Robust Feature Matching for Remote Sensing Image Registration Based on $L_{q}$ -Estimator

2016 ◽  
Vol 13 (12) ◽  
pp. 1989-1993 ◽  
Author(s):  
Jiayuan Li ◽  
Qingwu Hu ◽  
Mingyao Ai
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Feature Matching ◽  
Remote Sensing Image

scholarly journals Modality-Free Feature Detector and Descriptor for Multimodal Remote Sensing Image Registration

2020 ◽  
Vol 12 (18) ◽  
pp. 2937
Author(s):  
Song Cui ◽  
Miaozhong Xu ◽  
Ailong Ma ◽  
Yanfei Zhong
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Near Infrared ◽  
Feature Matching ◽  
Simulated Data ◽  
Remote Sensing Image ◽  
Surface Model ◽  
Phase Congruency ◽  
Remote Sensing Images ◽  
Registration Method

The nonlinear radiation distortions (NRD) among multimodal remote sensing images bring enormous challenges to image registration. The traditional feature-based registration methods commonly use the image intensity or gradient information to detect and describe the features that are sensitive to NRD. However, the nonlinear mapping of the corresponding features of the multimodal images often results in failure of the feature matching, as well as the image registration. In this paper, a modality-free multimodal remote sensing image registration method (SRIFT) is proposed for the registration of multimodal remote sensing images, which is invariant to scale, radiation, and rotation. In SRIFT, the nonlinear diffusion scale (NDS) space is first established to construct a multi-scale space. A local orientation and scale phase congruency (LOSPC) algorithm are then used so that the features of the images with NRD are mapped to establish a one-to-one correspondence, to obtain sufficiently stable key points. In the feature description stage, a rotation-invariant coordinate (RIC) system is adopted to build a descriptor, without requiring estimation of the main direction. The experiments undertaken in this study included one set of simulated data experiments and nine groups of experiments with different types of real multimodal remote sensing images with rotation and scale differences (including synthetic aperture radar (SAR)/optical, digital surface model (DSM)/optical, light detection and ranging (LiDAR) intensity/optical, near-infrared (NIR)/optical, short-wave infrared (SWIR)/optical, classification/optical, and map/optical image pairs), to test the proposed algorithm from both quantitative and qualitative aspects. The experimental results showed that the proposed method has strong robustness to NRD, being invariant to scale, radiation, and rotation, and the achieved registration precision was better than that of the state-of-the-art methods.

scholarly journals Feedback Unilateral Grid-Based Clustering Feature Matching for Remote Sensing Image Registration

2019 ◽  
Vol 11 (12) ◽  
pp. 1418
Author(s):  
Zhaohui Zheng ◽  
Hong Zheng ◽  
Yong Ma ◽  
Fan Fan ◽  
Jianping Ju ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Image Matching ◽  
Feature Matching ◽  
Remote Sensing Image ◽  
Registration Accuracy ◽  
Computation Efficiency ◽  
Feature Based ◽  
Local Grid ◽  
Grid Based

In feature-based image matching, implementing a fast and ultra-robust feature matching technique is a challenging task. To solve the problems that the traditional feature matching algorithm suffers from, such as long running time and low registration accuracy, an algorithm called feedback unilateral grid-based clustering (FUGC) is presented which is able to improve computation efficiency, accuracy and robustness of feature-based image matching while applying it to remote sensing image registration. First, the image is divided by using unilateral grids and then fast coarse screening of the initial matching feature points through local grid clustering is performed to eliminate a great deal of mismatches in milliseconds. To ensure that true matches are not erroneously screened, a local linear transformation is designed to take feedback verification further, thereby performing fine screening between true matching points deleted erroneously and undeleted false positives in and around this area. This strategy can not only extract high-accuracy matching from coarse baseline matching with low accuracy, but also preserves the true matching points to the greatest extent. The experimental results demonstrate the strong robustness of the FUGC algorithm on various real-world remote sensing images. The FUGC algorithm outperforms current state-of-the-art methods and meets the real-time requirement.

Guided Locality Preserving Feature Matching for Remote Sensing Image Registration

2018 ◽  
Vol 56 (8) ◽  
pp. 4435-4447 ◽  
Author(s):  
Jiayi Ma ◽  
Junjun Jiang ◽  
Huabing Zhou ◽  
Ji Zhao ◽  
Xiaojie Guo
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Feature Matching ◽  
Remote Sensing Image ◽  
Locality Preserving

Remote Sensing Image Registration With Modified SIFT and Enhanced Feature Matching

2017 ◽  
Vol 14 (1) ◽  
pp. 3-7 ◽  
Author(s):  
Wenping Ma ◽  
Zelian Wen ◽  
Yue Wu ◽  
Licheng Jiao ◽  
Maoguo Gong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Feature Matching ◽  
Remote Sensing Image

scholarly journals An Instance Segmentation Based Framework for Large-Sized High-Resolution Remote Sensing Images Registration

2021 ◽  
Vol 13 (9) ◽  
pp. 1657
Author(s):  
Junyan Lu ◽  
Hongguang Jia ◽  
Tie Li ◽  
Zhuqiang Li ◽  
Jingyu Ma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Feature Matching ◽  
Local Area ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Fine Grained ◽  
Feature Based ◽  
Instance Segmentation

Feature-based remote sensing image registration methods have achieved great accomplishments. However, they have faced some limitations of applicability, automation, accuracy, efficiency, and robustness for large high-resolution remote sensing image registration. To address the above issues, we propose a novel instance segmentation based registration framework specifically for large-sized high-resolution remote sensing images. First, we design an instance segmentation model based on a convolutional neural network (CNN), which can efficiently extract fine-grained instances as the deep features for local area matching. Then, a feature-based method combined with the instance segmentation results is adopted to acquire more accurate local feature matching. Finally, multi-constraints based on the instance segmentation results are introduced to work on the outlier removal. In the experiments of high-resolution remote sensing image registration, the proposal effectively copes with the circumstance of the sensed image with poor positioning accuracy. In addition, the method achieves superior accuracy and competitive robustness compared with state-of-the-art feature-based methods, while being rather efficient.

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