Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images

By Amin Sedaghat, Mehdi Mokhtarzade, Hamid Ebadi. IEEE Transactions on Geoscience and Remote Sensing, 2011

302 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a88950630aeecf3043502c72969cd17cd46e0825

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Francesca Bovolo
2. Lorenzo Bruzzone
3. Xiaojie Guo
4. Maoguo Gong
5. Jie Shan
6. Xingyu Jiang
7. Guobao Xiao
8. Zhenfeng Shao

In Computer Vision and Pattern Recognition

1. Jiayi Ma
2. Zhenfeng Shao
3. Xiaojie Guo
4. Guobao Xiao
5. Xingyu Jiang
6. Francesca Bovolo
7. Maoguo Gong
8. L. Jiao

Citations in the corpus

• Remote Sensing Image Registration With Modified SIFT and Enhanced Feature Matching (2017, background): “[3] proposed the uniform R-SIFT (UR-SIFT) algorithm.”
• Pyramid of Spatial Relatons for Scene-Level Land Use Classification (2015, background): “They also began to gain popularity in the remote sensing community due to the robustness to rotation, scale changes, and occlusion [3], [19], [29], [32]–[34], [40]–[44].”
• A review of multimodal image matching: Methods and applications (2021, methodology): “35 [173] to uniformly detect local features and introduced an advanced self-similarity descriptor called distinctive order-based self-similarity (DOBSS descriptor to match the detected feature points.”
• SAR-SIFT: A SIFT-Like Algorithm for SAR Images (2015, methodology): “Several registration methods [3], [4], [5] use SIFT keypoints as Control Points (CP) to estimate deformation models.”
• Fast and Robust Matching for Multimodal Remote Sensing Image Registration (2019, background): “ar intensity changes, they are vulnerable to nonlinear intensity differences. To improve their matching performance, researchers proposed some improved local features such as the uniform robust SIFT (Sedaghat et al., 2011), the adaptive binning SIFT (Sedaghat and Ebadi, 2015), and the scale restrict”
• RIFT: Multi-Modal Image Matching Based on Radiation-Variation Insensitive Feature Transform (2019, methodology): “Uniform robust SIFT (UR-SIFT) [30] studies the distribution of SIFT feature points and proposes an entropy-based feature point selection strategy to improve the distribution uniformity.”