Matching Multi-Source Optical Satellite Imagery Exploiting a Multi-Stage Approach

Liu, Yuxuan; Mo, Fan; Tao, Pengjie

doi:10.3390/rs9121249

Cited by 18 publications

(14 citation statements)

References 29 publications

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“…They firstly estimated homographic transformation between source and target image by initial matching, and then used probability relaxation to expand matching. Though the matching scheme presented in [25] obtained satisfied matching results in satellite images, it has the same defect as in [18] that if the initial matching fails, the whole matching process will end up with a failure.…”

Section: Introductionmentioning

confidence: 98%

“…Sedaghat and Ebadi [12] combined the merits of LSS and the intensity order pooling scheme, and proposed DOBSS for matching multi-senor remote sensing images. Similar to Ye and San's matching strategy [18], Liu et al [25] proposed a multi-stage matching approach for multi-source optical satellite imagery matching. They firstly estimated homographic transformation between source and target image by initial matching, and then used probability relaxation to expand matching.…”

Section: Introductionmentioning

confidence: 99%

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Matching Multi-Sensor Remote Sensing Images via an Affinity Tensor

Chen

Yuan

et al. 2018

Remote Sensing

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Matching multi-sensor remote sensing images is still a challenging task due to textural changes and non-linear intensity differences. In this paper, a novel matching method is proposed for multi-sensor remote sensing images. To establish feature correspondences, an affinity tensor is used to integrate geometric and radiometric information. The matching process consists of three steps. First, features from an accelerated segment test are extracted from both source and target images, and two complete graphs are constructed with their nodes representing these features. Then, the geometric and radiometric similarities of the feature points are represented by the three-order affinity tensor, and the initial feature correspondences are established by tensor power iteration. Finally, a tensor-based mismatch detection process is conducted to purify the initial matched points. The robustness and capability of the proposed method are tested with a variety of remote sensing images such as Ziyuan-3 backward, Ziyuan-3 nadir, Gaofen-1, Gaofen-2, unmanned aerial vehicle platform, and Jilin-1. The experiments show that the average matching recall is greater than 0.5, which outperforms state-of-the-art multi-sensor image-matching algorithms such as SIFT, SURF, NG-SIFT, OR-SIFT and GOM-SIFT.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Matching Multi-Sensor Remote Sensing Images via an Affinity Tensor

Chen

Yuan

et al. 2018

Remote Sensing

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“…Feature matching is the basis of various remote sensing processing tasks, such as image retrieval [1,2], object recognition [3,4] and image registration [5][6][7][8]. eature matching can also contribute to calibrate attitude sensor performance [9,10].…”

Section: Introductionmentioning

confidence: 99%

Local Deep Descriptor for Remote Sensing Image Feature Matching

et al. 2019

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Feature matching via local descriptors is one of the most fundamental problems in many computer vision tasks, as well as in the remote sensing image processing community. For example, in terms of remote sensing image registration based on the feature, feature matching is a vital process to determine the quality of transform model. While in the process of feature matching, the quality of feature descriptor determines the matching result directly. At present, the most commonly used descriptor is hand-crafted by the designer’s expertise or intuition. However, it is hard to cover all the different cases, especially for remote sensing images with nonlinear grayscale deformation. Recently, deep learning shows explosive growth and improves the performance of tasks in various fields, especially in the computer vision community. Here, we created remote sensing image training patch samples, named Invar-Dataset in a novel and automatic way, then trained a deep learning convolutional neural network, named DescNet to generate a robust feature descriptor for feature matching. A special experiment was carried out to illustrate that our created training dataset was more helpful to train a network to generate a good feature descriptor. A qualitative experiment was then performed to show that feature descriptor vector learned by the DescNet could be used to register remote sensing images with large gray scale difference successfully. A quantitative experiment was then carried out to illustrate that the feature vector generated by the DescNet could acquire more matched points than those generated by hand-crafted feature Scale Invariant Feature Transform (SIFT) descriptor and other networks. On average, the matched points acquired by DescNet was almost twice those acquired by other methods. Finally, we analyzed the advantages of our created training dataset Invar-Dataset and DescNet and gave the possible development of training deep descriptor network.

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“…Image registration is a fundamental and challenging task that is used in many different research areas such as remote sensing, medical imaging, computer vision and video processing [1][2][3][4]. Especially in the remote sensing community, the technology is widely used in many applications, such as mapping of various applications, change detection, image fusion, mosaicking, and earth surface dynamics monitoring [5][6][7][8][9]. The accuracy of registration determines the quality of the remote sensing application and analysis and even the success or failure of these applications.…”

Section: Introductionmentioning

confidence: 99%

An Extension of Phase Correlation-Based Image Registration to Estimate Similarity Transform Using Multiple Polar Fourier Transform

et al. 2018

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Image registration is a core technology of many different image processing areas and is widely used in the remote sensing community. The accuracy of image registration largely determines the effect of subsequent applications. In recent years, phase correlation-based image registration has drawn much attention because of its high accuracy and efficiency as well as its robustness to gray difference and even slight changes in content. Many researchers have reported that the phase correlation method can acquire a sub-pixel accuracy of 1/10 or even 1/100. However, its performance is acquired only in the case of translation, which limits the scope of the application of the method. However, there are few reports on the estimation of scales and angles based on the phase correlation method. To take advantage of the high accuracy property and other merits of phase correlation-based image registration and extend it to estimate the similarity transform, we proposed a novel algorithm, the Multilayer Polar Fourier Transform (MPFT), which uses a fast and accurate polar Fourier transform with different scaling factors to calculate the log-polar Fourier transform. The structure of the polar grids of MPFT is more similar to the one of the log-polar grid. In particular, for rotation estimation only, the polar grid of MPFT is the calculation grid. To validate its effectiveness and high accuracy in estimating angles and scales, both qualitative and quantitative experiments were carried out. The quantitative experiments included a numerical simulation as well as synthetic and real data experiments. The experimental results showed that the proposed method, MPFT, performs better than the existing phase correlation-based similarity transform estimation methods, the Pseudo-polar Fourier Transform (PPFT) and the Multilayer Fractional Fourier Transform method (MLFFT), and the classical feature-based registration method, Scale-Invariant Feature Transform (SIFT), and its variant, ms-SIFT.

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Matching Multi-Source Optical Satellite Imagery Exploiting a Multi-Stage Approach

Cited by 18 publications

References 29 publications

Matching Multi-Sensor Remote Sensing Images via an Affinity Tensor

Matching Multi-Sensor Remote Sensing Images via an Affinity Tensor

Local Deep Descriptor for Remote Sensing Image Feature Matching

An Extension of Phase Correlation-Based Image Registration to Estimate Similarity Transform Using Multiple Polar Fourier Transform

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