New Point Matching Algorithm Using Sparse Representation of Image Patch Feature for SAR Image Registration

Fan, Jianwei; Wu, Yan; Wang, Fan; Zhang, Peng; Li, Ming

doi:10.1109/tgrs.2016.2626373

Cited by 33 publications

(13 citation statements)

References 28 publications

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“…Moreover, recall vs 1-precision [20,38,39] is used for performance comparisons. The recall vs 1-precision is defined as…”

Section: Evaluation Metricsmentioning

confidence: 99%

Exploring the Utilization of Gradient Information in SIFT Based Local Image Descriptors

Dong¹,

Han²,

Lv³

et al. 2019

Symmetry

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The utilization of gradient information is a key issue in building Scale Invariant Feature Transform (SIFT)-like descriptors. In the literature, two types of gradient information, i.e., Gradient Magnitude (GM) and Gradient Occurrence (GO), are used for building descriptors. However, both of these two types of gradient information have limitations in building and matching local image descriptors. In our prior work, a strategy of combining these two types of gradient information was proposed to intersect the keypoint matches which are obtained by using gradient magnitude and gradient occurrence individually. Different from this combination strategy, this paper explores novel strategies of weighting these two types of gradient information to build new descriptors with high discriminative power. These proposed weighting strategies are extensively evaluated against gradient magnitude and gradient occurrence as well as the combination strategy on a few image registration datasets. From the perspective of building new descriptors, experimental results will show that each of the proposed strategies achieve higher matching accuracy as compared to both GM-based and GO-based descriptors. In terms of recall results, one of the proposed strategies outperforms both GM-based and GO-based descriptors.

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“…Moreover, recall vs 1-precision [20,38,39] is used for performance comparisons. The recall vs 1-precision is defined as…”

Section: Evaluation Metricsmentioning

confidence: 99%

Exploring the Utilization of Gradient Information in SIFT Based Local Image Descriptors

Dong¹,

Han²,

Lv³

et al. 2019

Symmetry

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show abstract

“…In the first step (a), the slave and master images are Another relevant work can be found in [10], where Fan…”

Section: Introductionmentioning

confidence: 99%

Intersensor Remote Sensing Image Registration Using Multispectral Semantic Embeddings

Fernández-Beltrán

Pla

2019

IEEE Geosci. Remote Sensing Lett.

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This letter presents a novel inter-sensor registration framework specially designed to register Sentinel-3 (S3) operational data using the Sentinel-2 (S2) instrument as a reference. The substantially higher resolution of the Multi-Spectral Instrument (MSI), on-board S2, with respect to the Ocean and Land Color Instrument (OLCI), carried by S3, makes the former sensor a suitable spatial reference to finely adjust OLCI products. Nonetheless, the important spectral-spatial differences between both instruments may constrain traditional registration mechanisms to effectively align data of such different nature. In this context, the proposed registration scheme advocates the use of a topic model-based embedding approach to conduct the inter-sensor registration task within a common multi-spectral semantic space, where the input imagery is represented according to their corresponding spectral feature patterns instead of the low-level attributes. Thus, the OLCI products can be effectively registered to the MSI reference data by aligning those hidden patterns that fundamentally express the same visual concepts across the This work was supported by Generalitat Valenciana

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“…SAR ATR is widely used in various fields such as urban monitoring, natural environment survey, and military target reconnaissance [1,2], which can acquire earth observation images from severely adverse weather conditions and excavate hidden and camouflaged targets effectively [3,4]. Compared with other microwave detection tools, distinctive characteristics derived from SAR images can work well better than other sensors, like optical and infrared methods on coherent imaging system and electromagnetic scattering mechanism.…”

Section: Introductionmentioning

confidence: 99%

Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L²-Regularization

Zhai

Deng

et al. 2019

Computational Intelligence and Neuroscience

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Though Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) via Convolutional Neural Networks (CNNs) has made huge progress toward deep learning, some key issues still remain unsolved due to the lack of sufficient samples and robust model. In this paper, we proposed an efficient transferred Max-Slice CNN (MS-CNN) with L2-Regularization for SAR ATR, which could enrich the features and recognize the targets with superior performance. Firstly, the data amplification method is presented to reduce the computational time and enrich the raw features of SAR targets. Secondly, the proposed MS-CNN framework with L2-Regularization is trained to extract robust features, in which the L2-Regularization is incorporated to avoid the overfitting phenomenon and further optimizing our proposed model. Thirdly, transfer learning is introduced to enhance the feature representation and discrimination, which could boost the performance and robustness of the proposed model on small samples. Finally, various activation functions and dropout strategies are evaluated for further improving recognition performance. Extensive experiments demonstrated that our proposed method could not only outperform other state-of-the-art methods on the public and extended MSTAR dataset but also obtain good performance on the random small datasets.

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New Point Matching Algorithm Using Sparse Representation of Image Patch Feature for SAR Image Registration

Cited by 33 publications

References 28 publications

Exploring the Utilization of Gradient Information in SIFT Based Local Image Descriptors

Exploring the Utilization of Gradient Information in SIFT Based Local Image Descriptors

Intersensor Remote Sensing Image Registration Using Multispectral Semantic Embeddings

Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L²-Regularization

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New Point Matching Algorithm Using Sparse Representation of Image Patch Feature for SAR Image Registration

Cited by 33 publications

References 28 publications

Exploring the Utilization of Gradient Information in SIFT Based Local Image Descriptors

Exploring the Utilization of Gradient Information in SIFT Based Local Image Descriptors

Intersensor Remote Sensing Image Registration Using Multispectral Semantic Embeddings

Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L2-Regularization

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Product

Resources

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Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L²-Regularization