Feature Matching for Remote-Sensing Image Registration via Neighborhood Topological and Affine Consistency

Gong, Xi; Feng, Yong; Ma, Jiayi; Jiang, Junjun; Lü, Tao; Zhang, Yanduo; Zhou, Huabing

doi:10.3390/rs14112606

Cited by 12 publications

(4 citation statements)

References 50 publications

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“…For example, cross-modal feature description matching network (CM-Net) [30], multiscale framework with unsupervised learning (MU-Net) [31] and so on. [32] 2022 256×256 satellite-aerial image pairs literature [33] 2022 256×256 GF3 FSII literature [34] 2022 1240×1400 AHB dataset literature [35] 2022 600×600 Multimodal Remote Sensing literature [36] 2022 13056×11008 Google Earth literature [37] 2022 800×800 UAV, PAN, SAR, CIAP literature [31] 2022 512×512 Multimodal Remote Sensing literature [38] 2023 750×750 Multimodal Remote Sensing literature [39] 2023 27620×29200 Jilin-1, Gaofen-1, Gaofen-2 literature [40] 2023 855×831 Multimodal Remote Sensing…”

Section: A Image Matching Methodsmentioning

confidence: 99%

MSRSI-TPMF: A Tie Points Matching Framework of Multisource Remote Sensing Images

Cheng,

Li,

Wang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Remote sensing sensor platforms are typically located at a significant distance from the ground, ranging from several hundred meters to hundreds of kilometers. This means that, compared to natural images, remote sensing images (RSI) have larger coverage areas and more complex information. The larger size and data volume of RSI presents challenges for computer vision matching algorithms (MAs), making it difficult to apply them directly to RSI matching. Moreover, a matching framework for multi-source remote sensing images capable of large-scale processing by integrating multiple MAs with the entire RSI as input is presently lacking. This study proposes a Tie Points (TPs) Matching Framework of Multi-Source Remote Sensing Images (MSRSI-TPMF) based on the geometric and radiation characteristics of RSI. First, RSI is divided into different grids and undergo local geometry correction. Next, matching between slice images are performed by MAs. Finally, TPs are generated by mapping matched points in multiple slice images to the whole RSI using a geometric processing model. Six representative MAs including artificial feature matching algorithms and deep learning algorithms are integrated into the framework to match TPs from different RSI. Results demonstrate the extraction of TPs for multi-source RSI, validating the framework's efficacy. In addition, a large-scale TPs matching test for deep learning matching algorithm is performed by using 13 synthetic aperture radar (SAR) images (10m resolution) with TPs root mean square error (RMSE) of 0.368 pixels, further confirming the framework's reliability. Our framework is available at https://github.com/TobyChengV1/MSRSI-TPMF.

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Section: A Image Matching Methodsmentioning

confidence: 99%

MSRSI-TPMF: A Tie Points Matching Framework of Multisource Remote Sensing Images

Cheng,

Li,

Wang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Image feature matching plays a critical role in the field of computer vision as it aims to accurately establish correspondences between feature points across two or more images of the same scene [1]. Feature matching for remote sensing images holds significant value in various domains such as image fusion, change detection, environmental monitoring, map updating, image retrieval [2,3], and image mosaic [4]. Image feature matching technology refers to the process of identifying and comparing similar features between two or more images.…”

Section: Introductionmentioning

confidence: 99%

Feature matching of remote‐sensing images based on bilateral local–global structure consistency

Chen,

Feng

2023

IET Image Processing

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The goal of feature matching is to establish accurate correspondences between feature points in different images depicting the same scene. To address the polymorphism of local structures, the authors propose a mismatch removal method using bilateral local–global structural consistency. This method incorporates the problem of mismatch removal into the framework of graph matching, constructs a global affinity matrix using local structural similarity and global affine transformation consistency, and optimizes it using a constrained integer quadratic programming method. To comprehensively describe the local structure, the signature quadratic form distance (SQFD) is used to measure the consistency of the neighbourhood structure. Specifically, the weights of edges are constructed based on the SQFD of the local structure, while the matching correctness of nodes and edges between the two graphs is described using local vector similarity. Furthermore, the consistency of the global affine transformation is evaluated by assessing the consistency of the local neighbourhood affine transformation between different corresponding point pairs. In estimating the local affine transformation, a bilateral correction is performed using a total least‐squares (TLS) algorithm to measure the similarity of nodes between the two different graphs. Experimental results demonstrate that the proposed algorithm outperforms state‐of‐the‐art methods in terms of accuracy and effectiveness.

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“…D UE to the cost, physical limitation, and complexity constraints in remote sensing, obtained optic images with high spectral resolution usually maintain a more subordinate spatial resolution compared to images of lower spectral resolution [1], [2]. The high-spatial-spectral resolution image has great importance in various applications, such as environment monitoring, target tracking, and military investigation [3], [4], [5]. Therefore, in seeking to achieve a high-spatial-spectral resolution image using the available images, considerable strategies have been developed to tackle this problem by fusing the high spectral resolution image with a high spatial resolution image [6].…”

Section: Introductionmentioning

confidence: 99%

Model-Guided Deep Unfolded Fusion Network With Nonlocal Spatial-Spectral Priors for Hyperspectral Image Super-Resolution

Khader

Yang

Xiao

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Due to the physical boundaries, fusing low spatial resolution hyperspectral (LrHSI) with high spatial resolution multispectral (HrMSI) images is a hot and promising area for obtaining hyperspectral that have high spatial-spectral resolution images (HrHSI). Effectively formulating the fundamental features of hyperspectral images (HSI), such as global spectral correlation, nonlocal spatial correlation, as well spatial-spectral correlation, is complex in HSI-MSI fusion. Moreover, the fusion process is highly affected by the degradation systems, where these systems are not known in real scenarios. To this end, in this article, we proposed a model-guided deep unfolded fusion network with nonlocal spatial-spectral priors (MGDuNLSS-net) that can maintain the essential features of the HSIs and implicitly estimates the degradation process in an adequate running time. Specifically, the proposed method is designed based on subspace representation in an iterative manner and unrolling its steps toward a deep network as an end-to-end framework. This approach contains two submodules, fusion [nonlocal spatial-spectral block (NLSSB)] and imaging system submodules. The former submodule is proposed to exploit the images' intrinsic characteristics to improve the preservation of spectral and spatial details. NLSSB contains two nonlocal self-similarity (NLSS) layers embedded between two bidirectional simple recurrent unit (BSRU) layers. The recurrent calculation, as well as refined components to maintain the global spectral correlation, are the light recurrence operation and highway network, while 3-D convolutions in the BSRU can retain the spatial-spectral correlation. The NLSS layer can efficiently and effectively model long-range spatial contexts, which is designed based on criss-cross attention. The later submodule is used to refine the prediction of the degradation process at any iteration via backprojecting the estimated fused image to the observed pair, which can ensure the good performance of fusion. Compared with state-of-the-art fusion approaches, three remote sensing datasets are used to validate the proposed approach's performance.

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Feature Matching for Remote-Sensing Image Registration via Neighborhood Topological and Affine Consistency

Cited by 12 publications

References 50 publications

MSRSI-TPMF: A Tie Points Matching Framework of Multisource Remote Sensing Images

MSRSI-TPMF: A Tie Points Matching Framework of Multisource Remote Sensing Images

Feature matching of remote‐sensing images based on bilateral local–global structure consistency

Model-Guided Deep Unfolded Fusion Network With Nonlocal Spatial-Spectral Priors for Hyperspectral Image Super-Resolution

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