Machine-learning based detection of corresponding interest points in optical and SAR images

Hänsch, Ronny; Hellwich, Olaf; Tu, Xiaohong

doi:10.1109/igarss.2016.7729381

Cited by 4 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides (Pseudo-)Siamese networks, many efforts have also been made in other network architectures. In [31], a Random Forest-based prediction framework is proposed to transform the matching problem into a classification task. It doubles the number of correspondences comparing to the Scale-Invariant Feature Transform (SIFT) [39] method.…”

Section: Related Workmentioning

confidence: 99%

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

Zhou

Zhao

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

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

Zhou

Zhao

et al. 2020

IEEE Access

View full text Add to dashboard Cite

“…Feature-based techniques first detect distinctive features, such as points, lines and regions, both in the reference and sensed images, then construct descriptors based on the local image neighborhood and attemp to find corresponding features (Hänsch et al, 2016). Among the feature-based methods, the SIFT-like algorithms are the most widely used techniques in SAR image registration due to the efficient performance and invariance to scale, rotation and illumination changes (Dellinger et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

Automatic Coregistration for Multiview Sar Images in Urban Areas

Xiang

Kang

Wang

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Due to the high resolution property and the side-looking mechanism of SAR sensors, complex buildings structures make the registration of SAR images in urban areas becomes very hard. In order to solve the problem, an automatic and robust coregistration approach for multiview high resolution SAR images is proposed in the paper, which consists of three main modules. First, both the reference image and the sensed image are segmented into two parts, urban areas and nonurban areas. Urban areas caused by double or multiple scattering in a SAR image have a tendency to show higher local mean and local variance values compared with general homogeneous regions due to the complex structural information. Based on this criterion, building areas are extracted. After obtaining the target regions, L-shape structures are detected using the SAR phase congruency model and Hough transform. The double bounce scatterings formed by wall and ground are shown as strong L- or T-shapes, which are usually taken as the most reliable indicator for building detection. According to the assumption that buildings are rectangular and flat models, planimetric buildings are delineated using the L-shapes, then the reconstructed target areas are obtained. For the orignal areas and the reconstructed target areas, the SAR-SIFT matching algorithm is implemented. Finally, correct corresponding points are extracted by the fast sample consensus (FSC) and the transformation model is also derived. The experimental results on a pair of multiview TerraSAR images with 1-m resolution show that the proposed approach gives a robust and precise registration performance, compared with the orignal SAR-SIFT method.

show abstract

“…Fig. 6 delineates patches encompassing individual intrigue focuses first in the first pictures and after that in the distorted ones 7 . We utilized 30 irregular relative distortions for each level of pivot to create 10800 pictures.…”

Section: Comparison With Randomized Treesmentioning

confidence: 99%

Object Recognition by Using Multi-level Feature Point Extraction

Cheng¹,

Dubois²

2017

Preprint

View full text Add to dashboard Cite

In this paper, we present a novel approach for object recognition in real-time by employing multilevel feature analysis and demonstrate the practicality of adapting feature extraction into a Naive Bayesian classification framework that enables simple, efficient, and robust performance. We also show the proposed method scales well as the number of level-classes grows. To effectively understand the patches surrounding a keypoint, the trained classifier uses hundreds of simple binary features and models class posterior probabilities. In addition, the classification process is computationally cheap under the assumed independence between arbitrary sets of features. Even though for some particular scenarios, this assumption can be invalid. We demonstrate that the efficient classifier nevertheless performs remarkably well on image datasets with a large variation in the illumination environment and image capture perspectives. The experiment results show consistent accuracy can be achieved on many challenging dataset while offer interactive speed for large resolution images. The method demonstrates promising results that outperform the state-of-the-art methods on pattern recognition.

show abstract

Machine-learning based detection of corresponding interest points in optical and SAR images

Cited by 4 publications

References 17 publications

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

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

Automatic Coregistration for Multiview Sar Images in Urban Areas

Object Recognition by Using Multi-level Feature Point Extraction

Contact Info

Product

Resources

About