Area-Based Dense Image Matching with Subpixel Accuracy for Remote Sensing Applications: Practical Analysis and Comparative Study

Ye, Zhen; Xu, Yangsheng; Chen, Hao; Zhu, Jingwei; Tong, Xiaohua; Stilla, Uwe

doi:10.3390/rs12040696

Cited by 18 publications

(14 citation statements)

References 71 publications

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“…Because epipolar images have no y-disparities, dense stereo matching between a pair of epipolar images only necessitates the x-disparities. The proposed method adopted semiglobal matching (SGM) to compute the pixelwise x-disparities of each pair of epipolar images, and a pyramid constraint strategy was adopted to improve the quality of the dense image matching [37][38][39][40][41][42][43][44][45][46][47] results. The specific process is as follows: 1) Set one of the epipolar images as the main image and set another epipolar image as the secondary image, then divide the two images into fixed-size blocks.…”

Section: Multiview Dense Stereo Matchingmentioning

confidence: 99%

Research on Multiview Stereo Mapping Based on Satellite Video Images

Gao

Zhang

et al. 2021

IEEE Access

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In recent years, video satellite technology has greatly developed, and satellite video is captured using this technology. Because video satellites in motion are consistently aimed at certain areas, satellite video is usually composed of a series of frame images of the same area with different viewing angles; these satellite video images have different base-height ratios with each other, which makes multiview stereo mapping possible. This paper presents a method of multiview stereo mapping based on satellite video images to generate a digital surface model (DSM), digital elevation model (DEM), and digital ortho map (DOM) and analyzes the influence of different numbers and different viewing angles of satellite video images involved in multiview stereo mapping on the quality of the results. Satellite video images of Urumqi, China, captured by the "Jilin-1" video-03 satellite, were used as experimental data for the verification of the proposed method, and the results of the experiment show that: a high-quality DSM, DEM, and DOM can be generated by multiview stereo mapping based on two or more satellite video images, and the elevation precision and plane precision of the DEM results are 2.751 m and 2.419 m, respectively; such as in traditional stereo mapping, the larger the base-height ratio of the images involved in the calculation is, the higher the elevation precision of the DSM results will be within a certain extent. When the images participating in the calculation comprise images viewed from the nadir direction, forward direction and backward direction at the same time, DSM results with better building textures can be generated with high precision, and as the number of satellite video images increases, the building textures in the DSM results are optimized.

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Section: Multiview Dense Stereo Matchingmentioning

confidence: 99%

Research on Multiview Stereo Mapping Based on Satellite Video Images

Gao

Zhang

et al. 2021

IEEE Access

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“…With the rapid development of CNN, more and more feature extraction networks have shown strong feature extraction ability. Many of them can be applied to existing computer vision tasks such as object detection [41], land-cover classification [42][43][44], and image matching [45][46][47]. For change detection tasks that may be regarded as pixel-level classification problems, a fully convolutional layer rather than a fully connected layer could achieve this [48].…”

Section: Feature Extractormentioning

confidence: 99%

MCCRNet: A Multi-Level Change Contextual Refinement Network for Remote Sensing Image Change Detection

Zhang

2021

IJGI

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Change detection based on bi-temporal remote sensing images has made significant progress in recent years, aiming to identify the changed and unchanged pixels between a registered pair of images. However, most learning-based change detection methods only utilize fused high-level features from the feature encoder and thus miss the detailed representations that low-level feature pairs contain. Here we propose a multi-level change contextual refinement network (MCCRNet) to strengthen the multi-level change representations of feature pairs. To effectively capture the dependencies of feature pairs while avoiding fusing them, our atrous spatial pyramid cross attention (ASPCA) module introduces a crossed spatial attention module and a crossed channel attention module to emphasize the position importance and channel importance of each feature while simultaneously keeping the scale of input and output the same. This module can be plugged into any feature extraction layer of a Siamese change detection network. Furthermore, we propose a change contextual representations (CCR) module from the perspective of the relationship between the change pixels and the contextual representation, named change region contextual representations. The CCR module aims to correct changed pixels mistakenly predicted as unchanged by a class attention mechanism. Finally, we introduce an effective sample number adaptively weighted loss to solve the class-imbalanced problem of change detection datasets. On the whole, compared with other attention modules that only use fused features from the highest feature pairs, our method can capture the multi-level spatial, channel, and class context of change discrimination information. The experiments are performed with four public change detection datasets of various image resolutions. Compared to state-of-the-art methods, our MCCRNet achieved superior performance on all datasets (i.e., LEVIR, Season-Varying Change Detection Dataset, Google Data GZ, and DSIFN) with improvements of 0.47%, 0.11%, 2.62%, and 3.99%, respectively.

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“…Similar to the conventional scheme of Earth surface dynamics monitoring from optical images [4,37,38], the adopted workflow of glacier surface motion estimation using SAR intensity images includes three basic steps including pre-processing, dense matching, and post-processing, which are shown in Figure 1.…”

Section: Workflow Of Glacier Surface Motion Estimation Using Sar Intementioning

confidence: 99%

Glacier Surface Motion Estimation from SAR Intensity Images Based on Subpixel Gradient Correlation

et al. 2020

Sensors

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With the current extensive availability of synthetic-aperture radar (SAR) datasets with high temporal (e.g., a repeat cycle of a few or a dozen days) and spatial resolution (e.g., in the order of ∼1 m), radar remote sensing possesses an increasing potential for the monitoring of glacier surface motion thanks to the nearly weather and time-independent advantages. This paper proposes a robust subpixel frequency-based image correlation method for dense matching and integrates the improved matching into a workflow of glacier surface motion estimation using SAR intensity images with specific pre-processing and post-processing steps. The proposed matching method combines complex edge maps and local upsampling in the frequency domain for subpixel intensity tracking, which ensure the accuracy and robustness of glacier surface motion estimation. Experiments were carried out with TerraSAR-X and Sentinel-1 images covering two glacier areas in pole and alpine regions. The results of the monitoring and investigation of glacier motion validate the feasibility and reliability of the presented motion estimation method based on subpixel gradient correlation. The comparative results using both simulated and real SAR data indicate that the proposed matching method outperforms commonly used correlation-based matching methods in terms of matching accuracy and the ability to obtain correct matches.

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Area-Based Dense Image Matching with Subpixel Accuracy for Remote Sensing Applications: Practical Analysis and Comparative Study

Cited by 18 publications

References 71 publications

Research on Multiview Stereo Mapping Based on Satellite Video Images

Research on Multiview Stereo Mapping Based on Satellite Video Images

MCCRNet: A Multi-Level Change Contextual Refinement Network for Remote Sensing Image Change Detection

Glacier Surface Motion Estimation from SAR Intensity Images Based on Subpixel Gradient Correlation

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