registration is extremely difficult for off-nadir VHR images acquired over dense urban areas. Therefore, this research proposes a Disparity-Based Elevation Co-Registration (DECR) method for generating a Line-of-Sight Digital Surface Model (LoS-DSM) to efficiently achieve image-elevation data co-registration with pixel-level accuracy. Relative to the traditional photogrammetric approach, the RMSE value of the derived elevations is found to be less than 2 pixels. The applicability of the DECR method is demonstrated through elevation-based building detection (EBD) in a challenging dense urban area. The quality of the detection result is found to be more than 90%. Additionally, the detected objects were geo-referenced successfully to their correct ground locations to allow direct integration with other maps. In comparison to the original LoS-DSM development algorithm, the DECR algorithm is more efficient by reducing the calculation steps, preserving the co-registration accuracy, and minimizing the need for elevation normalization in dense urban areas.

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Section: Epipolarity and Inter-proportionality Resultsmentioning

confidence: 99%

Disparity-Based Generation of Line-of-Sight DSM for Image-Elevation Co-Registration to Support Building Detection in Off-Nadir VHR Satellite Images

Suliman¹,

Zhang²

2018

JGIS

Self Cite

View full text Add to dashboard Cite

show abstract

“…For stereo image pairs, there are corresponding matching points in both images. After performing epipolar rectification on the image pair, which aligns matching points on the same horizontal line, these matching points have different horizontal coordinates in the image pair; this difference in coordinates is called "disparity" [3]. By using rectified stereo image pairs, disparity maps are generated by calculating the corresponding point in the right image for each pixel in the left image.…”

Section: Introductionmentioning

confidence: 99%

Stereo Matching Method for Remote Sensing Images Based on Attention and Scale Fusion

Wei,

Huang,

2024

Remote Sensing

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With the development of remote sensing satellite technology for Earth observation, remote sensing stereo images have been used for three-dimensional reconstruction in various fields, such as urban planning and construction. However, remote sensing images often contain noise, occluded regions, untextured areas, and repeated textures, which can lead to reduced accuracy in stereo matching and affect the quality of 3D reconstruction results. To reduce the impact of complex scenes in remote sensing images on stereo matching and to ensure both speed and accuracy, we propose a new end-to-end stereo matching network based on convolutional neural networks (CNNs). The proposed stereo matching network can learn features at different scales from the original images and construct cost volumes with varying scales to obtain richer scale information. Additionally, when constructing the cost volume, we introduce negative disparity to adapt to the common occurrence of both negative and non-negative disparities in remote sensing stereo image pairs. For cost aggregation, we employ a 3D convolution-based encoder–decoder structure that allows the network to adaptively aggregate information. Before feature aggregation, we also introduce an attention module to retain more valuable feature information, enhance feature representation, and obtain a higher-quality disparity map. By training on the publicly available US3D dataset, we obtain an accuracy of 1.115 pixels in end-point error (EPE) and 5.32% in the error pixel ratio (D1) on the test dataset, and the inference speed is 92 ms. Comparing our model with existing state-of-the-art models, we achieve higher accuracy, and the network is beneficial for the three-dimensional reconstruction of remote sensing images.

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“…For stereo image pairs, there are corresponding matching points in both images. After performing epipolar rectification on the image pair, which aligns matching points on the same horizontal line, these matching points have different horizontal coordinates in the image pair, and the difference in coordinates is called "disparity" [3]. By using rectified stereo image pairs, disparity maps are generated by calculating the corresponding point in the right image for each pixel in the left image.…”

Section: Introductionmentioning

confidence: 99%

Stereo Matching Method for Remote Sensing Images Based on Attention and Scale Fusion

Wei,

Huang,

2023

Preprint

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With the development of remote sensing satellite technology for Earth observation, remote sensing stereo images have been used for three-dimensional reconstruction in various fields, such as urban planning and construction. However, remote sensing images often include noise, occluded regions, weakly textured areas and repeated textures, which can lead to reduced accuracy in stereo matching and affect the quality of the 3D reconstruction results. To reduce the impact of complex scenes in remote sensing images on stereo matching and to ensure both speed and accuracy, we propose a new end-to-end stereo matching network based on convolutional neural networks (CNNs). The proposed stereo matching network can learn features at different scales from the original images and construct cost volumes with varying scales to obtain richer scale information. Additionally, when constructing the cost volume, we introduce negative disparity to adapt to the common occurrence of both negative and nonnegative disparities in remote sensing stereo image pairs. For cost aggregation, we employ a 3D convolution-based encoder-decoder structure that allows the network to adaptively aggregate information. Before feature aggregation, we also introduce an attention module to retain more valuable feature information, enhance feature representation, and obtain a higher-quality disparity map. By training on the publicly available US3D dataset, we obtain the accuracy that 1.115 pixel in end-point error (EPE) and 5.32% in the error pixel ratio (D1) on the test dataset, and the inference speed is 92 ms. Comparing our model with existing state-of-the-art models, we achieve higher accuracy, and the network is beneficial for the three-dimensional reconstruction of remote sensing images.

show abstract

Enhanced disparity maps from multi-view satellite images

Cited by 3 publications

References 4 publications

Disparity-Based Generation of Line-of-Sight DSM for Image-Elevation Co-Registration to Support Building Detection in Off-Nadir VHR Satellite Images

Disparity-Based Generation of Line-of-Sight DSM for Image-Elevation Co-Registration to Support Building Detection in Off-Nadir VHR Satellite Images

Stereo Matching Method for Remote Sensing Images Based on Attention and Scale Fusion

Stereo Matching Method for Remote Sensing Images Based on Attention and Scale Fusion

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