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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. [ABSTRACT FROM AUTHOR]

With the advancement of artificial intelligence technology and computer hardware, the stereo matching algorithm has been widely researched and applied in the field of image processing. In scenarios such as robot navigation and autonomous driving, stereo matching algorithms are used to assist robots in acquiring depth information about the surrounding environment, thereby improving the robot's ability for autonomous navigation during self-driving. In this paper, we address the issue of low matching accuracy of stereo matching algorithms in specular regions of images and propose a multi-attention-based stereo matching algorithm called MANet. The proposed algorithm embeds a multi-spectral attention module into the residual feature-extraction network of the PSMNet algorithm. It utilizes different 2D discrete cosine transforms to extract frequency-specific feature information, providing rich and effective features for cost computation in matching. The pyramid pooling module incorporates a coordinated attention mechanism, which not only maintains long-range dependencies with directional awareness but also captures more positional information during the pooling process, thereby enhancing the network's representational capacity. The MANet algorithm was evaluated on three major benchmark datasets, namely, SceneFlow, KITTI2015, and KITTI2012, and compared with relevant algorithms. Experimental results demonstrated that the MANet algorithm achieved higher accuracy in predicting disparities and exhibited stronger robustness against specular reflections, enabling more accurate disparity prediction in specular regions. [ABSTRACT FROM AUTHOR]

Due to the recent advances of sensing technologies, high dynamic range (HDR) imaging devices have been developed for the image acquisition of real-world scenes with a wide range of luminance changes. In this article, the hierarchical matching techniques for HDR image pairs are investigated. A bit slicing method is combined with the state-of-the-art stereo matching algorithms for HDR images generated from multiple exposures. Our approach demonstrates that it is possible to use only the most significant 16 bits of the HDR image encoding per pixel to achieve a comparable bad pixel rate on stereo matching. Thus, the computational power and memory constraints can be reduced while benefiting from the HDR images. This makes HDR stereo matching feasible under general resource limited setups. In the experiments, the evaluation and comparison carried out using Middlebury stereo datasets have illustrated the effectiveness of the proposed framework. The source codes are available at: https://github.com/chillwater/HDR_Stereo_Match. [ABSTRACT FROM AUTHOR]

Stereo matching algorithms generate disparity maps, which contain the depth information of the environment, from two or more images of a scene taken from different viewpoints. The process of obtaining dense disparity maps is a problem which is still being actively researched. The presence of radiometric differences in the images only further complicates the stereo matching problem. In the present research work, the images are initially split into small patches of pixels, such that pixels in each patch have similar intensities. The authors attempt to study the effect of the parameters, namely, tuning parameter 'a' and the number of segments, while the images are subjected to variations in exposure and illumination. The value 'a' performs the function of a weight signifying the contribution of each data cost, when the two data costs are combined in a linear fashion. Lastly, the results of this methodology are compared with other methods that try to tackle the problem of stereo matching under radiometric variations. [ABSTRACT FROM AUTHOR]

To make a matching algorithm to satisfy the requirements of high precision and anti-interference, a novel stereo-matching algorithm with efficient matching cost and adaptive guided image filter is proposed. Firstly, we adopt a modified Census transform with a local texture metric to compute the initial cost. It can make full use of the cross-correlation information between pixels. Meanwhile, we incorporate the Census, color and gradient costs as a mixed matching cost algorithm. Then, we aggregate the costs with guided image filter based on adaptive rectangular support window instead of the traditional fixed support window. The variable kernel window is constructed by the local color similarity and spatial distance. In this way, less occluded points will be included in the support region. On this basis, we adopt integral image to further speed up the computation of this step. Finally, the initial disparity of each pixel is selected using winner takes all optimization and the final disparity maps are gained after post-processing. The experimental results demonstrate that the proposed algorithm not only achieves an average error rate of 5.22 % on the Middlebury stereo benchmark data set, but can also overcome the influence of illumination distortion in the matching effectively. [ABSTRACT FROM AUTHOR]

As an essential task in remote sensing, disparity estimation of high-resolution stereo images is still confronted with intractable problems due to extremely complex scenes and dynamically changing disparities. Especially in areas containing texture-less regions, repetitive patterns, disparity discontinuities, and occlusions, stereo matching is difficult. Recently, convolutional neural networks have provided a new paradigm for disparity estimation, but it is difficult for current models to consider both accuracy and speed. This paper proposes a novel end-to-end network to overcome the aforementioned obstacles. The proposed network learns stereo matching at dual scales, in which the low one captures coarse-grained information while the high one captures fine-grained information, helpful for matching structures of different scales. Moreiver, we construct cost volumes from negative to positive values to make the network work well for both negative and nonnegative disparities since the disparity varies dramatically in remote sensing stereo images. A 3D encoder-decoder module formed by factorized 3D convolutions is introduced to adaptively learn cost aggregation, which is of high efficiency and able to alleviate the edge-fattening issue at disparity discontinuities and approximate the matching of occlusions. Besides, we use a refinement module that brings in shallow features as guidance to attain high-quality full-resolution disparity maps. The proposed network is compared with several typical models. Experimental results on a challenging dataset demonstrate that our network shows powerful learning and generalization abilities. It achieves convincing performance on both accuracy and efficiency, and improvements of stereo matching in these challenging areas are noteworthy. [ABSTRACT FROM AUTHOR]

We propose a new stereo matching framework based on image bit-plane slicing. A pair of image sequences with various intensity quantization levels constructed by taking different bit-rate of the images is used for hierarchical stereo matching. The basic idea is to use the low bit-rate image pairs to compute rough disparity maps. The hierarchical matching strategy is then carried out iteratively to update the low confident disparities with the information provided by extra image bit-planes. It is shown that, depending on the stereo matching algorithms, even the image pairs with low intensity quantization are able to produce fairly good disparity results. Consequently, variate bit-rate matching is performed only regionally in the images for each iteration, and the average image bit-rate for disparity computation is reduced. Our method provides a hierarchical matching framework and can be combined with the existing stereo matching algorithms. Experiments on Middlebury datasets show that the proposed technique gives good results compared to the conventional full bit-rate matching. [ABSTRACT FROM AUTHOR]

To obtain effective indoor moving target localization, a reliable and stable moving target localization method based on binocular stereo vision is proposed in this paper. A moving target recognition extraction algorithm, which integrates displacement pyramid Horn–Schunck (HS) optical flow, Delaunay triangulation and Otsu threshold segmentation, is presented to separate a moving target from a complex background, called the Otsu Delaunay HS (O-DHS) method. Additionally, a stereo matching algorithm based on deep matching and stereo vision is presented to obtain dense stereo matching points pairs, called stereo deep matching (S-DM). The stereo matching point pairs of the moving target were extracted with the moving target area and stereo deep matching point pairs, then the three dimensional coordinates of the points in the moving target area were reconstructed according to the principle of binocular vision's parallel structure. Finally, the moving target was located by the centroid method. The experimental results showed that this method can better resist image noise and repeated texture, can effectively detect and separate moving targets, and can match stereo image points in repeated textured areas more accurately and stability. This method can effectively improve the effectiveness, accuracy and robustness of three-dimensional moving target coordinates. [ABSTRACT FROM AUTHOR]

Image dense matching has become one of the widely used means for DSM generation due to its good performance in both accuracy and efficiency. However, for water areas, the most common ground object, accurate disparity estimation is always a challenge to excellent image dense matching methods, as represented by semi-global matching (SGM), due to the poor texture. For this reason, a great deal of manual editing is always inevitable before practical applications. The main reason for this is the lack of uniqueness of matching primitives, with fixed size and shape, used by those methods. In this paper, we propose a novel DSM generation method, namely semi-global and block matching (SGBM), to achieve accurate disparity and height estimation in water areas by adaptive block matching instead of pixel matching. First, the water blocks are extracted by seed point growth, and an adaptive block matching strategy considering geometrical deformations, called end-block matching (EBM), is adopted to achieve accurate disparity estimation. Then, the disparity of all other pixels beyond these water blocks is obtained by SGM. Last, the median value of height of all pixels within the same block is selected as the final height for this block after forward intersection. Experiments are conducted on ZiYuan-3 (ZY-3) stereo images, and the results show that DSM generated by our method in water areas has high accuracy and visual quality. [ABSTRACT FROM AUTHOR]