Semi-Global Stereo Matching of Remote Sensing Images Combined with Speeded up Robust Features

Objects in satellite remote sensing image sequences often have large deformations, and the stereo matching of this kind of image is so difficult that the matching rate generally drops. A disparity refinement method is needed to correct and fill the disparity. A method for disparity refinement based on the results of plane segmentation is proposed in this paper. The plane segmentation algorithm includes two steps: Initial segmentation based on mean-shift and alpha-expansion-based energy minimization. According to the results of plane segmentation and fitting, the disparity is refined by filling missed matching regions and removing outliers. The experimental results showed that the proposed plane segmentation method could not only accurately fit the plane in the presence of noise but also approximate the surface by plane combination. After the proposed plane segmentation method was applied to the disparity refinement of remote sensing images, many missed matches were filled, and the elevation errors were reduced. This proved that the proposed algorithm was effective. For difficult evaluations resulting from significant variations in remote sensing images of different satellites, the edge matching rate and the edge matching map are proposed as new stereo matching evaluation and analysis tools. Experiment results showed that they were easy to use, intuitive, and effective.

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Research on Remote Sensing Image Matching with Special Texture Background

Symmetry ◽

10.3390/sym13081380 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1380

Author(s):

Sen Wang ◽

Xiaoming Sun ◽

Pengfei Liu ◽

Kaige Xu ◽

Weifeng Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Remote Sensing Image ◽

Geometric Distortion ◽

Reference Image ◽

Remote Sensing Images ◽

Scale Invariant ◽

Sift Algorithm ◽

Speeded Up Robust Features ◽

Adaptive Quantization ◽

Texture Background

The purpose of image registration is to find the symmetry between the reference image and the image to be registered. In order to improve the registration effect of unmanned aerial vehicle (UAV) remote sensing imagery with a special texture background, this paper proposes an improved scale-invariant feature transform (SIFT) algorithm by combining image color and exposure information based on adaptive quantization strategy (AQCE-SIFT). By using the color and exposure information of the image, this method can enhance the contrast between the textures of the image with a special texture background, which allows easier feature extraction. The algorithm descriptor was constructed through an adaptive quantization strategy, so that remote sensing images with large geometric distortion or affine changes have a higher correct matching rate during registration. The experimental results showed that the AQCE-SIFT algorithm proposed in this paper was more reasonable in the distribution of the extracted feature points compared with the traditional SIFT algorithm. In the case of 0 degree, 30 degree, and 60 degree image geometric distortion, when the remote sensing image had a texture scarcity region, the number of matching points increased by 21.3%, 45.5%, and 28.6%, respectively and the correct matching rate increased by 0%, 6.0%, and 52.4%, respectively. When the remote sensing image had a large number of similar repetitive regions of texture, the number of matching points increased by 30.4%, 30.9%, and −11.1%, respectively and the correct matching rate increased by 1.2%, 0.8%, and 20.8% respectively. When processing remote sensing images with special texture backgrounds, the AQCE-SIFT algorithm also has more advantages than the existing common algorithms such as color SIFT (CSIFT), gradient location and orientation histogram (GLOH), and speeded-up robust features (SURF) in searching for the symmetry of features between images.

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An Edge-Sense Bidirectional Pyramid Network for Stereo Matching of VHR Remote Sensing Images

Remote Sensing ◽

10.3390/rs12244025 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4025

Author(s):

Rongshu Tao ◽

Yuming Xiang ◽

Hongjian You

Keyword(s):

Remote Sensing ◽

Stereo Matching ◽

Tall Buildings ◽

Disparity Estimation ◽

Complex Structures ◽

Learning Networks ◽

Remote Sensing Images ◽

Essential Step ◽

Disparity Range ◽

The Cost

As an essential step in 3D reconstruction, stereo matching still faces unignorable problems due to the high resolution and complex structures of remote sensing images. Especially in occluded areas of tall buildings and textureless areas of waters and woods, precise disparity estimation has become a difficult but important task. In this paper, we develop a novel edge-sense bidirectional pyramid stereo matching network to solve the aforementioned problems. The cost volume is constructed from negative to positive disparities since the disparity range in remote sensing images varies greatly and traditional deep learning networks only work well for positive disparities. Then, the occlusion-aware maps based on the forward-backward consistency assumption are applied to reduce the influence of the occluded area. Moreover, we design an edge-sense smoothness loss to improve the performance of textureless areas while maintaining the main structure. The proposed network is compared with two baselines. The experimental results show that our proposed method outperforms two methods, DenseMapNet and PSMNet, in terms of averaged endpoint error (EPE) and the fraction of erroneous pixels (D1), and the improvements in occluded and textureless areas are significant.

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Stereo matching of remote sensing images using deep stereo matching

10.1117/12.2597702 ◽

2021 ◽

Author(s):

Mang Chen ◽

Johann Briffa ◽

Gianluca Valentino ◽

Reuben Farrugia

Keyword(s):

Remote Sensing ◽

Stereo Matching ◽

Remote Sensing Images

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Stereo Matching Algorithm for High-Resolution Remote Sensing Images Based on Sparse Coding and Dictionary Learning

10.1109/igarss47720.2021.9553750 ◽

2021 ◽

Author(s):

Dongyang Liu ◽

Junping Zhang ◽

Youliang Guo

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Dictionary Learning ◽

Sparse Coding ◽

Stereo Matching ◽

Remote Sensing Images ◽

Matching Algorithm

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Stereo Matching of VHR Remote Sensing Images via Bidirectional Pyramid Network

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9324093 ◽

2020 ◽

Author(s):

Rongshu Tao ◽

Yuming Xiang ◽

Hongjian You

Keyword(s):

Remote Sensing ◽

Stereo Matching ◽

Remote Sensing Images

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DispNet Based Stereo Matching for Planetary Scene Depth Estimation Using Remote Sensing Images

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) ◽

10.1109/prrs.2018.8486195 ◽

2018 ◽

Author(s):

Qingling Jia ◽

Xue Wan ◽

Baoqin Hei ◽

Shengyang Li

Keyword(s):

Remote Sensing ◽

Stereo Matching ◽

Depth Estimation ◽

Remote Sensing Images ◽

Scene Depth

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Automatic Sub-Pixel Co-Registration of Remote Sensing Images Using Phase Correlation and Harris Detector

Remote Sensing ◽

10.3390/rs13122314 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2314

Author(s):

Laila Rasmy ◽

Imane Sebari ◽

Mohamed Ettarid

Keyword(s):

Remote Sensing ◽

High Reliability ◽

Phase Correlation ◽

Gradient Algorithm ◽

Remote Sensing Images ◽

Step Size ◽

Registration Accuracy ◽

Speeded Up Robust Features ◽

Harris Corner ◽

Harris Detector

In this paper, we propose a new approach for sub-pixel co-registration based on Fourier phase correlation combined with the Harris detector. Due to the limitation of the standard phase correlation method to achieve only pixel-level accuracy, another approach is required to reach sub-pixel matching precision. We first applied the Harris corner detector to extract corners from both references and sensed images. Then, we identified their corresponding points using phase correlation between the image pairs. To achieve sub-pixel registration accuracy, two optimization algorithms were used. The effectiveness of the proposed method was tested with very high-resolution (VHR) remote sensing images, including Pleiades satellite images and aerial imagery. Compared with the speeded-up robust features (SURF)-based method, phase correlation with the Blackman window function produced 91% more matches with high reliability. Moreover, the results of the optimization analysis have revealed that Nelder–Mead algorithm performs better than the two-point step size gradient algorithm regarding localization accuracy and computation time. The proposed approach achieves better accuracy than 0.5 pixels and outperforms the speeded-up robust features (SURF)-based method. It can achieve sub-pixel accuracy in the presence of noise and produces large numbers of correct matching points.

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