A Semi-Supervised Approach to Sar-Optical Image Matching

Hughes, Lloyd Haydn; Schmitt, Michael

doi:10.5194/isprs-annals-iv-2-w7-71-2019

Cited by 13 publications

(7 citation statements)

References 18 publications

(19 reference statements)

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“…He et al proposed a remote sensing image matching technique based on siamese convolutional neural network to learn feature and similarity metrics [31]. Hughes et al constructed SAR and optical image matching networks using semi-supervised learning, aiming to overcome dataset limitations [32]. Zhang et al constructed a general workflow for multimodal remote sensing image coregistration based on a siamese fully convolutional network and adopted the strategy of maximizing the feature distance between positive and negative samples.…”

Section: Et Al Proposed the Uniform Nonlinear Diffusion-basedmentioning

confidence: 99%

A Robust 3D Density Descriptor Based on Histogram of Oriented Primary Edge Structure for SAR and Optical Image Co-Registration

Ren³

et al. 2022

Remote Sensing

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The co-registration between SAR and optical images is a challenging task because of the speckle noise of SAR and the nonlinear radiation distortions (NRD), particularly in the one-look situation. In this paper, we propose a novel density descriptor based on the histogram of oriented primary edge structure (HOPES) for the co-registration of SAR and optical images, aiming to describe the shape structure of patches more firm. In order to extract the primary edge structure, we develop the novel multi-scale sigmoid Gabor (MSG) detector and a primary edge fusion algorithm. Based on the HOPES, we propose the co-registration method. To obtain stable and uniform keypoints, the non-maximum suppressed SAR-Harris (NMS-SAR-Harris) and deviding grids methods are used. NMS-SSD fast template matching and fast sample consensus (FSC) algorithm are used to further complete and optimize matching. We use two one-look simulated SAR images to demonstrate that the signal-to-noise ratio (SNR) of MSG is more than 10 dB higher than other state-of-the-stage detectors; the binary edge maps and F-score show that MSG has more accurate positioning performance. Compared with the other state-of-the-stage co-registration methods, the image co-registration results obtained on seven pairs of test images show that, the correct match rate (CMR) and the root mean squared error (RMSE) improve by more than 25% and 15% on average, respectively. It is experimentally demonstrated that the HOPES is robust against speckle noise and NRD, which can effectively improve the matching success rate and accuracy.

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Section: Et Al Proposed the Uniform Nonlinear Diffusion-basedmentioning

confidence: 99%

A Robust 3D Density Descriptor Based on Histogram of Oriented Primary Edge Structure for SAR and Optical Image Co-Registration

Ren³

et al. 2022

Remote Sensing

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“…From Table 1, it follows that the credibility of the positioning result of SAR image matching using SIFT algorithm (89.6%) was higher than that using the SURF algorithm (81.3%) because SIFT algorithm can get more successfully matched features and smaller pixel-offset. We also performed the image matching between a real-time SAR image and an optical image as a reference [24]. The optical image was downloaded from Google Maps.…”

Section: Credibility Evaluation For Sar Image Matchingmentioning

confidence: 99%

A SINS/SAR/GPS Fusion Positioning System Based on Sensor Credibility Evaluations

et al. 2021

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A reliable framework for SINS/SAR/GPS integrated positioning systems is proposed for the case that sensors are in critical environments. Credibility is used to describe the difference between the true error and the initial setting standard deviation. Credibility evaluation methods for inertial measurement unit (IMU), synthetic aperture radar (SAR), and global positioning system (GPS) are presented. In particular, IMU credibility is modeled by noises and constant drifts that are accumulated with time in a strapdown inertial navigation system (SINS). The quality of the SAR image decides the credibility of positioning based on SAR image matching. In addition, a cumulative residual chi-square test is used to evaluate GPS credibility. An extended Kalman filter based on a sensor credibility evaluation is introduced to integrate the measurements. The measurement of a sensor is either discarded when its credibility value is below a threshold or the variance matrix for the estimated state is otherwise adjusted. Simulations show that the final fusion positioning accuracy with credibility evaluation can be improved by 1–2 times compared to that without evaluation. In addition, the derived standard deviation correctly indicates the value of the position error with credibility evaluation. Moreover, the experiments on an unmanned ground vehicle partially verify the proposed evaluation method of GPS and the fusion framework in the actual environment.

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“…Recently, learning-based methods have shown promising results in matching SAR and optical images [28]- [33], [37], [38], where Siamese and Pseudo-Siamese networks are the most popular network architectures. In [28], a Siamese network is proposed for learning the shift between SAR and optical patches.…”

Section: Related Workmentioning

confidence: 99%

“…In [40], a deep metric based on a fully convolutional neural network (FCN) is proposed to predict whether SAR-optical image pairs are aligned or not. In [38], autoencoder-based matching techniques are extended to semi-supervised learning for SAR-optical image matching. To improve the geo-localization accuracy of optical images, [30] uses the HardNet [41] algorithm to classify matching and non-matching image pairs based on the Euclidean distance.…”

Section: Related Workmentioning

confidence: 99%

K-Means Clustering Guided Generative Adversarial Networks for SAR-Optical Image Matching

Zhou

Zhao

et al. 2020

IEEE Access

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A Semi-Supervised Approach to Sar-Optical Image Matching

Cited by 13 publications

References 18 publications

A Robust 3D Density Descriptor Based on Histogram of Oriented Primary Edge Structure for SAR and Optical Image Co-Registration

A Robust 3D Density Descriptor Based on Histogram of Oriented Primary Edge Structure for SAR and Optical Image Co-Registration

A SINS/SAR/GPS Fusion Positioning System Based on Sensor Credibility Evaluations

K-Means Clustering Guided Generative Adversarial Networks for SAR-Optical Image Matching

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