Automatic Registration of Optical and SAR Images Via Improved Phase Congruency Model

Xiang, Yuming; Tao, Rongshu; Wang, Feng; You, Hongjian; Han, Bing

doi:10.1109/jstars.2020.3026162

Cited by 54 publications

(12 citation statements)

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“…Second, slice the prematched images into 1536*1536 pixel patches with a 768-pixel stride. Finally, we use the registration algorithm from [12] to match the slice pairs exactly and keep only the center 768*768 pixels. For ease of use, we crop the patch into 256×256 pixels without overlap.…”

Section: Datasets a Gaofen Multiclass Datasetmentioning

confidence: 99%

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CFNet: A Cross Fusion Network for Joint Land Cover Classification Using Optical and SAR Images

Kang

Xiang

Wang

et al. 2022

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

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As two of the most widely-used remote sensing images, optical and synthetic aperture radar (SAR) images show abundant and complementary information on the same target owing to their individual imaging mechanisms. Consequently, using optical and SAR images simultaneously can better describe the inherent features of the target and thus be beneficial for subsequent remote sensing applications. In this paper, we propose a novel modular fully convolutional network model to improve the accuracy of land cover classification by fully exploiting the complementary features of the two sensors. We investigate where and how to fuse the two images in the joint classification network. A cross-gate module with bidirectional information flow is proposed to achieve the best fusion performance. In addition, to validate the proposed model, we construct a multiclass land cover classification dataset. Exhaustive experiments show that the proposed joint classification network presents superior results than state-of-the-art classification models using single-sensor images.

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Section: Datasets a Gaofen Multiclass Datasetmentioning

confidence: 99%

“…The heterogenous alignment of optical and SAR images is an important research area. Recent studies have included both traditional methods based on phase congruency [12] and alignment models based on deep learning [13]. As the matching problem is irrelevant to classification methods, the research usually focuses on the last two.…”

Section: Introductionmentioning

confidence: 99%

CFNet: A Cross Fusion Network for Joint Land Cover Classification Using Optical and SAR Images

Kang

Xiang

Wang

et al. 2022

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

Self Cite

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“…In [28], proposes a method based on SIFT to extract features from the SAR images by considering three important factors: stability, distinctiveness, and distribution. The authors in [29] propose using the improved Phase Congruency (PC) model to identify the best features and automatically register optical and SAR Images. In [30] the authors use deep learning, structural information, and multiscale strategies to refine the structural descriptors.…”

Section: Revisited Literaturementioning

confidence: 99%

Coregistration of Remote Sensing Image Based on Histogram Kernel Predictability

Hugo¹,

Aranda²,

Mejia-Zuluaga³

et al. 2022

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

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Registration of remote sensing images has been approached using different strategies; one of the most popular is based on similarity measures. There are different measures of similarity in the literature: Normalized Cross-Correlation (NCC), Mutual Information (MI), etc. Normalized Mutual Information (NMI) has received the most attention in image processing; among the most important limitations are its high computational cost and lack of robustness to strong radiometric changes. For this reason, in this work, we introduce a co-registration approach based on the Histogram Kernel Predictability (HKP). This formulation reduces numerical errors and requires less computing time in comparison to NMI. To the best of our knowledge, this is the first work for registering any remote sensing images by using HKP. Additionally, we propose to use an algorithm based on meta-heuristics called Evolutionary Centers Algorithm (ECA), which allows having fewer iterations to solve the registration problem. In addition, we incorporate a parallelization scheme that permits reducing processing times. The results show that our proposal can solve co-registration problems that the NMI cannot solve while obtaining competitive computational times and registration errors comparable with other existing works in the literature. The HKP approach solves most of all the transformations of a set of simulated registration problems, while the NMI, in some cases, only solves half of the registration problems. Moreover, we compare our approach with feature-based methods in real datasets. This research presents an alternative to remote sensing problems where MI has traditionally been used.

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“…VIS-SAR. Optical-SAR [37] provides aligned gray level and synthetic aperture radar image pairs in the uniform size of 512×512, which are remotely sensed by the satellite and cover field and town scenes. There are 2011 and 424 image pairs in the training set and test set, respectively.…”

Section: Vis-nirmentioning

confidence: 99%

ReDFeat: Recoupling Detection and Description for Multimodal Feature Learning

Deng,

2022

Preprint

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Deep-learning-based local feature extraction algorithms that combine detection and description have made significant progress in visible image matching. However, the end-toend training of such frameworks is notoriously unstable due to the lack of strong supervision of detection and the inappropriate coupling between detection and description. The problem is magnified in cross-modal scenarios, in which most methods heavily rely on the pre-training. In this paper, we recouple independent constraints of detection and description of multimodal feature learning with a mutual weighting strategy, in which the detected probabilities of robust features are forced to peak and repeat, while features with high detection scores are emphasized during optimization. Different from previous works, those weights are detached from back propagation so that the detected probability of indistinct features would not be directly suppressed and the training would be more stable. Moreover, we propose the Super Detector, a detector that possesses a large receptive field and is equipped with learnable non-maximum suppression layers, to fulfill the harsh terms of detection. Finally, we build a benchmark that contains cross visible, infrared, near-infrared and synthetic aperture radar image pairs for evaluating the performance of features in feature matching and image registration tasks. Extensive experiments demonstrate that features trained with the recoulped detection and description, named ReDFeat, surpass previous state-of-the-arts in the benchmark, while the model can be readily trained from scratch.

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Automatic Registration of Optical and SAR Images Via Improved Phase Congruency Model

Cited by 54 publications

References 50 publications

CFNet: A Cross Fusion Network for Joint Land Cover Classification Using Optical and SAR Images

CFNet: A Cross Fusion Network for Joint Land Cover Classification Using Optical and SAR Images

Coregistration of Remote Sensing Image Based on Histogram Kernel Predictability

ReDFeat: Recoupling Detection and Description for Multimodal Feature Learning

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