scholarly journals Bidirectional Symmetry Network with Dual-Field Cyclic Attention for Multi-Temporal Aerial Remote Sensing Image Registration

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1863
Author(s):  
Ying Chen ◽  
Qi Zhang ◽  
Wencheng Zhang ◽  
Lei Chen
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Image Registration ◽  
Network Architecture ◽  
Feature Matching ◽  
Pearson Correlation ◽  
Remote Sensing Image ◽  
Aerial Image ◽  
Source Image ◽  
Multi Temporal

Multi-temporal remote sensing image registration is a geometric symmetry process that involves matching a source image with a target image. To improve the accuracy and enhance the robustness of the algorithm, this study proposes an end-to-end registration network—a bidirectional symmetry network based on dual-field cyclic attention for multi-temporal remote sensing image registration, which mainly improves feature extraction and feature matching. (1) We propose a feature extraction framework combining an attention module and a pre-training model, which can accurately locate important areas in images and quickly extract features. Not only is the dual receptive field module designed to enhance attention in the spatial region, a loop structure is also used to improve the network model and improve overall accuracy. (2) Matching has not only directivity but also symmetry. We design a symmetric network of two-way matching to reduce the registration deviation caused by one-way matching and use a Pearson correlation method to improve the cross-correlation matching and enhance the robustness of the matching relation. In contrast with two traditional methods and three deep learning-based algorithms, the proposed approach works well under five indicators in three public multi-temporal datasets. Notably, in the case of the Aerial Image Dataset, the accuracy of the proposed method is improved by 39.8% compared with the Two-stream Ensemble method under a PCK (Percentage of Correct Keypoints) index of 0.05. When the PCK index is 0.03, accuracy increases by 46.8%, and increases by 18.7% under a PCK index of 0.01. Additionally, when adding the innovation points in feature extraction into the basic network CNNGeo (Convolutional Neural Network Architecture for Geometric Matching), accuracy is increased by 36.7% under 0.05 PCK, 18.2% under 0.03 PCK, and 8.4% under 0.01 PCK. Meanwhile, by adding the innovation points in feature matching into CNNGeo, accuracy is improved by 16.4% under 0.05 PCK, 9.1% under 0.03 PCK, and 5.2% under 0.01 PCK. In most cases, this paper reports high registration accuracy and efficiency for multi-temporal remote sensing image registration.

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.

scholarly journals Multimodal Remote Sensing Image Registration Methods and Advancements: A Survey

2021 ◽  
Vol 13 (24) ◽  
pp. 5128
Author(s):  
Xinyue Zhang ◽  
Chengcai Leng ◽  
Yameng Hong ◽  
Zhao Pei ◽  
Irene Cheng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Remote Sensing Image ◽  
Comprehensive Analysis ◽  
Remote Sensing Images ◽  
Research Focus ◽  
Theoretical Frameworks ◽  
Nonlinear Radiation ◽  
Multi Temporal ◽  
Feature Based

With rapid advancements in remote sensing image registration algorithms, comprehensive imaging applications are no longer limited to single-modal remote sensing images. Instead, multi-modal remote sensing (MMRS) image registration has become a research focus in recent years. However, considering multi-source, multi-temporal, and multi-spectrum input introduces significant nonlinear radiation differences in MMRS images for which researchers need to develop novel solutions. At present, comprehensive reviews and analyses of MMRS image registration methods are inadequate in related fields. Thus, this paper introduces three theoretical frameworks: namely, area-based, feature-based and deep learning-based methods. We present a brief review of traditional methods and focus on more advanced methods for MMRS image registration proposed in recent years. Our review or comprehensive analysis is intended to provide researchers in related fields with advanced understanding to achieve further breakthroughs and innovations.

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